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Before Width: | Height: | Size: 34 KiB After Width: | Height: | Size: 34 KiB |
@ -17,6 +17,7 @@
|
|||||||
#+LATEX_HEADER_EXTRA: \address[a3]{CSIR --- Structural Engineering Research Centre, Taramani, Chennai --- 600113, India.}
|
#+LATEX_HEADER_EXTRA: \address[a3]{CSIR --- Structural Engineering Research Centre, Taramani, Chennai --- 600113, India.}
|
||||||
#+LATEX_HEADER_EXTRA: \address[a4]{Universit\'{e} Libre de Bruxelles, Precision Mechatronics Laboratory, BEAMS Department, 1050 Brussels, Belgium.}
|
#+LATEX_HEADER_EXTRA: \address[a4]{Universit\'{e} Libre de Bruxelles, Precision Mechatronics Laboratory, BEAMS Department, 1050 Brussels, Belgium.}
|
||||||
|
|
||||||
|
#+LATEX_HEADER: \usepackage{subcaption}
|
||||||
#+LATEX_HEADER: \usepackage{amsfonts}
|
#+LATEX_HEADER: \usepackage{amsfonts}
|
||||||
#+LATEX_HEADER: \usepackage{siunitx}
|
#+LATEX_HEADER: \usepackage{siunitx}
|
||||||
#+LATEX_HEADER_EXTRA: \usepackage{tabularx}
|
#+LATEX_HEADER_EXTRA: \usepackage{tabularx}
|
||||||
@ -27,7 +28,7 @@
|
|||||||
#+LATEX_HEADER_EXTRA: \hypersetup{colorlinks=true}
|
#+LATEX_HEADER_EXTRA: \hypersetup{colorlinks=true}
|
||||||
:END:
|
:END:
|
||||||
|
|
||||||
* Build :noexport:
|
* Build :noexport:
|
||||||
#+NAME: startblock
|
#+NAME: startblock
|
||||||
#+BEGIN_SRC emacs-lisp :results none
|
#+BEGIN_SRC emacs-lisp :results none
|
||||||
(add-to-list 'org-latex-classes
|
(add-to-list 'org-latex-classes
|
||||||
@ -126,7 +127,7 @@ Sensor fusion \sep{} Optimal filters \sep{} $\mathcal{H}_\infty$ synthesis \sep{
|
|||||||
- cite:hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system: FIR + convex optimization
|
- cite:hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system: FIR + convex optimization
|
||||||
- Similar to feedback system:
|
- Similar to feedback system:
|
||||||
- cite:plummer06_optim_compl_filter_their_applic_motion_measur use H-Infinity to optimize complementary filters (flatten the super sensor noise spectral density)
|
- cite:plummer06_optim_compl_filter_their_applic_motion_measur use H-Infinity to optimize complementary filters (flatten the super sensor noise spectral density)
|
||||||
- cite:jensen13_basic_uas design of complementary filters with classical control theory
|
- cite:jensen13_basic_uas design of complementary filters with classical control theory, PID
|
||||||
|
|
||||||
- 3 complementary filters: cite:becker15_compl_filter_desig_three_frequen_bands
|
- 3 complementary filters: cite:becker15_compl_filter_desig_three_frequen_bands
|
||||||
|
|
||||||
@ -260,30 +261,69 @@ Complementary filters provides a framework for fusing signals from different sen
|
|||||||
As the effectiveness of the fusion depends on the proper design of the complementary filters, they are expected to fulfill certain requirements.
|
As the effectiveness of the fusion depends on the proper design of the complementary filters, they are expected to fulfill certain requirements.
|
||||||
These requirements are discussed in this section.
|
These requirements are discussed in this section.
|
||||||
|
|
||||||
** Sensor Models
|
|
||||||
<<sec:sensor_models>>
|
|
||||||
|
|
||||||
- Noise + dynamics
|
|
||||||
|
|
||||||
#+name: fig:sensor_model
|
|
||||||
#+caption: Basic Sensor Model
|
|
||||||
[[file:figs/sensor_model.pdf]]
|
|
||||||
|
|
||||||
- Suppose we calibrate the sensors
|
|
||||||
|
|
||||||
#+name: fig:sensor_model_calibrated
|
|
||||||
#+caption: Calibrated Sensor
|
|
||||||
[[file:figs/sensor_model_calibrated.pdf]]
|
|
||||||
|
|
||||||
** Sensor Fusion Architecture
|
** Sensor Fusion Architecture
|
||||||
<<sec:sensor_fusion>>
|
<<sec:sensor_fusion>>
|
||||||
|
|
||||||
Let's consider two sensors measuring the same physical quantity $x$ with dynamics $G_1(s)$ and $G_2(s)$, and with uncorrelated noise characteristics $n_1$ and $n_2$.
|
A general sensor fusion architecture is shown in Figure ref:fig:sensor_fusion_overview where several sensors (here two) are measuring the same physical quantity $x$.
|
||||||
|
The two sensors output signals are estimates $\hat{x}_1$ and $\hat{x}_2$ of $x$.
|
||||||
|
Each of these estimates are then filtered out by complementary filters and combined to form a new estimate $\hat{x}$.
|
||||||
|
We further call the overall system from $x$ to $\hat{x}$ the "super sensor".
|
||||||
|
|
||||||
The signals from both sensors are fed into two complementary filters $H_1(s)$ and $H_2(s)$ and then combined to yield an estimate $\hat{x}$ of $x$ as shown in Fig. ref:fig:fusion_super_sensor.
|
#+name: fig:sensor_fusion_overview
|
||||||
|
#+caption: Schematic of a sensor fusion architecture
|
||||||
|
[[file:figs/sensor_fusion_overview.pdf]]
|
||||||
|
|
||||||
|
The filters $H_1(s)$ and $H_2(s)$ are complementary which implies that:
|
||||||
|
#+name: eq:comp_filter
|
||||||
|
\begin{equation}
|
||||||
|
H_1(s) + H_2(s) = 1
|
||||||
|
\end{equation}
|
||||||
|
|
||||||
|
It will soon become clear why the complementary property is important.
|
||||||
|
|
||||||
|
** Sensor Models and Sensor Normalization
|
||||||
|
<<sec:sensor_models>>
|
||||||
|
|
||||||
|
In order to study such sensor fusion architecture, a model of the sensor is required.
|
||||||
|
|
||||||
|
The sensor model is shown in Figure ref:fig:sensor_model.
|
||||||
|
It consists of a Linear Time Invariant system (LTI) $G_i(s)$ representing the dynamics of the sensor and an additive noise input $n_i$ representing its noise.
|
||||||
|
The model input $x$ is the measured quantity and its output $\tilde{x}_i$ is the "raw" output of the sensor.
|
||||||
|
|
||||||
|
Before filtering the sensor outputs $\tilde{x}_i$ by the complementary filters, the sensors are usually normalized.
|
||||||
|
This normalization consists of obtaining an estimate $\hat{G}_i(s)$ of the sensor dynamics $G_i(s)$.
|
||||||
|
The raw output of the sensor $\tilde{x}_i$ is then passed through the inverse of the sensor dynamics estimate as shown in Figure ref:fig:sensor_model_calibrated.
|
||||||
|
This way, the units of the estimates $\hat{x}_i$ are equal to the units of the physical quantity $x$.
|
||||||
|
The sensor dynamics estimate $\hat{G}_1(s)$ can be a simple gain or more complex transfer functions.
|
||||||
|
|
||||||
|
#+begin_export latex
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{subfigure}[b]{0.49\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model.pdf}
|
||||||
|
\caption{\label{fig:sensor_model} Basic sensor model consisting of a noise input $n_i$ and a dynamics $G_i(s)$}
|
||||||
|
\end{subfigure}
|
||||||
|
\hfill
|
||||||
|
\begin{subfigure}[b]{0.49\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model_calibrated.pdf}
|
||||||
|
\caption{\label{fig:sensor_model_calibrated} Calibrated sensors using the inverse of an estimate $\hat{G}_1(s)$ of the sensor dynamics}
|
||||||
|
\end{subfigure}
|
||||||
|
\caption{\label{fig:figure_name}Sensor models with an without normalization}
|
||||||
|
\centering
|
||||||
|
\end{figure}
|
||||||
|
#+end_export
|
||||||
|
|
||||||
|
Let's now combine the two calibrated sensors models (Figure ref:fig:sensor_model_calibrated) with the sensor fusion architecture of figure ref:fig:sensor_fusion_overview.
|
||||||
|
The result is shown in Figure ref:fig:fusion_super_sensor.
|
||||||
|
|
||||||
|
The two sensors are measuring the same physical quantity $x$ with dynamics $G_1(s)$ and $G_2(s)$, and with /uncorrelated/ noises $n_1$ and $n_2$.
|
||||||
|
The signals from both calibrated sensors are fed into two complementary filters $H_1(s)$ and $H_2(s)$ and then combined to yield an estimate $\hat{x}$ of $x$ as shown in Fig. ref:fig:fusion_super_sensor.
|
||||||
|
|
||||||
|
The super sensor output is therefore equal to:
|
||||||
#+name: eq:comp_filter_estimate
|
#+name: eq:comp_filter_estimate
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\hat{x} = \left(G_1 H_1 + G_2 H_2\right) x + H_1 n_1 + H_2 n_2
|
\hat{x} = \Big( H_1(s) \hat{G}_1(s) G_1(s) + H_2(s) \hat{G}_2(s) G_2(s) \Big) x + H_1(s) \hat{G}_1(s) G_1(s) n_1 + H_2(s) \hat{G}_2(s) G_2(s) n_2
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
#+name: fig:fusion_super_sensor
|
#+name: fig:fusion_super_sensor
|
||||||
@ -291,66 +331,77 @@ The signals from both sensors are fed into two complementary filters $H_1(s)$ an
|
|||||||
#+attr_latex: :scale 1
|
#+attr_latex: :scale 1
|
||||||
[[file:figs/fusion_super_sensor.pdf]]
|
[[file:figs/fusion_super_sensor.pdf]]
|
||||||
|
|
||||||
The complementary property of $H_1(s)$ and $H_2(s)$ implies that their transfer function sum is equal to one at all frequencies eqref:eq:comp_filter.
|
|
||||||
#+name: eq:comp_filter
|
|
||||||
\begin{equation}
|
|
||||||
H_1(s) + H_2(s) = 1
|
|
||||||
\end{equation}
|
|
||||||
|
|
||||||
** Noise Sensor Filtering
|
** Noise Sensor Filtering
|
||||||
<<sec:noise_filtering>>
|
<<sec:noise_filtering>>
|
||||||
|
|
||||||
Let's first consider sensors with perfect dynamics
|
In this section, it is suppose that all the sensors are correctly calibrated, such that:
|
||||||
#+name: eq:perfect_dynamics
|
#+name: eq:perfect_dynamics
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
G_1(s) = G_2(s) = 1
|
\frac{\hat{x}_i}{x} = \hat{G}_i(s) G_i(s) \approx 1
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
The estimate $\hat{x}$ is then described by
|
The effect of a non-ideal normalization will be discussed in the next section.
|
||||||
|
|
||||||
|
The super sensor output $\hat{x}$ is then:
|
||||||
#+name: eq:estimate_perfect_dyn
|
#+name: eq:estimate_perfect_dyn
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\hat{x} = x + H_1 n_1 + H_2 n_2
|
\hat{x} = x + H_1(s) n_1 + H_2(s) n_2
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
From eqref:eq:estimate_perfect_dyn, the complementary filters $H_1(s)$ and $H_2(s)$ are shown to only operate on the sensor's noise.
|
|
||||||
|
From eqref:eq:estimate_perfect_dyn, the complementary filters $H_1(s)$ and $H_2(s)$ are shown to only operate on the sensor's noises.
|
||||||
Thus, this sensor fusion architecture permits to filter the noise of both sensors without introducing any distortion in the physical quantity to be measured.
|
Thus, this sensor fusion architecture permits to filter the noise of both sensors without introducing any distortion in the physical quantity to be measured.
|
||||||
|
|
||||||
Let's define the estimation error $\delta x$ by eqref:eq:estimate_error.
|
Let's define the estimation error $\delta x$ by eqref:eq:estimate_error.
|
||||||
#+name: eq:estimate_error
|
#+name: eq:estimate_error
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\delta x \triangleq \hat{x} - x = H_1 n_1 + H_2 n_2
|
\delta x \triangleq \hat{x} - x = H_1(s) n_1 + H_2(s) n_2
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
As shown in eqref:eq:noise_filtering_psd, the Power Spectral Density (PSD) of the estimation error $\Phi_{\delta x}$ depends both on the norm of the two complementary filters and on the PSD of the noise sources $\Phi_{n_1}$ and $\Phi_{n_2}$.
|
As shown in eqref:eq:noise_filtering_psd, the Power Spectral Density (PSD) of the estimation error $\Phi_{\delta x}$ depends both on the norm of the two complementary filters and on the PSD of the noise sources $\Phi_{n_1}$ and $\Phi_{n_2}$.
|
||||||
#+name: eq:noise_filtering_psd
|
#+name: eq:noise_filtering_psd
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\Phi_{\delta x} = \left|H_1\right|^2 \Phi_{n_1} + \left|H_2\right|^2 \Phi_{n_2}
|
\Phi_{\delta x}(\omega) = \left|H_1(j\omega)\right|^2 \Phi_{n_1}(\omega) + \left|H_2(j\omega)\right|^2 \Phi_{n_2}(\omega)
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
|
# TODO - Rework, tell that we can put requirements on the *norm* of the complementary filters
|
||||||
Usually, the two sensors have high noise levels over distinct frequency regions.
|
Usually, the two sensors have high noise levels over distinct frequency regions.
|
||||||
In order to lower the noise of the super sensor, the value of the norm $|H_1|$ has to be lowered when $\Phi_{n_1}$ is larger than $\Phi_{n_2}$ and that of $|H_2|$ lowered when $\Phi_{n_2}$ is larger than $\Phi_{n_1}$.
|
In order to lower the noise of the super sensor, the value of the norm $|H_1|$ has to be lowered when $\Phi_{n_1}$ is larger than $\Phi_{n_2}$ and that of $|H_2|$ lowered when $\Phi_{n_2}$ is larger than $\Phi_{n_1}$.
|
||||||
|
|
||||||
** Robustness of the Fusion
|
** Robustness of the Fusion
|
||||||
<<sec:fusion_robustness>>
|
<<sec:fusion_robustness>>
|
||||||
|
|
||||||
In practical systems the sensor dynamics is not perfect and eqref:eq:perfect_dynamics is not verified.
|
In practical systems the sensor normalization is not perfect and eqref:eq:perfect_dynamics is not verified.
|
||||||
In such case, one can use an inversion filter $\hat{G}_i^{-1}(s)$ to normalize the sensor dynamics, where $\hat{G}_i(s)$ is an estimate of the sensor dynamics $G_i(s)$.
|
|
||||||
However, as there is always some level of uncertainty on the dynamics, it cannot be perfectly inverted and $\hat{G}_i^{-1}(s) G_i(s) \neq 1$.
|
|
||||||
|
|
||||||
#+name: fig:sensor_model_uncertainty
|
In order to study such imperfection, the sensor dynamical uncertainty is modeled using multiplicative input uncertainty (Figure ref:fig:sensor_model_uncertainty), where the nominal model is taken as the estimated model for the normalization $\hat{G}_i(s)$, $\Delta_i$ is any stable transfer function satisfying $|\Delta_i(j\omega)| \le 1,\ \forall\omega$, and $w_i(s)$ is a weight representing the magnitude of the uncertainty.
|
||||||
#+caption: Input Uncertainty
|
|
||||||
[[file:figs/sensor_model_uncertainty.png]]
|
|
||||||
|
|
||||||
#+name: fig:sensor_model_uncertainty_simplified
|
# The weight $w_i(s)$ is chosen such that the real sensor dynamics is contained in the uncertain region represented by...
|
||||||
#+caption: Input Uncertainty
|
|
||||||
#+RESULTS:
|
|
||||||
[[file:figs/sensor_model_uncertainty_simplified.png]]
|
|
||||||
|
|
||||||
Let's represent the resulting dynamic uncertainty of the inverted sensors by an input multiplicative uncertainty as shown in Fig. ref:fig:sensor_fusion_dynamic_uncertainty where $\Delta_i$ is any stable transfer function satisfying $|\Delta_i(j\omega)| \le 1,\ \forall\omega$, and $|w_i(s)|$ is a weight representing the magnitude of the uncertainty.
|
As the nominal sensor dynamics is taken as the normalized filter, the normalized sensor can be further simplified as shown in Figure ref:fig:sensor_model_uncertainty_simplified.
|
||||||
|
|
||||||
|
#+begin_export latex
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{subfigure}[b]{0.59\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model_uncertainty.pdf}
|
||||||
|
\caption{\label{fig:sensor_model_uncertainty} Sensor with multiplicative input uncertainty}
|
||||||
|
\end{subfigure}
|
||||||
|
\hfill
|
||||||
|
\begin{subfigure}[b]{0.39\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model_uncertainty_simplified.pdf}
|
||||||
|
\caption{\label{fig:sensor_model_uncertainty_simplified} Simplified sensor model}
|
||||||
|
\end{subfigure}
|
||||||
|
\hfill
|
||||||
|
\caption{\label{fig:sensor_models_uncertainty}Sensor models with dynamical uncertainty}
|
||||||
|
\centering
|
||||||
|
\end{figure}
|
||||||
|
#+end_export
|
||||||
|
|
||||||
|
A sensor fusion architecture with two sensors with dynamical uncertainty is shown in Figure ref:fig:sensor_fusion_dynamic_uncertainty.
|
||||||
|
|
||||||
#+name: fig:sensor_fusion_dynamic_uncertainty
|
#+name: fig:sensor_fusion_dynamic_uncertainty
|
||||||
#+caption: Sensor fusion architecture with sensor dynamics uncertainty
|
#+caption: Sensor fusion architecture with sensor dynamics uncertainty
|
||||||
#+attr_latex: :scale 1
|
|
||||||
[[file:figs/sensor_fusion_dynamic_uncertainty.pdf]]
|
[[file:figs/sensor_fusion_dynamic_uncertainty.pdf]]
|
||||||
|
|
||||||
The super sensor dynamics eqref:eq:super_sensor_dyn_uncertainty is no longer equal to $1$ and now depends on the sensor dynamics uncertainty weights $w_i(s)$ as well as on the complementary filters $H_i(s)$.
|
The super sensor dynamics eqref:eq:super_sensor_dyn_uncertainty is no longer equal to $1$ and now depends on the sensor dynamics uncertainty weights $w_i(s)$ as well as on the complementary filters $H_i(s)$.
|
||||||
@ -359,11 +410,10 @@ The super sensor dynamics eqref:eq:super_sensor_dyn_uncertainty is no longer equ
|
|||||||
\frac{\hat{x}}{x} = 1 + w_1(s) H_1(s) \Delta_1(s) + w_2(s) H_2(s) \Delta_2(s)
|
\frac{\hat{x}}{x} = 1 + w_1(s) H_1(s) \Delta_1(s) + w_2(s) H_2(s) \Delta_2(s)
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
The uncertainty region of the super sensor can be represented in the complex plane by a circle centered on $1$ with a radius equal to $|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|$ as shown in Fig. ref:fig:uncertainty_set_super_sensor.
|
The uncertainty region of the super sensor can be represented in the complex plane by a circle centered on $1$ with a radius equal to $|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|$ as shown in Figure ref:fig:uncertainty_set_super_sensor.
|
||||||
|
|
||||||
#+name: fig:uncertainty_set_super_sensor
|
#+name: fig:uncertainty_set_super_sensor
|
||||||
#+caption: Uncertainty region of the super sensor dynamics in the complex plane (solid circle). The contribution of both sensors 1 and 2 to the uncertainty are represented respectively by a dotted and a dashed circle
|
#+caption: Uncertainty region of the super sensor dynamics in the complex plane (solid circle). The contribution of both sensors 1 and 2 to the uncertainty are represented respectively by a blue circle and a red circle
|
||||||
#+attr_latex: :scale 1
|
|
||||||
[[file:figs/uncertainty_set_super_sensor.pdf]]
|
[[file:figs/uncertainty_set_super_sensor.pdf]]
|
||||||
|
|
||||||
The maximum phase added $\Delta\phi(\omega)$ by the super sensor dynamics at frequency $\omega$ is then
|
The maximum phase added $\Delta\phi(\omega)$ by the super sensor dynamics at frequency $\omega$ is then
|
||||||
@ -554,7 +604,7 @@ They are found to be very close to each other and this shows the effectiveness o
|
|||||||
#+attr_latex: :scale 1
|
#+attr_latex: :scale 1
|
||||||
[[file:figs/comp_fir_ligo_hinf.pdf]]
|
[[file:figs/comp_fir_ligo_hinf.pdf]]
|
||||||
|
|
||||||
* Discussion :noexport:
|
* Discussion
|
||||||
** Alternative configuration
|
** Alternative configuration
|
||||||
- Feedback architecture : Similar to mixed sensitivity
|
- Feedback architecture : Similar to mixed sensitivity
|
||||||
- 2 inputs / 1 output
|
- 2 inputs / 1 output
|
||||||
@ -601,13 +651,13 @@ By choosing $H_1(s) \triangleq 1 - H_2(s) - H_3(s)$, the proposed $\mathcal{H}_\
|
|||||||
|
|
||||||
*** Example of generated complementary filters :ignore:
|
*** Example of generated complementary filters :ignore:
|
||||||
An example is given to validate the method where three sensors are used in different frequency bands (up to $\SI{1}{Hz}$, from $1$ to $\SI{10}{Hz}$ and above $\SI{10}{Hz}$ respectively).
|
An example is given to validate the method where three sensors are used in different frequency bands (up to $\SI{1}{Hz}$, from $1$ to $\SI{10}{Hz}$ and above $\SI{10}{Hz}$ respectively).
|
||||||
Three weighting functions are designed using eqref:eq:weight_formula and shown by dashed curves in Fig. ref:fig:hinf_three_synthesis_results.
|
Three weighting functions are designed using eqref:eq:weight_formula and shown by dashed curves in Fig. ref:fig:three_complementary_filters_results.
|
||||||
The bode plots of the obtained complementary filters are shown in Fig. ref:fig:hinf_three_synthesis_results.
|
The bode plots of the obtained complementary filters are shown in Fig. ref:fig:three_complementary_filters_results.
|
||||||
|
|
||||||
#+name: fig:hinf_three_synthesis_results
|
#+name: fig:three_complementary_filters_results
|
||||||
#+caption: Frequency response of the weighting functions and three complementary filters obtained using $\mathcal{H}_\infty$ synthesis
|
#+caption: Frequency response of the weighting functions and three complementary filters obtained using $\mathcal{H}_\infty$ synthesis
|
||||||
#+attr_latex: :scale 1
|
#+attr_latex: :scale 1
|
||||||
[[file:figs/hinf_three_synthesis_results.pdf]]
|
[[file:figs/three_complementary_filters_results.pdf]]
|
||||||
|
|
||||||
* Conclusion
|
* Conclusion
|
||||||
<<sec:conclusion>>
|
<<sec:conclusion>>
|
||||||
|
@ -1,4 +1,4 @@
|
|||||||
% Created 2021-04-30 ven. 11:16
|
% Created 2021-05-03 lun. 17:46
|
||||||
% Intended LaTeX compiler: pdflatex
|
% Intended LaTeX compiler: pdflatex
|
||||||
\documentclass[preprint, sort&compress]{elsarticle}
|
\documentclass[preprint, sort&compress]{elsarticle}
|
||||||
\usepackage[utf8]{inputenc}
|
\usepackage[utf8]{inputenc}
|
||||||
@ -14,6 +14,7 @@
|
|||||||
\usepackage{amssymb}
|
\usepackage{amssymb}
|
||||||
\usepackage{capt-of}
|
\usepackage{capt-of}
|
||||||
\usepackage{hyperref}
|
\usepackage{hyperref}
|
||||||
|
\usepackage{subcaption}
|
||||||
\usepackage{amsfonts}
|
\usepackage{amsfonts}
|
||||||
\usepackage{siunitx}
|
\usepackage{siunitx}
|
||||||
\journal{Mechanical Systems and Signal Processing}
|
\journal{Mechanical Systems and Signal Processing}
|
||||||
@ -57,7 +58,7 @@ Sensor fusion \sep{} Optimal filters \sep{} \(\mathcal{H}_\infty\) synthesis \se
|
|||||||
\end{frontmatter}
|
\end{frontmatter}
|
||||||
|
|
||||||
\section{Introduction}
|
\section{Introduction}
|
||||||
\label{sec:org341e767}
|
\label{sec:org0c85494}
|
||||||
\label{sec:introduction}
|
\label{sec:introduction}
|
||||||
\begin{itemize}
|
\begin{itemize}
|
||||||
\item \cite{bendat57_optim_filter_indep_measur_two} roots of sensor fusion
|
\item \cite{bendat57_optim_filter_indep_measur_two} roots of sensor fusion
|
||||||
@ -90,7 +91,7 @@ Sensor fusion \sep{} Optimal filters \sep{} \(\mathcal{H}_\infty\) synthesis \se
|
|||||||
\item Similar to feedback system:
|
\item Similar to feedback system:
|
||||||
\begin{itemize}
|
\begin{itemize}
|
||||||
\item \cite{plummer06_optim_compl_filter_their_applic_motion_measur} use H-Infinity to optimize complementary filters (flatten the super sensor noise spectral density)
|
\item \cite{plummer06_optim_compl_filter_their_applic_motion_measur} use H-Infinity to optimize complementary filters (flatten the super sensor noise spectral density)
|
||||||
\item \cite{jensen13_basic_uas} design of complementary filters with classical control theory
|
\item \cite{jensen13_basic_uas} design of complementary filters with classical control theory, PID
|
||||||
\end{itemize}
|
\end{itemize}
|
||||||
|
|
||||||
\item 3 complementary filters: \cite{becker15_compl_filter_desig_three_frequen_bands}
|
\item 3 complementary filters: \cite{becker15_compl_filter_desig_three_frequen_bands}
|
||||||
@ -104,45 +105,76 @@ Most of the requirements => shape of the complementary filters
|
|||||||
=> propose a way to shape complementary filters.
|
=> propose a way to shape complementary filters.
|
||||||
|
|
||||||
\section{Complementary Filters Requirements}
|
\section{Complementary Filters Requirements}
|
||||||
\label{sec:org77471d1}
|
\label{sec:org05c7608}
|
||||||
\label{sec:requirements}
|
\label{sec:requirements}
|
||||||
Complementary filters provides a framework for fusing signals from different sensors.
|
Complementary filters provides a framework for fusing signals from different sensors.
|
||||||
As the effectiveness of the fusion depends on the proper design of the complementary filters, they are expected to fulfill certain requirements.
|
As the effectiveness of the fusion depends on the proper design of the complementary filters, they are expected to fulfill certain requirements.
|
||||||
These requirements are discussed in this section.
|
These requirements are discussed in this section.
|
||||||
\subsection{Sensor Models}
|
|
||||||
\label{sec:org363af04}
|
|
||||||
\label{sec:sensor_models}
|
|
||||||
|
|
||||||
\begin{itemize}
|
|
||||||
\item Noise + dynamics
|
|
||||||
\end{itemize}
|
|
||||||
|
|
||||||
\begin{figure}[htbp]
|
|
||||||
\centering
|
|
||||||
\includegraphics[scale=1]{figs/sensor_model.pdf}
|
|
||||||
\caption{\label{fig:sensor_model}Basic Sensor Model}
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
\begin{itemize}
|
|
||||||
\item Suppose we calibrate the sensors
|
|
||||||
\end{itemize}
|
|
||||||
|
|
||||||
\begin{figure}[htbp]
|
|
||||||
\centering
|
|
||||||
\includegraphics[scale=1]{figs/sensor_model_calibrated.pdf}
|
|
||||||
\caption{\label{fig:sensor_model_calibrated}Calibrated Sensor}
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
\subsection{Sensor Fusion Architecture}
|
\subsection{Sensor Fusion Architecture}
|
||||||
\label{sec:org240da2b}
|
\label{sec:orgca80a74}
|
||||||
\label{sec:sensor_fusion}
|
\label{sec:sensor_fusion}
|
||||||
|
|
||||||
Let's consider two sensors measuring the same physical quantity \(x\) with dynamics \(G_1(s)\) and \(G_2(s)\), and with uncorrelated noise characteristics \(n_1\) and \(n_2\).
|
A general sensor fusion architecture is shown in Figure \ref{fig:sensor_fusion_overview} where several sensors (here two) are measuring the same physical quantity \(x\).
|
||||||
|
The two sensors output signals are estimates \(\hat{x}_1\) and \(\hat{x}_2\) of \(x\).
|
||||||
|
Each of these estimates are then filtered out by complementary filters and combined to form a new estimate \(\hat{x}\).
|
||||||
|
We further call the overall system from \(x\) to \(\hat{x}\) the ``super sensor''.
|
||||||
|
|
||||||
The signals from both sensors are fed into two complementary filters \(H_1(s)\) and \(H_2(s)\) and then combined to yield an estimate \(\hat{x}\) of \(x\) as shown in Fig. \ref{fig:fusion_super_sensor}.
|
\begin{figure}[htbp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_fusion_overview.pdf}
|
||||||
|
\caption{\label{fig:sensor_fusion_overview}Schematic of a sensor fusion architecture}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
The filters \(H_1(s)\) and \(H_2(s)\) are complementary which implies that:
|
||||||
|
\begin{equation}
|
||||||
|
\label{eq:comp_filter}
|
||||||
|
H_1(s) + H_2(s) = 1
|
||||||
|
\end{equation}
|
||||||
|
|
||||||
|
It will soon become clear why the complementary property is important.
|
||||||
|
|
||||||
|
\subsection{Sensor Models and Sensor Normalization}
|
||||||
|
\label{sec:orgfc7a65c}
|
||||||
|
\label{sec:sensor_models}
|
||||||
|
|
||||||
|
In order to study such sensor fusion architecture, a model of the sensor is required.
|
||||||
|
|
||||||
|
The sensor model is shown in Figure \ref{fig:sensor_model}.
|
||||||
|
It consists of a Linear Time Invariant system (LTI) \(G_i(s)\) representing the dynamics of the sensor and an additive noise input \(n_i\) representing its noise.
|
||||||
|
The model input \(x\) is the measured quantity and its output \(\tilde{x}_i\) is the ``raw'' output of the sensor.
|
||||||
|
|
||||||
|
Before filtering the sensor outputs \(\tilde{x}_i\) by the complementary filters, the sensors are usually normalized.
|
||||||
|
This normalization consists of obtaining an estimate \(\hat{G}_i(s)\) of the sensor dynamics \(G_i(s)\).
|
||||||
|
The raw output of the sensor \(\tilde{x}_i\) is then passed through the inverse of the sensor dynamics estimate as shown in Figure \ref{fig:sensor_model_calibrated}.
|
||||||
|
This way, the units of the estimates \(\hat{x}_i\) are equal to the units of the physical quantity \(x\).
|
||||||
|
The sensor dynamics estimate \(\hat{G}_1(s)\) can be a simple gain or more complex transfer functions.
|
||||||
|
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{subfigure}[b]{0.49\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model.pdf}
|
||||||
|
\caption{\label{fig:sensor_model} Basic sensor model consisting of a noise input $n_i$ and a dynamics $G_i(s)$}
|
||||||
|
\end{subfigure}
|
||||||
|
\hfill
|
||||||
|
\begin{subfigure}[b]{0.49\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model_calibrated.pdf}
|
||||||
|
\caption{\label{fig:sensor_model_calibrated} Calibrated sensors using the inverse of an estimate $\hat{G}_1(s)$ of the sensor dynamics}
|
||||||
|
\end{subfigure}
|
||||||
|
\caption{\label{fig:figure_name}Sensor models with an without normalization}
|
||||||
|
\centering
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
Let's now combine the two calibrated sensors models (Figure \ref{fig:sensor_model_calibrated}) with the sensor fusion architecture of figure \ref{fig:sensor_fusion_overview}.
|
||||||
|
The result is shown in Figure \ref{fig:fusion_super_sensor}.
|
||||||
|
|
||||||
|
The two sensors are measuring the same physical quantity \(x\) with dynamics \(G_1(s)\) and \(G_2(s)\), and with \emph{uncorrelated} noises \(n_1\) and \(n_2\).
|
||||||
|
The signals from both calibrated sensors are fed into two complementary filters \(H_1(s)\) and \(H_2(s)\) and then combined to yield an estimate \(\hat{x}\) of \(x\) as shown in Fig. \ref{fig:fusion_super_sensor}.
|
||||||
|
|
||||||
|
The super sensor output is therefore equal to:
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eq:comp_filter_estimate}
|
\label{eq:comp_filter_estimate}
|
||||||
\hat{x} = \left(G_1 H_1 + G_2 H_2\right) x + H_1 n_1 + H_2 n_2
|
\hat{x} = \Big( H_1(s) \hat{G}_1(s) G_1(s) + H_2(s) \hat{G}_2(s) G_2(s) \Big) x + H_1(s) \hat{G}_1(s) G_1(s) n_1 + H_2(s) \hat{G}_2(s) G_2(s) n_2
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
\begin{figure}[htbp]
|
\begin{figure}[htbp]
|
||||||
@ -151,71 +183,75 @@ The signals from both sensors are fed into two complementary filters \(H_1(s)\)
|
|||||||
\caption{\label{fig:fusion_super_sensor}Sensor fusion architecture}
|
\caption{\label{fig:fusion_super_sensor}Sensor fusion architecture}
|
||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
The complementary property of \(H_1(s)\) and \(H_2(s)\) implies that their transfer function sum is equal to one at all frequencies \eqref{eq:comp_filter}.
|
|
||||||
\begin{equation}
|
|
||||||
\label{eq:comp_filter}
|
|
||||||
H_1(s) + H_2(s) = 1
|
|
||||||
\end{equation}
|
|
||||||
|
|
||||||
\subsection{Noise Sensor Filtering}
|
\subsection{Noise Sensor Filtering}
|
||||||
\label{sec:orgc5064da}
|
\label{sec:org2a2ea67}
|
||||||
\label{sec:noise_filtering}
|
\label{sec:noise_filtering}
|
||||||
|
|
||||||
Let's first consider sensors with perfect dynamics
|
In this section, it is suppose that all the sensors are correctly calibrated, such that:
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eq:perfect_dynamics}
|
\label{eq:perfect_dynamics}
|
||||||
G_1(s) = G_2(s) = 1
|
\frac{\hat{x}_i}{x} = \hat{G}_i(s) G_i(s) \approx 1
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
The estimate \(\hat{x}\) is then described by
|
The effect of a non-ideal normalization will be discussed in the next section.
|
||||||
|
|
||||||
|
The super sensor output \(\hat{x}\) is then:
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eq:estimate_perfect_dyn}
|
\label{eq:estimate_perfect_dyn}
|
||||||
\hat{x} = x + H_1 n_1 + H_2 n_2
|
\hat{x} = x + H_1(s) n_1 + H_2(s) n_2
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
From \eqref{eq:estimate_perfect_dyn}, the complementary filters \(H_1(s)\) and \(H_2(s)\) are shown to only operate on the sensor's noise.
|
|
||||||
|
From \eqref{eq:estimate_perfect_dyn}, the complementary filters \(H_1(s)\) and \(H_2(s)\) are shown to only operate on the sensor's noises.
|
||||||
Thus, this sensor fusion architecture permits to filter the noise of both sensors without introducing any distortion in the physical quantity to be measured.
|
Thus, this sensor fusion architecture permits to filter the noise of both sensors without introducing any distortion in the physical quantity to be measured.
|
||||||
|
|
||||||
Let's define the estimation error \(\delta x\) by \eqref{eq:estimate_error}.
|
Let's define the estimation error \(\delta x\) by \eqref{eq:estimate_error}.
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eq:estimate_error}
|
\label{eq:estimate_error}
|
||||||
\delta x \triangleq \hat{x} - x = H_1 n_1 + H_2 n_2
|
\delta x \triangleq \hat{x} - x = H_1(s) n_1 + H_2(s) n_2
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
As shown in \eqref{eq:noise_filtering_psd}, the Power Spectral Density (PSD) of the estimation error \(\Phi_{\delta x}\) depends both on the norm of the two complementary filters and on the PSD of the noise sources \(\Phi_{n_1}\) and \(\Phi_{n_2}\).
|
As shown in \eqref{eq:noise_filtering_psd}, the Power Spectral Density (PSD) of the estimation error \(\Phi_{\delta x}\) depends both on the norm of the two complementary filters and on the PSD of the noise sources \(\Phi_{n_1}\) and \(\Phi_{n_2}\).
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eq:noise_filtering_psd}
|
\label{eq:noise_filtering_psd}
|
||||||
\Phi_{\delta x} = \left|H_1\right|^2 \Phi_{n_1} + \left|H_2\right|^2 \Phi_{n_2}
|
\Phi_{\delta x}(\omega) = \left|H_1(j\omega)\right|^2 \Phi_{n_1}(\omega) + \left|H_2(j\omega)\right|^2 \Phi_{n_2}(\omega)
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
Usually, the two sensors have high noise levels over distinct frequency regions.
|
Usually, the two sensors have high noise levels over distinct frequency regions.
|
||||||
In order to lower the noise of the super sensor, the value of the norm \(|H_1|\) has to be lowered when \(\Phi_{n_1}\) is larger than \(\Phi_{n_2}\) and that of \(|H_2|\) lowered when \(\Phi_{n_2}\) is larger than \(\Phi_{n_1}\).
|
In order to lower the noise of the super sensor, the value of the norm \(|H_1|\) has to be lowered when \(\Phi_{n_1}\) is larger than \(\Phi_{n_2}\) and that of \(|H_2|\) lowered when \(\Phi_{n_2}\) is larger than \(\Phi_{n_1}\).
|
||||||
|
|
||||||
\subsection{Robustness of the Fusion}
|
\subsection{Robustness of the Fusion}
|
||||||
\label{sec:orgfc9ea9e}
|
\label{sec:orgca279c9}
|
||||||
\label{sec:fusion_robustness}
|
\label{sec:fusion_robustness}
|
||||||
|
|
||||||
In practical systems the sensor dynamics is not perfect and \eqref{eq:perfect_dynamics} is not verified.
|
In practical systems the sensor normalization is not perfect and \eqref{eq:perfect_dynamics} is not verified.
|
||||||
In such case, one can use an inversion filter \(\hat{G}_i^{-1}(s)\) to normalize the sensor dynamics, where \(\hat{G}_i(s)\) is an estimate of the sensor dynamics \(G_i(s)\).
|
|
||||||
However, as there is always some level of uncertainty on the dynamics, it cannot be perfectly inverted and \(\hat{G}_i^{-1}(s) G_i(s) \neq 1\).
|
In order to study such imperfection, the sensor dynamical uncertainty is modeled using multiplicative input uncertainty (Figure \ref{fig:sensor_model_uncertainty}), where the nominal model is taken as the estimated model for the normalization \(\hat{G}_i(s)\), \(\Delta_i\) is any stable transfer function satisfying \(|\Delta_i(j\omega)| \le 1,\ \forall\omega\), and \(w_i(s)\) is a weight representing the magnitude of the uncertainty.
|
||||||
|
|
||||||
|
As the nominal sensor dynamics is taken as the normalized filter, the normalized sensor can be further simplified as shown in Figure \ref{fig:sensor_model_uncertainty_simplified}.
|
||||||
|
|
||||||
\begin{figure}[htbp]
|
\begin{figure}[htbp]
|
||||||
|
\begin{subfigure}[b]{0.59\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model_uncertainty.pdf}
|
||||||
|
\caption{\label{fig:sensor_model_uncertainty} Sensor with multiplicative input uncertainty}
|
||||||
|
\end{subfigure}
|
||||||
|
\hfill
|
||||||
|
\begin{subfigure}[b]{0.39\linewidth}
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1]{figs/sensor_model_uncertainty_simplified.pdf}
|
||||||
|
\caption{\label{fig:sensor_model_uncertainty_simplified} Simplified sensor model}
|
||||||
|
\end{subfigure}
|
||||||
|
\hfill
|
||||||
|
\caption{\label{fig:sensor_models_uncertainty}Sensor models with dynamical uncertainty}
|
||||||
\centering
|
\centering
|
||||||
\includegraphics[scale=1]{figs/sensor_model_uncertainty.png}
|
|
||||||
\caption{\label{fig:sensor_model_uncertainty}Input Uncertainty}
|
|
||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
\begin{figure}[htbp]
|
A sensor fusion architecture with two sensors with dynamical uncertainty is shown in Figure \ref{fig:sensor_fusion_dynamic_uncertainty}.
|
||||||
\centering
|
|
||||||
\includegraphics[scale=1]{figs/sensor_model_uncertainty_simplified.png}
|
|
||||||
\caption{\label{fig:sensor_model_uncertainty_simplified}Input Uncertainty}
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
Let's represent the resulting dynamic uncertainty of the inverted sensors by an input multiplicative uncertainty as shown in Fig. \ref{fig:sensor_fusion_dynamic_uncertainty} where \(\Delta_i\) is any stable transfer function satisfying \(|\Delta_i(j\omega)| \le 1,\ \forall\omega\), and \(|w_i(s)|\) is a weight representing the magnitude of the uncertainty.
|
|
||||||
|
|
||||||
\begin{figure}[htbp]
|
\begin{figure}[htbp]
|
||||||
\centering
|
\centering
|
||||||
\includegraphics[scale=1,scale=1]{figs/sensor_fusion_dynamic_uncertainty.pdf}
|
\includegraphics[scale=1]{figs/sensor_fusion_dynamic_uncertainty.pdf}
|
||||||
\caption{\label{fig:sensor_fusion_dynamic_uncertainty}Sensor fusion architecture with sensor dynamics uncertainty}
|
\caption{\label{fig:sensor_fusion_dynamic_uncertainty}Sensor fusion architecture with sensor dynamics uncertainty}
|
||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
@ -225,12 +261,12 @@ The super sensor dynamics \eqref{eq:super_sensor_dyn_uncertainty} is no longer e
|
|||||||
\frac{\hat{x}}{x} = 1 + w_1(s) H_1(s) \Delta_1(s) + w_2(s) H_2(s) \Delta_2(s)
|
\frac{\hat{x}}{x} = 1 + w_1(s) H_1(s) \Delta_1(s) + w_2(s) H_2(s) \Delta_2(s)
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
The uncertainty region of the super sensor can be represented in the complex plane by a circle centered on \(1\) with a radius equal to \(|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|\) as shown in Fig. \ref{fig:uncertainty_set_super_sensor}.
|
The uncertainty region of the super sensor can be represented in the complex plane by a circle centered on \(1\) with a radius equal to \(|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|\) as shown in Figure \ref{fig:uncertainty_set_super_sensor}.
|
||||||
|
|
||||||
\begin{figure}[htbp]
|
\begin{figure}[htbp]
|
||||||
\centering
|
\centering
|
||||||
\includegraphics[scale=1,scale=1]{figs/uncertainty_set_super_sensor.pdf}
|
\includegraphics[scale=1]{figs/uncertainty_set_super_sensor.pdf}
|
||||||
\caption{\label{fig:uncertainty_set_super_sensor}Uncertainty region of the super sensor dynamics in the complex plane (solid circle). The contribution of both sensors 1 and 2 to the uncertainty are represented respectively by a dotted and a dashed circle}
|
\caption{\label{fig:uncertainty_set_super_sensor}Uncertainty region of the super sensor dynamics in the complex plane (solid circle). The contribution of both sensors 1 and 2 to the uncertainty are represented respectively by a blue circle and a red circle}
|
||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
The maximum phase added \(\Delta\phi(\omega)\) by the super sensor dynamics at frequency \(\omega\) is then
|
The maximum phase added \(\Delta\phi(\omega)\) by the super sensor dynamics at frequency \(\omega\) is then
|
||||||
@ -249,12 +285,12 @@ where \(\Delta \phi_\text{max}\) is the maximum allowed added phase.
|
|||||||
Thus the norm of the complementary filter \(|H_i|\) should be made small at frequencies where \(|w_i|\) is large.
|
Thus the norm of the complementary filter \(|H_i|\) should be made small at frequencies where \(|w_i|\) is large.
|
||||||
|
|
||||||
\section{Complementary Filters Shaping using \(\mathcal{H}_\infty\) Synthesis}
|
\section{Complementary Filters Shaping using \(\mathcal{H}_\infty\) Synthesis}
|
||||||
\label{sec:org678f099}
|
\label{sec:org3d11f72}
|
||||||
\label{sec:hinf_method}
|
\label{sec:hinf_method}
|
||||||
As shown in Sec. \ref{sec:requirements}, the performance and robustness of the sensor fusion architecture depends on the complementary filters norms.
|
As shown in Sec. \ref{sec:requirements}, the performance and robustness of the sensor fusion architecture depends on the complementary filters norms.
|
||||||
Therefore, the development of a synthesis method of complementary filters that allows the shaping of their norm is necessary.
|
Therefore, the development of a synthesis method of complementary filters that allows the shaping of their norm is necessary.
|
||||||
\subsection{Synthesis Objective}
|
\subsection{Synthesis Objective}
|
||||||
\label{sec:orgf726b5b}
|
\label{sec:org867aacd}
|
||||||
\label{sec:synthesis_objective}
|
\label{sec:synthesis_objective}
|
||||||
The synthesis objective is to shape the norm of two filters \(H_1(s)\) and \(H_2(s)\) while ensuring their complementary property \eqref{eq:comp_filter}.
|
The synthesis objective is to shape the norm of two filters \(H_1(s)\) and \(H_2(s)\) while ensuring their complementary property \eqref{eq:comp_filter}.
|
||||||
This is equivalent as to finding stable transfer functions \(H_1(s)\) and \(H_2(s)\) such that conditions \eqref{eq:comp_filter_problem_form} are satisfied.
|
This is equivalent as to finding stable transfer functions \(H_1(s)\) and \(H_2(s)\) such that conditions \eqref{eq:comp_filter_problem_form} are satisfied.
|
||||||
@ -269,7 +305,7 @@ This is equivalent as to finding stable transfer functions \(H_1(s)\) and \(H_2(
|
|||||||
where \(W_1(s)\) and \(W_2(s)\) are two weighting transfer functions that are chosen to shape the norms of the corresponding filters.
|
where \(W_1(s)\) and \(W_2(s)\) are two weighting transfer functions that are chosen to shape the norms of the corresponding filters.
|
||||||
|
|
||||||
\subsection{Shaping of Complementary Filters using \(\mathcal{H}_\infty\) synthesis}
|
\subsection{Shaping of Complementary Filters using \(\mathcal{H}_\infty\) synthesis}
|
||||||
\label{sec:orga266a36}
|
\label{sec:orgec7ca01}
|
||||||
\label{sec:hinf_synthesis}
|
\label{sec:hinf_synthesis}
|
||||||
In order to express this optimization problem as a standard \(\mathcal{H}_\infty\) problem, the architecture shown in Fig. \ref{fig:h_infinity_robust_fusion} is used where the generalized plant \(P\) is described by \eqref{eq:generalized_plant}.
|
In order to express this optimization problem as a standard \(\mathcal{H}_\infty\) problem, the architecture shown in Fig. \ref{fig:h_infinity_robust_fusion} is used where the generalized plant \(P\) is described by \eqref{eq:generalized_plant}.
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
@ -305,7 +341,7 @@ The conditions \eqref{eq:hinf_cond_h1} and \eqref{eq:hinf_cond_h2} on the filter
|
|||||||
Therefore, all the conditions \eqref{eq:comp_filter_problem_form} are satisfied using this synthesis method based on \(\mathcal{H}_\infty\) synthesis, and thus it permits to shape complementary filters as desired.
|
Therefore, all the conditions \eqref{eq:comp_filter_problem_form} are satisfied using this synthesis method based on \(\mathcal{H}_\infty\) synthesis, and thus it permits to shape complementary filters as desired.
|
||||||
|
|
||||||
\subsection{Weighting Functions Design}
|
\subsection{Weighting Functions Design}
|
||||||
\label{sec:org911c399}
|
\label{sec:org1b0a8b2}
|
||||||
\label{sec:hinf_weighting_func}
|
\label{sec:hinf_weighting_func}
|
||||||
The proper design of the weighting functions is of primary importance for the success of the presented complementary filters \(\mathcal{H}_\infty\) synthesis.
|
The proper design of the weighting functions is of primary importance for the success of the presented complementary filters \(\mathcal{H}_\infty\) synthesis.
|
||||||
|
|
||||||
@ -350,7 +386,7 @@ The general shape of a weighting function generated using \eqref{eq:weight_formu
|
|||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
\subsection{Validation of the proposed synthesis method}
|
\subsection{Validation of the proposed synthesis method}
|
||||||
\label{sec:org6867aff}
|
\label{sec:org9091752}
|
||||||
\label{sec:hinf_example}
|
\label{sec:hinf_example}
|
||||||
Let's validate the proposed design method of complementary filters with a simple example where two complementary filters \(H_1(s)\) and \(H_2(s)\) have to be designed such that:
|
Let's validate the proposed design method of complementary filters with a simple example where two complementary filters \(H_1(s)\) and \(H_2(s)\) have to be designed such that:
|
||||||
\begin{itemize}
|
\begin{itemize}
|
||||||
@ -392,7 +428,7 @@ The bode plots of the obtained complementary filters are shown in Fig. \ref{fig:
|
|||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
\section{Application: Design of Complementary Filters used in the Active Vibration Isolation System at the LIGO}
|
\section{Application: Design of Complementary Filters used in the Active Vibration Isolation System at the LIGO}
|
||||||
\label{sec:org377e66e}
|
\label{sec:orgf547be3}
|
||||||
\label{sec:application_ligo}
|
\label{sec:application_ligo}
|
||||||
Several complementary filters are used in the active isolation system at the LIGO \cite{hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system}.
|
Several complementary filters are used in the active isolation system at the LIGO \cite{hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system}.
|
||||||
The requirements on those filters are very tight and thus their design is complex.
|
The requirements on those filters are very tight and thus their design is complex.
|
||||||
@ -401,7 +437,7 @@ The obtained FIR filters are compliant with the requirements. However they are o
|
|||||||
|
|
||||||
The effectiveness of the proposed method is demonstrated by designing complementary filters with the same requirements as the one described in \cite{hua05_low_ligo}.
|
The effectiveness of the proposed method is demonstrated by designing complementary filters with the same requirements as the one described in \cite{hua05_low_ligo}.
|
||||||
\subsection{Complementary Filters Specifications}
|
\subsection{Complementary Filters Specifications}
|
||||||
\label{sec:org75813ae}
|
\label{sec:orgd0486d1}
|
||||||
\label{sec:ligo_specifications}
|
\label{sec:ligo_specifications}
|
||||||
The specifications for one pair of complementary filters used at the LIGO are summarized below (for further details, refer to \cite{hua04_polyp_fir_compl_filter_contr_system}) and shown in Fig. \ref{fig:ligo_weights}:
|
The specifications for one pair of complementary filters used at the LIGO are summarized below (for further details, refer to \cite{hua04_polyp_fir_compl_filter_contr_system}) and shown in Fig. \ref{fig:ligo_weights}:
|
||||||
\begin{itemize}
|
\begin{itemize}
|
||||||
@ -412,7 +448,7 @@ The specifications for one pair of complementary filters used at the LIGO are su
|
|||||||
\end{itemize}
|
\end{itemize}
|
||||||
|
|
||||||
\subsection{Weighting Functions Design}
|
\subsection{Weighting Functions Design}
|
||||||
\label{sec:org7015511}
|
\label{sec:org1a654aa}
|
||||||
\label{sec:ligo_weights}
|
\label{sec:ligo_weights}
|
||||||
The weighting functions should be designed such that their inverse magnitude is as close as possible to the specifications in order to not over-constrain the synthesis problem.
|
The weighting functions should be designed such that their inverse magnitude is as close as possible to the specifications in order to not over-constrain the synthesis problem.
|
||||||
However, the order of each weight should stay reasonably small in order to reduce the computational costs of the optimization problem as well as for the physical implementation of the filters.
|
However, the order of each weight should stay reasonably small in order to reduce the computational costs of the optimization problem as well as for the physical implementation of the filters.
|
||||||
@ -428,7 +464,7 @@ The magnitudes of the weighting functions are shown in Fig. \ref{fig:ligo_weight
|
|||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
\subsection{\(\mathcal{H}_\infty\) Synthesis}
|
\subsection{\(\mathcal{H}_\infty\) Synthesis}
|
||||||
\label{sec:orge5b6fe6}
|
\label{sec:org93cef71}
|
||||||
\label{sec:ligo_results}
|
\label{sec:ligo_results}
|
||||||
\(\mathcal{H}_\infty\) synthesis is performed using the architecture shown in Fig. \ref{eq:generalized_plant}.
|
\(\mathcal{H}_\infty\) synthesis is performed using the architecture shown in Fig. \ref{eq:generalized_plant}.
|
||||||
The complementary filters obtained are of order \(27\).
|
The complementary filters obtained are of order \(27\).
|
||||||
@ -441,8 +477,63 @@ They are found to be very close to each other and this shows the effectiveness o
|
|||||||
\caption{\label{fig:comp_fir_ligo_hinf}Comparison of the FIR filters (solid) designed in \cite{hua05_low_ligo} with the filters obtained with \(\mathcal{H}_\infty\) synthesis (dashed)}
|
\caption{\label{fig:comp_fir_ligo_hinf}Comparison of the FIR filters (solid) designed in \cite{hua05_low_ligo} with the filters obtained with \(\mathcal{H}_\infty\) synthesis (dashed)}
|
||||||
\end{figure}
|
\end{figure}
|
||||||
|
|
||||||
|
\section{Discussion}
|
||||||
|
\label{sec:org016320e}
|
||||||
|
\subsection{Alternative configuration}
|
||||||
|
\label{sec:org69bd60e}
|
||||||
|
\begin{itemize}
|
||||||
|
\item Feedback architecture : Similar to mixed sensitivity
|
||||||
|
\item 2 inputs / 1 output
|
||||||
|
\end{itemize}
|
||||||
|
|
||||||
|
Explain differences
|
||||||
|
|
||||||
|
\subsection{Imposing zero at origin / roll-off}
|
||||||
|
\label{sec:org7f88310}
|
||||||
|
3 methods:
|
||||||
|
|
||||||
|
Link to literature about doing that with mixed sensitivity
|
||||||
|
|
||||||
|
\subsection{Synthesis of Three Complementary Filters}
|
||||||
|
\label{sec:orgd378e04}
|
||||||
|
\label{sec:hinf_three_comp_filters}
|
||||||
|
Some applications may require to merge more than two sensors.
|
||||||
|
In such a case, it is necessary to design as many complementary filters as the number of sensors used.
|
||||||
|
The synthesis problem is then to compute \(n\) stable transfer functions \(H_i(s)\) such that \eqref{eq:hinf_problem_gen} is satisfied.
|
||||||
|
\begin{subequations}
|
||||||
|
\label{eq:hinf_problem_gen}
|
||||||
|
\begin{align}
|
||||||
|
& \sum_{i=0}^n H_i(s) = 1 \label{eq:hinf_cond_compl_gen} \\
|
||||||
|
& \left| H_i(j\omega) \right| < \frac{1}{\left| W_i(j\omega) \right|}, \quad \forall \omega,\ i = 1 \dots n \label{eq:hinf_cond_perf_gen}
|
||||||
|
\end{align}
|
||||||
|
\end{subequations}
|
||||||
|
The synthesis method is generalized here for the synthesis of three complementary filters using the architecture shown in Fig. \ref{fig:comp_filter_three_hinf}.
|
||||||
|
|
||||||
|
The \(\mathcal{H}_\infty\) synthesis objective applied on \(P(s)\) is to design two stable filters \(H_2(s)\) and \(H_3(s)\) such that the \(\mathcal{H}_\infty\) norm of the transfer function from \(w\) to \([z_1,\ z_2, \ z_3]\) is less than one \eqref{eq:hinf_syn_obj_three}.
|
||||||
|
\begin{equation}
|
||||||
|
\label{eq:hinf_syn_obj_three}
|
||||||
|
\left\| \begin{matrix} \left[1 - H_2(s) - H_3(s)\right] W_1(s) \\ H_2(s) W_2(s) \\ H_3(s) W_3(s) \end{matrix} \right\|_\infty \le 1
|
||||||
|
\end{equation}
|
||||||
|
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1,scale=1]{figs/comp_filter_three_hinf.pdf}
|
||||||
|
\caption{\label{fig:comp_filter_three_hinf}Architecture for \(\mathcal{H}_\infty\) synthesis of three complementary filters}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
By choosing \(H_1(s) \triangleq 1 - H_2(s) - H_3(s)\), the proposed \(\mathcal{H}_\infty\) synthesis solves the design problem \eqref{eq:hinf_problem_gen}. \par
|
||||||
|
An example is given to validate the method where three sensors are used in different frequency bands (up to \(\SI{1}{Hz}\), from \(1\) to \(\SI{10}{Hz}\) and above \(\SI{10}{Hz}\) respectively).
|
||||||
|
Three weighting functions are designed using \eqref{eq:weight_formula} and shown by dashed curves in Fig. \ref{fig:three_complementary_filters_results}.
|
||||||
|
The bode plots of the obtained complementary filters are shown in Fig. \ref{fig:three_complementary_filters_results}.
|
||||||
|
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[scale=1,scale=1]{figs/three_complementary_filters_results.pdf}
|
||||||
|
\caption{\label{fig:three_complementary_filters_results}Frequency response of the weighting functions and three complementary filters obtained using \(\mathcal{H}_\infty\) synthesis}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
\section{Conclusion}
|
\section{Conclusion}
|
||||||
\label{sec:org39b90d9}
|
\label{sec:org46a0029}
|
||||||
\label{sec:conclusion}
|
\label{sec:conclusion}
|
||||||
This paper has shown how complementary filters can be used to combine multiple sensors in order to obtain a super sensor.
|
This paper has shown how complementary filters can be used to combine multiple sensors in order to obtain a super sensor.
|
||||||
Typical specification on the super sensor noise and on the robustness of the sensor fusion has been shown to be linked to the norm of the complementary filters.
|
Typical specification on the super sensor noise and on the robustness of the sensor fusion has been shown to be linked to the norm of the complementary filters.
|
||||||
@ -450,7 +541,7 @@ Therefore, a synthesis method that permits the shaping of the complementary filt
|
|||||||
Future work will aim at further developing this synthesis method for the robust and optimal synthesis of complementary filters used in sensor fusion.
|
Future work will aim at further developing this synthesis method for the robust and optimal synthesis of complementary filters used in sensor fusion.
|
||||||
|
|
||||||
\section*{Acknowledgment}
|
\section*{Acknowledgment}
|
||||||
\label{sec:org1ece332}
|
\label{sec:orgc8d6b1f}
|
||||||
This research benefited from a FRIA grant from the French Community of Belgium.
|
This research benefited from a FRIA grant from the French Community of Belgium.
|
||||||
|
|
||||||
\bibliographystyle{elsarticle-num}
|
\bibliographystyle{elsarticle-num}
|
||||||
|
605
matlab/index.org
@ -185,9 +185,10 @@ set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
|||||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||||
hold off;
|
hold off;
|
||||||
xlim([freqs(1), freqs(end)]);
|
xlim([freqs(1), freqs(end)]);
|
||||||
ylim([5e-4, 20]);
|
ylim([1e-4, 20]);
|
||||||
xticks([0.1, 1, 10, 100, 1000]);
|
xticks([0.1, 1, 10, 100, 1000]);
|
||||||
legend('location', 'northeast', 'FontSize', 8);
|
leg = legend('location', 'southeast', 'FontSize', 8);
|
||||||
|
leg.ItemTokenSize(1) = 18;
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+begin_src matlab :tangle no :exports results :results file replace
|
#+begin_src matlab :tangle no :exports results :results file replace
|
||||||
@ -278,7 +279,8 @@ ylabel('Magnitude');
|
|||||||
set(gca, 'XTickLabel',[]);
|
set(gca, 'XTickLabel',[]);
|
||||||
ylim([1e-4, 20]);
|
ylim([1e-4, 20]);
|
||||||
yticks([1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1]);
|
yticks([1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1]);
|
||||||
legend('location', 'southeast', 'FontSize', 8, 'NumColumns', 2);
|
leg = legend('location', 'southeast', 'FontSize', 8, 'NumColumns', 2);
|
||||||
|
leg.ItemTokenSize(1) = 18;
|
||||||
|
|
||||||
% Phase
|
% Phase
|
||||||
ax2 = nexttile;
|
ax2 = nexttile;
|
||||||
@ -387,8 +389,9 @@ plot(freqs, 1./abs(squeeze(freqresp(W3, freqs, 'Hz'))), '--', 'DisplayName', '$|
|
|||||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||||
hold off;
|
hold off;
|
||||||
xlim([freqs(1), freqs(end)]);
|
xlim([freqs(1), freqs(end)]); ylim([2e-4, 1.3e1])
|
||||||
legend('location', 'northeast', 'FontSize', 8);
|
leg = legend('location', 'northeast', 'FontSize', 8);
|
||||||
|
leg.ItemTokenSize(1) = 18;
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+begin_src matlab :tangle no :exports results :results file replace
|
#+begin_src matlab :tangle no :exports results :results file replace
|
||||||
@ -481,7 +484,8 @@ set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
|||||||
ylabel('Magnitude');
|
ylabel('Magnitude');
|
||||||
set(gca, 'XTickLabel',[]);
|
set(gca, 'XTickLabel',[]);
|
||||||
ylim([1e-4, 20]);
|
ylim([1e-4, 20]);
|
||||||
legend('location', 'northeast', 'FontSize', 8, 'NumColumns', 2);
|
leg = legend('location', 'northeast', 'FontSize', 8, 'NumColumns', 2);
|
||||||
|
leg.ItemTokenSize(1) = 18;
|
||||||
|
|
||||||
% Phase
|
% Phase
|
||||||
ax2 = nexttile;
|
ax2 = nexttile;
|
||||||
@ -573,7 +577,8 @@ xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
|||||||
hold off;
|
hold off;
|
||||||
xlim([freqs(1), freqs(end)]);
|
xlim([freqs(1), freqs(end)]);
|
||||||
ylim([1e-4, 10]);
|
ylim([1e-4, 10]);
|
||||||
legend('location', 'southeast', 'FontSize', 8);
|
leg = legend('location', 'southeast', 'FontSize', 8);
|
||||||
|
leg.ItemTokenSize(1) = 18;
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+begin_src matlab :tangle no :exports results :results file replace
|
#+begin_src matlab :tangle no :exports results :results file replace
|
||||||
@ -752,19 +757,19 @@ ylim([5e-3, 5]);
|
|||||||
% Phase
|
% Phase
|
||||||
ax2 = nexttile;
|
ax2 = nexttile;
|
||||||
hold on;
|
hold on;
|
||||||
plot(w, 180/pi*angle(H), 'k-');
|
plot(w, 180/pi*unwrap(angle(H)), 'k-');
|
||||||
plot(w, 180/pi*angle(1-H), 'k--');
|
plot(w, 180/pi*unwrap(angle(1-H)), 'k--');
|
||||||
hold off;
|
hold off;
|
||||||
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||||
set(gca, 'XScale', 'log');
|
set(gca, 'XScale', 'log');
|
||||||
yticks([-180:90:180]); ylim([-180, 180]);
|
yticks([-450:90:180]); ylim([-450, 200]);
|
||||||
|
|
||||||
linkaxes([ax1,ax2],'x');
|
linkaxes([ax1,ax2],'x');
|
||||||
xlim([1e-3, 1]);
|
xlim([1e-3, 1]);
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+begin_src matlab :tangle no :exports results :results file replace
|
#+begin_src matlab :tangle no :exports results :results file replace
|
||||||
exportFig('figs/fir_filter_ligo.pdf', 'width', 'wide', 'height', 'normal');
|
exportFig('figs/fir_filter_ligo.pdf', 'width', 'wide', 'height', 'tall');
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+name: fig:fir_filter_ligo
|
#+name: fig:fir_filter_ligo
|
||||||
@ -828,7 +833,8 @@ xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
|||||||
hold off;
|
hold off;
|
||||||
xlim([freqs(1), freqs(end)]);
|
xlim([freqs(1), freqs(end)]);
|
||||||
ylim([1e-3, 10]);
|
ylim([1e-3, 10]);
|
||||||
legend('location', 'southeast', 'FontSize', 8);
|
leg = legend('location', 'southeast', 'FontSize', 8);
|
||||||
|
leg.ItemTokenSize(1) = 18;
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+begin_src matlab :tangle no :exports results :results file replace
|
#+begin_src matlab :tangle no :exports results :results file replace
|
||||||
@ -925,7 +931,8 @@ xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
|||||||
hold off;
|
hold off;
|
||||||
xlim([freqs(1), freqs(end)]);
|
xlim([freqs(1), freqs(end)]);
|
||||||
ylim([1e-3, 10]);
|
ylim([1e-3, 10]);
|
||||||
legend('location', 'southeast', 'FontSize', 8);
|
leg = legend('location', 'southeast', 'FontSize', 8);
|
||||||
|
leg.ItemTokenSize(1) = 18;
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+begin_src matlab :tangle no :exports results :results file replace
|
#+begin_src matlab :tangle no :exports results :results file replace
|
||||||
@ -1014,7 +1021,7 @@ addpath('./matlab');
|
|||||||
addpath('./matlab/src');
|
addpath('./matlab/src');
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
#+begin_src matlab :exec no
|
#+begin_src matlab :eval no
|
||||||
addpath('./src');
|
addpath('./src');
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
@ -1425,6 +1432,576 @@ xlim([freqs(1), freqs(end)]);
|
|||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
|
|
||||||
|
** Compare "open-loop" shaping with "close-loop" shaping
|
||||||
|
*** Simple weights
|
||||||
|
#+begin_src matlab
|
||||||
|
n = 2; w0 = 2*pi*11; G0 = 1/10; G1 = 1000; Gc = 1/2;
|
||||||
|
W1 = ((((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n);
|
||||||
|
|
||||||
|
n = 3; w0 = 2*pi*10; G0 = 1000; G1 = 0.1; Gc = 1/2;
|
||||||
|
W2 = ((((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pol = [0 W1 1;
|
||||||
|
W2 -W1 0];
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results value replace :exports results :tangle no
|
||||||
|
sprintf('The number of states of Pol is %i', length(Pol.StateName))
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
: The number of states of Pol is 7
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
[Hol, ~, gamma, ~] = hinfsyn(Pol, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hol_2 = Hol;
|
||||||
|
Hol_1 = 1 - Hol;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pcl = [0 W2 1
|
||||||
|
W1 -W2 -1];
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results value replace :exports results :tangle no
|
||||||
|
sprintf('The number of states of Pcl is %i', length(Pcl.StateName))
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
: The number of states of Pcl is 8
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
[Hcl, ~, gamma, ~] = hinfsyn(Pcl, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hcl_1 = 1 - 1/(1 + Hcl);
|
||||||
|
Hcl_2 = 1/(1 + Hcl);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports results
|
||||||
|
size(Hol_1)
|
||||||
|
size(Hol_2)
|
||||||
|
size(Hcl_1)
|
||||||
|
size(Hcl_2)
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
#+begin_example
|
||||||
|
size(Hol_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
size(Hol_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
size(Hcl_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
size(Hcl_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
'org_babel_eoe'
|
||||||
|
ans =
|
||||||
|
'org_babel_eoe'
|
||||||
|
#+end_example
|
||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
freqs = logspace(-1, 3, 1000);
|
||||||
|
|
||||||
|
figure;
|
||||||
|
tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None');
|
||||||
|
|
||||||
|
% Magnitude
|
||||||
|
ax1 = nexttile([2, 1]);
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_1, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_2, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_1, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_2, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
|
||||||
|
hold off;
|
||||||
|
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||||
|
ylabel('Magnitude');
|
||||||
|
set(gca, 'XTickLabel',[]);
|
||||||
|
leg = legend('location', 'southeast', 'FontSize', 8, 'NumColumns', 2);
|
||||||
|
leg.ItemTokenSize(1) = 16;
|
||||||
|
|
||||||
|
% Phase
|
||||||
|
ax2 = nexttile;
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_1, freqs, 'Hz')))), '-');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_2, freqs, 'Hz')))), '-');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_1, freqs, 'Hz')))), '--');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_2, freqs, 'Hz')))), '--');
|
||||||
|
set(gca, 'XScale', 'log');
|
||||||
|
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||||
|
hold off;
|
||||||
|
yticks([-450:90:180]); ylim([-450, 200]);
|
||||||
|
|
||||||
|
linkaxes([ax1,ax2],'x');
|
||||||
|
xlim([freqs(1), freqs(end)]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
*** Simple weights with LMI
|
||||||
|
#+begin_src matlab
|
||||||
|
n = 2; w0 = 2*pi*11; G0 = 1/10; G1 = 1000; Gc = 1/2;
|
||||||
|
W1 = ((((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n);
|
||||||
|
|
||||||
|
n = 3; w0 = 2*pi*10; G0 = 1000; G1 = 0.1; Gc = 1/2;
|
||||||
|
W2 = ((((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pol = [0 W1 1;
|
||||||
|
W2 -W1 0];
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results value replace :exports results :tangle no
|
||||||
|
sprintf('The number of states of Pol is %i', length(Pol.StateName))
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
: The number of states of Pol is 7
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
[Hol, ~, gamma, ~] = hinfsyn(Pol, 1, 1,'TOLGAM', 0.001, 'METHOD', 'lmi', 'DISPLAY', 'on');
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hol_2 = Hol;
|
||||||
|
Hol_1 = 1 - Hol;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pcl = [0 W2 1
|
||||||
|
W1 -W2 -1];
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results value replace :exports results :tangle no
|
||||||
|
sprintf('The number of states of Pcl is %i', length(Pcl.StateName))
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
: The number of states of Pcl is 8
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
[Hcl, ~, gamma, ~] = hinfsyn(Pcl, 1, 1,'TOLGAM', 0.001, 'METHOD', 'lmi', 'DISPLAY', 'on');
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hcl_1 = 1 - 1/(1 + Hcl);
|
||||||
|
Hcl_2 = 1/(1 + Hcl);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports results
|
||||||
|
size(Hol_1)
|
||||||
|
size(Hol_2)
|
||||||
|
size(Hcl_1)
|
||||||
|
size(Hcl_2)
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
#+begin_example
|
||||||
|
size(Hol_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
size(Hol_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
size(Hcl_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
size(Hcl_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 5 states.
|
||||||
|
'org_babel_eoe'
|
||||||
|
ans =
|
||||||
|
'org_babel_eoe'
|
||||||
|
#+end_example
|
||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
freqs = logspace(-1, 3, 1000);
|
||||||
|
|
||||||
|
figure;
|
||||||
|
tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None');
|
||||||
|
|
||||||
|
% Magnitude
|
||||||
|
ax1 = nexttile([2, 1]);
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_1, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_2, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_1, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_2, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
|
||||||
|
hold off;
|
||||||
|
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||||
|
ylabel('Magnitude');
|
||||||
|
set(gca, 'XTickLabel',[]);
|
||||||
|
leg = legend('location', 'southeast', 'FontSize', 8, 'NumColumns', 2);
|
||||||
|
leg.ItemTokenSize(1) = 16;
|
||||||
|
|
||||||
|
% Phase
|
||||||
|
ax2 = nexttile;
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_1, freqs, 'Hz')))), '-');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_2, freqs, 'Hz')))), '-');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_1, freqs, 'Hz')))), '--');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_2, freqs, 'Hz')))), '--');
|
||||||
|
set(gca, 'XScale', 'log');
|
||||||
|
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||||
|
hold off;
|
||||||
|
yticks([-450:90:180]); ylim([-450, 200]);
|
||||||
|
|
||||||
|
linkaxes([ax1,ax2],'x');
|
||||||
|
xlim([freqs(1), freqs(end)]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
*** Complex weights
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
w1 = 2*pi*0.008; x1 = 0.35;
|
||||||
|
w2 = 2*pi*0.04; x2 = 0.5;
|
||||||
|
w3 = 2*pi*0.05; x3 = 0.5;
|
||||||
|
|
||||||
|
% Slope of +3 from w1
|
||||||
|
wH = 0.008*(s^2/w1^2 + 2*x1/w1*s + 1)*(s/w1 + 1);
|
||||||
|
% Little bump from w2 to w3
|
||||||
|
wH = wH*(s^2/w2^2 + 2*x2/w2*s + 1)/(s^2/w3^2 + 2*x3/w3*s + 1);
|
||||||
|
% No Slope at high frequencies
|
||||||
|
wH = wH/(s^2/w3^2 + 2*x3/w3*s + 1)/(s/w3 + 1);
|
||||||
|
% Little bump between w2 and w3
|
||||||
|
w0 = 2*pi*0.045; xi = 0.1; A = 2; n = 1;
|
||||||
|
wH = wH*((s^2 + 2*w0*xi*A^(1/n)*s + w0^2)/(s^2 + 2*w0*xi*s + w0^2))^n;
|
||||||
|
|
||||||
|
wH = 1/wH;
|
||||||
|
W1 = wH;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
n = 20; Rp = 1; Wp = 2*pi*0.102;
|
||||||
|
[b,a] = cheby1(n, Rp, Wp, 'high', 's');
|
||||||
|
wL = 0.04*tf(a, b);
|
||||||
|
|
||||||
|
wL = 1/wL;
|
||||||
|
W2 = wL;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pol = ss([0 W1 1;
|
||||||
|
W2 -W1 0]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pcl = ss([0 W2 1
|
||||||
|
W1 -W2 -1]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports results
|
||||||
|
size(Pol)
|
||||||
|
size(Pcl)
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
: size(Pol)
|
||||||
|
: State-space model with 2 outputs, 3 inputs, and 27 states.
|
||||||
|
: size(Pcl)
|
||||||
|
: State-space model with 2 outputs, 3 inputs, and 27 states.
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
for iter = 1:10
|
||||||
|
[Hol, ~, gamma, ~] = hinfsyn(Pol, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'off');
|
||||||
|
end;
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hol_1 = 1 - Hol;
|
||||||
|
Hol_2 = Hol;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
for iter = 1:10
|
||||||
|
[Hcl, ~, gamma, ~] = hinfsyn(Pcl, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'off');
|
||||||
|
end
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hcl_1 = 1 - 1/(1 + Hcl);
|
||||||
|
Hcl_2 = 1/(1 + Hcl);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports results
|
||||||
|
size(Hol_1)
|
||||||
|
size(Hol_2)
|
||||||
|
size(Hcl_1)
|
||||||
|
size(Hcl_2)
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
#+begin_example
|
||||||
|
size(Hol_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 27 states.
|
||||||
|
size(Hol_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 27 states.
|
||||||
|
size(Hcl_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 27 states.
|
||||||
|
size(Hcl_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 27 states.
|
||||||
|
'org_babel_eoe'
|
||||||
|
ans =
|
||||||
|
'org_babel_eoe'
|
||||||
|
#+end_example
|
||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
freqs = logspace(-3, 1, 1000);
|
||||||
|
|
||||||
|
figure;
|
||||||
|
tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None');
|
||||||
|
|
||||||
|
% Magnitude
|
||||||
|
ax1 = nexttile([2, 1]);
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_1, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_2, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_1, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_2, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
|
||||||
|
hold off;
|
||||||
|
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||||
|
ylabel('Magnitude');
|
||||||
|
set(gca, 'XTickLabel',[]);
|
||||||
|
ylim([5e-3, 10]);
|
||||||
|
leg = legend('location', 'southeast', 'FontSize', 8, 'NumColumns', 2);
|
||||||
|
leg.ItemTokenSize(1) = 16;
|
||||||
|
|
||||||
|
% Phase
|
||||||
|
ax2 = nexttile;
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_1, freqs, 'Hz')))), '-');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_2, freqs, 'Hz')))), '-');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_1, freqs, 'Hz')))), '--');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_2, freqs, 'Hz')))), '--');
|
||||||
|
set(gca, 'XScale', 'log');
|
||||||
|
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||||
|
hold off;
|
||||||
|
yticks([-450:90:180]); ylim([-450, 200]);
|
||||||
|
|
||||||
|
linkaxes([ax1,ax2],'x');
|
||||||
|
xlim([freqs(1), freqs(end)]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
*** Complex weights with alternative conf
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pcl = ss([W1 -W1;
|
||||||
|
0 W2;
|
||||||
|
1 -1]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
[Hcl, ~, gamma, ~] = hinfsyn(Pcl, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hcl_1 = 1 - 1/(1 + Hcl);
|
||||||
|
Hcl_2 = 1/(1 + Hcl);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
*** Complex weights with LMI
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
w1 = 2*pi*0.008; x1 = 0.35;
|
||||||
|
w2 = 2*pi*0.04; x2 = 0.5;
|
||||||
|
w3 = 2*pi*0.05; x3 = 0.5;
|
||||||
|
|
||||||
|
% Slope of +3 from w1
|
||||||
|
wH = 0.008*(s^2/w1^2 + 2*x1/w1*s + 1)*(s/w1 + 1);
|
||||||
|
% Little bump from w2 to w3
|
||||||
|
wH = wH*(s^2/w2^2 + 2*x2/w2*s + 1)/(s^2/w3^2 + 2*x3/w3*s + 1);
|
||||||
|
% No Slope at high frequencies
|
||||||
|
wH = wH/(s^2/w3^2 + 2*x3/w3*s + 1)/(s/w3 + 1);
|
||||||
|
% Little bump between w2 and w3
|
||||||
|
w0 = 2*pi*0.045; xi = 0.1; A = 2; n = 1;
|
||||||
|
wH = wH*((s^2 + 2*w0*xi*A^(1/n)*s + w0^2)/(s^2 + 2*w0*xi*s + w0^2))^n;
|
||||||
|
|
||||||
|
wH = 1/wH;
|
||||||
|
W1 = ss(minreal(wH));
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
n = 20; Rp = 1; Wp = 2*pi*0.102;
|
||||||
|
[b,a] = cheby1(n, Rp, Wp, 'high', 's');
|
||||||
|
wL = 0.04*tf(a, b);
|
||||||
|
|
||||||
|
wL = 1/wL;
|
||||||
|
W2 = ss(minreal(wL));
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pol = [0 W1 1;
|
||||||
|
W2 -W1 0];
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Pcl = [0 W2 1
|
||||||
|
W1 -W2 -1];
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
[Hol, ~, gamma, ~] = hinfsyn(Pol, 1, 1,'TOLGAM', 0.001, 'METHOD', 'lmi', 'DISPLAY', 'on');
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hol_1 = 1 - Hol;
|
||||||
|
Hol_2 = Hol;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports both
|
||||||
|
tic;
|
||||||
|
[Hcl, ~, gamma, ~] = hinfsyn(Pcl, 1, 1,'TOLGAM', 0.001, 'METHOD', 'lmi', 'DISPLAY', 'on');
|
||||||
|
toc;
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab
|
||||||
|
Hcl_1 = 1 - 1/(1 + Hcl);
|
||||||
|
Hcl_2 = 1/(1 + Hcl);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :results output replace :exports results
|
||||||
|
size(Hol_1)
|
||||||
|
size(Hol_2)
|
||||||
|
size(Hcl_1)
|
||||||
|
size(Hcl_2)
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
#+begin_example
|
||||||
|
size(Hol_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 34 states.
|
||||||
|
size(Hol_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 34 states.
|
||||||
|
size(Hcl_1)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 47 states.
|
||||||
|
size(Hcl_2)
|
||||||
|
State-space model with 1 outputs, 1 inputs, and 47 states.
|
||||||
|
'org_babel_eoe'
|
||||||
|
ans =
|
||||||
|
'org_babel_eoe'
|
||||||
|
#+end_example
|
||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
freqs = logspace(-3, 1, 1000);
|
||||||
|
|
||||||
|
figure;
|
||||||
|
tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None');
|
||||||
|
|
||||||
|
% Magnitude
|
||||||
|
ax1 = nexttile([2, 1]);
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_1, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hol_2, freqs, 'Hz'))), '-', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ OL');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_1, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_H(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, abs(squeeze(freqresp(Hcl_2, freqs, 'Hz'))), '--', ...
|
||||||
|
'DisplayName', '$H_L(s)$ - $\mathcal{H}_\infty$ CL');
|
||||||
|
|
||||||
|
hold off;
|
||||||
|
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||||
|
ylabel('Magnitude');
|
||||||
|
set(gca, 'XTickLabel',[]);
|
||||||
|
ylim([5e-3, 10]);
|
||||||
|
leg = legend('location', 'southeast', 'FontSize', 8, 'NumColumns', 2);
|
||||||
|
leg.ItemTokenSize(1) = 16;
|
||||||
|
|
||||||
|
% Phase
|
||||||
|
ax2 = nexttile;
|
||||||
|
hold on;
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_1, freqs, 'Hz')))), '-');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hol_2, freqs, 'Hz')))), '-');
|
||||||
|
|
||||||
|
set(gca,'ColorOrderIndex',1);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_1, freqs, 'Hz')))), '--');
|
||||||
|
set(gca,'ColorOrderIndex',2);
|
||||||
|
plot(freqs, 180/pi*unwrap(angle(squeeze(freqresp(Hcl_2, freqs, 'Hz')))), '--');
|
||||||
|
set(gca, 'XScale', 'log');
|
||||||
|
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||||
|
hold off;
|
||||||
|
yticks([-450:90:180]); ylim([-450, 200]);
|
||||||
|
|
||||||
|
linkaxes([ax1,ax2],'x');
|
||||||
|
xlim([freqs(1), freqs(end)]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
*** Conclusion
|
||||||
|
#+begin_important
|
||||||
|
There is no difference between " open-loop" shaping and "close-loop" shaping:
|
||||||
|
- same " solving" time
|
||||||
|
- same obtained filter orders
|
||||||
|
#+end_important
|
||||||
|
|
||||||
* Impose a positive slope at DC or a negative slope at infinite frequency
|
* Impose a positive slope at DC or a negative slope at infinite frequency
|
||||||
** Introduction :ignore:
|
** Introduction :ignore:
|
||||||
|
|
||||||
|
532
tikz/index.org
@ -21,20 +21,62 @@
|
|||||||
|
|
||||||
Configuration file is accessible [[file:config.org][here]].
|
Configuration file is accessible [[file:config.org][here]].
|
||||||
|
|
||||||
|
* Sensor Fusion - Overview
|
||||||
|
#+begin_src latex :file sensor_fusion_overview.pdf
|
||||||
|
\definecolor{myblue}{rgb}{0, 0.447, 0.741}
|
||||||
|
\definecolor{myred}{rgb}{0.8500, 0.325, 0.098}
|
||||||
|
|
||||||
|
\begin{tikzpicture}
|
||||||
|
\node[branch] (x) at (0, 0);
|
||||||
|
\node[block, above right=0.3 and 0.5 of x](sensor1){Sensor 1};
|
||||||
|
\node[block, below right=0.3 and 0.5 of x](sensor2){Sensor 2};
|
||||||
|
|
||||||
|
\node[block, right=1.1 of sensor1](H1){$H_1(s)$};
|
||||||
|
\node[block, right=1.1 of sensor2](H2){$H_2(s)$};
|
||||||
|
|
||||||
|
\node[addb, right=5.0 of x](add){};
|
||||||
|
|
||||||
|
\draw[] ($(x)+(-0.7, 0)$) node[above right]{$x$} -- (x.center);
|
||||||
|
\draw[->] (x.center) |- (sensor1.west);
|
||||||
|
\draw[->] (x.center) |- (sensor2.west);
|
||||||
|
\draw[->] (sensor1.east) -- node[midway, above]{$\hat{x}_1$} (H1.west);
|
||||||
|
\draw[->] (sensor2.east) -- node[midway, above]{$\hat{x}_2$} (H2.west);
|
||||||
|
\draw[->] (H1) -| (add.north);
|
||||||
|
\draw[->] (H2) -| (add.south);
|
||||||
|
\draw[->] (add.east) -- ++(0.9, 0) node[above left]{$\hat{x}$};
|
||||||
|
|
||||||
|
\begin{scope}[on background layer]
|
||||||
|
\node[fit={($(H2.south-|x) + (0, -0.2)$) ($(H1.north-|add.east) + (0.2, 0.6)$)}, fill=black!10!white, draw, inner sep=6pt] (supersensor) {};
|
||||||
|
\node[below] at (supersensor.north) {Super Sensor};
|
||||||
|
|
||||||
|
\node[fit={(sensor2.south west) (sensor1.north east)}, fill=black!20!white, draw, inner sep=6pt] (sensors) {};
|
||||||
|
\node[align=center] at (sensors.center) {{\tiny Normalized}\\[-0.5em]{\tiny Sensors}};
|
||||||
|
|
||||||
|
\node[fit={(H2.south west) (H1.north-|add.east)}, fill=black!20!white, draw, inner sep=6pt] (filters) {};
|
||||||
|
\node[align=center] at ($(filters.center) + (-0.3, 0)$) {{\tiny Complementary}\\[-0.5em]{\tiny Filters}};
|
||||||
|
\end{scope}
|
||||||
|
\end{tikzpicture}
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+name: fig:sensor_fusion_overview
|
||||||
|
#+caption: Sensor Fusion Architecture - Overview
|
||||||
|
#+RESULTS:
|
||||||
|
[[file:figs/sensor_fusion_overview.png]]
|
||||||
|
|
||||||
* Sensor Model
|
* Sensor Model
|
||||||
#+begin_src latex :file sensor_model.pdf
|
#+begin_src latex :file sensor_model.pdf
|
||||||
\begin{tikzpicture}
|
\begin{tikzpicture}
|
||||||
\node[addb](add1){};
|
\node[addb](add1){};
|
||||||
\node[block, right=0.8 of add1](G1){$G_1(s)$};
|
\node[block, right=0.8 of add1](G1){$G_i(s)$};
|
||||||
|
|
||||||
\draw[->] ($(add1.west)+(-0.7, 0)$) node[above right]{$x$} -- (add1.west);
|
\draw[->] ($(add1.west)+(-0.7, 0)$) node[above right]{$x$} -- (add1.west);
|
||||||
\draw[<-] (add1.north) -- ++(0, 0.7)node[below right](n1){$n_1$};
|
\draw[<-] (add1.north) -- ++(0, 0.7)node[below right](n1){$n_i$};
|
||||||
\draw[->] (add1.east) -- (G1.west);
|
\draw[->] (add1.east) -- (G1.west);
|
||||||
\draw[->] (G1.east) -- ++(0.7, 0) node[above left]{$\tilde{x}_1$};
|
\draw[->] (G1.east) -- ++(0.7, 0) node[above left]{$\tilde{x}_i$};
|
||||||
|
|
||||||
\begin{scope}[on background layer]
|
\begin{scope}[on background layer]
|
||||||
\node[fit={(add1.west |- G1.south) (n1.north -| G1.east)}, fill=black!20!white, draw, inner sep=3pt] (sensor1) {};
|
\node[fit={(add1.west |- G1.south) (n1.north -| G1.east)}, fill=black!20!white, draw, inner sep=3pt] (sensor1) {};
|
||||||
\node[below left] at (sensor1.north east) {Sensor 1};
|
\node[below left] at (sensor1.north east) {Sensor};
|
||||||
\end{scope}
|
\end{scope}
|
||||||
\end{tikzpicture}
|
\end{tikzpicture}
|
||||||
#+end_src
|
#+end_src
|
||||||
@ -48,21 +90,21 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
#+begin_src latex :file sensor_model_calibrated.pdf
|
#+begin_src latex :file sensor_model_calibrated.pdf
|
||||||
\begin{tikzpicture}
|
\begin{tikzpicture}
|
||||||
\node[addb](add1){};
|
\node[addb](add1){};
|
||||||
\node[block, right=0.8 of add1](G1){$G_1(s)$};
|
\node[block, right=0.8 of add1](G1){$G_i(s)$};
|
||||||
\node[block, right=0.8 of G1](G1inv){$\hat{G}_1^{-1}(s)$};
|
\node[block, right=0.8 of G1](G1inv){$\hat{G}_i^{-1}(s)$};
|
||||||
|
|
||||||
\draw[->] ($(add1.west)+(-0.7, 0)$) node[above right]{$x$} -- (add1.west);
|
\draw[->] ($(add1.west)+(-0.7, 0)$) node[above right]{$x$} -- (add1.west);
|
||||||
\draw[<-] (add1.north) -- ++(0, 0.7)node[below right](n1){$n_1$};
|
\draw[<-] (add1.north) -- ++(0, 0.7)node[below right](n1){$n_i$};
|
||||||
\draw[->] (add1.east) -- (G1.west);
|
\draw[->] (add1.east) -- (G1.west);
|
||||||
\draw[->] (G1.east) -- (G1inv.west) node[above left]{$\tilde{x}_1$};
|
\draw[->] (G1.east) -- (G1inv.west) node[above left]{$\tilde{x}_i$};
|
||||||
\draw[->] (G1inv.east) -- ++(0.8, 0) node[above left]{$\hat{x}_1$};
|
\draw[->] (G1inv.east) -- ++(0.8, 0) node[above left]{$\hat{x}_i$};
|
||||||
|
|
||||||
\begin{scope}[on background layer]
|
\begin{scope}[on background layer]
|
||||||
\node[fit={(add1.west |- G1inv.south) (n1.north -| G1inv.east)}, fill=black!10!white, draw, inner sep=6pt] (sensor1cal) {};
|
\node[fit={(add1.west |- G1inv.south) (n1.north -| G1inv.east)}, fill=black!10!white, draw, inner sep=6pt] (sensor1cal) {};
|
||||||
\node[below left] at (sensor1cal.north east) {Calibration};
|
\node[below left, align=right] at (sensor1cal.north east) {{\tiny Normalized}\\[-0.5em]{\tiny sensor}};
|
||||||
|
|
||||||
\node[fit={(add1.west |- G1.south) (n1.north -| G1.east)}, fill=black!20!white, draw, inner sep=3pt] (sensor1) {};
|
\node[fit={(add1.west |- G1.south) (n1.north -| G1.east)}, fill=black!20!white, draw, inner sep=3pt] (sensor1) {};
|
||||||
\node[below left] at (sensor1.north east) {Sensor 1};
|
\node[below left] at (sensor1.north east) {Sensor};
|
||||||
\end{scope}
|
\end{scope}
|
||||||
\end{tikzpicture}
|
\end{tikzpicture}
|
||||||
#+end_src
|
#+end_src
|
||||||
@ -72,73 +114,8 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
#+RESULTS:
|
#+RESULTS:
|
||||||
[[file:figs/sensor_model_calibrated.png]]
|
[[file:figs/sensor_model_calibrated.png]]
|
||||||
|
|
||||||
* Sensor Model with Uncertainty
|
|
||||||
#+begin_src latex :file sensor_model_uncertainty.pdf
|
|
||||||
\begin{tikzpicture}
|
|
||||||
\node[branch] (input) at (0,0) {};
|
|
||||||
\node[block, above right= 0.4 and 0.4 of input](W1){$w_1(s)$};
|
|
||||||
\node[block, right=0.4 of W1](delta1){$\Delta_1(s)$};
|
|
||||||
\node[addb] (addu) at ($(delta1.east|-input) + (0.4, 0)$) {};
|
|
||||||
\node[addb, right=0.4 of addu] (addn) {};
|
|
||||||
\node[block, right=0.4 of addn] (G1) {$\hat{G}_1(s)$};
|
|
||||||
\node[block, right=0.8 of G1](G1inv){$\hat{G}_1^{-1}(s)$};
|
|
||||||
|
|
||||||
\draw[->] ($(input)+(-0.7, 0)$) node[above right]{$x$} -- (addu);
|
|
||||||
\draw[->] (input.center) |- (W1.west);
|
|
||||||
\draw[->] (W1.east) -- (delta1.west);
|
|
||||||
\draw[->] (delta1.east) -| (addu.north);
|
|
||||||
\draw[->] (addu.east) -- (addn.west);
|
|
||||||
\draw[->] (addn.east) -- (G1.west);
|
|
||||||
\draw[<-] (addn.north) -- ++(0, 0.7)node[below right](n1){$n_1$};
|
|
||||||
\draw[->] (G1.east) -- (G1inv.west) node[above left]{$\tilde{x}_1$};
|
|
||||||
\draw[->] (G1inv.east) -- ++(0.8, 0) node[above left]{$\hat{x}_1$};
|
|
||||||
|
|
||||||
\begin{scope}[on background layer]
|
|
||||||
\node[fit={(input.west |- G1inv.south) (delta1.north -| G1inv.east)}, fill=black!10!white, draw, inner sep=6pt] (sensor1cal) {};
|
|
||||||
\node[below left] at (sensor1cal.north east) {Calibration};
|
|
||||||
|
|
||||||
\node[fit={(input.west |- G1.south) (delta1.north -| G1.east)}, fill=black!20!white, draw, inner sep=3pt] (sensor1) {};
|
|
||||||
\node[below left] at (sensor1.north east) {Sensor 1};
|
|
||||||
\end{scope}
|
|
||||||
\end{tikzpicture}
|
|
||||||
#+end_src
|
|
||||||
|
|
||||||
#+name: fig:sensor_model_uncertainty
|
|
||||||
#+caption: Input Uncertainty
|
|
||||||
#+RESULTS:
|
|
||||||
[[file:figs/sensor_model_uncertainty.png]]
|
|
||||||
|
|
||||||
* Sensor Model with Uncertainty - Simplified
|
|
||||||
#+begin_src latex :file sensor_model_uncertainty_simplified.pdf
|
|
||||||
\begin{tikzpicture}
|
|
||||||
\node[branch] (input) at (0,0) {};
|
|
||||||
\node[block, above right= 0.4 and 0.4 of input](W1){$w_1(s)$};
|
|
||||||
\node[block, right=0.4 of W1](delta1){$\Delta_1(s)$};
|
|
||||||
\node[addb] (addu) at ($(delta1.east|-input) + (0.4, 0)$) {};
|
|
||||||
\node[addb, right=0.4 of addu] (addn) {};
|
|
||||||
|
|
||||||
\draw[->] ($(input)+(-0.7, 0)$) node[above right]{$x$} -- (addu);
|
|
||||||
\draw[->] (input.center) |- (W1.west);
|
|
||||||
\draw[->] (W1.east) -- (delta1.west);
|
|
||||||
\draw[->] (delta1.east) -| (addu.north);
|
|
||||||
\draw[->] (addu.east) -- (addn.west);
|
|
||||||
\draw[<-] (addn.north) -- ++(0, 0.7)node[below right](n1){$n_1$};
|
|
||||||
\draw[->] (addn.east) -- ++(0.9, 0) node[above left]{$\hat{x}_1$};
|
|
||||||
|
|
||||||
\begin{scope}[on background layer]
|
|
||||||
\node[fit={(input.west |- addu.south) ($(delta1.north -| addn.east) + (0.1, 0.3)$)}, fill=black!10!white, draw, inner sep=6pt] (sensor1cal) {};
|
|
||||||
\node[below left] at (sensor1cal.north east) {Calibrated Sensor};
|
|
||||||
\end{scope}
|
|
||||||
\end{tikzpicture}
|
|
||||||
#+end_src
|
|
||||||
|
|
||||||
#+name: fig:sensor_model_uncertainty_simplified
|
|
||||||
#+caption: Input Uncertainty
|
|
||||||
#+RESULTS:
|
|
||||||
[[file:figs/sensor_model_uncertainty_simplified.png]]
|
|
||||||
|
|
||||||
* Sensor Fusion Architecture
|
* Sensor Fusion Architecture
|
||||||
#+begin_src latex :file fusion_super_sensor.pdf :tangle figs/fusion_super_sensor.tex
|
#+begin_src latex :file fusion_super_sensor.pdf
|
||||||
\definecolor{myblue}{rgb}{0, 0.447, 0.741}
|
\definecolor{myblue}{rgb}{0, 0.447, 0.741}
|
||||||
\definecolor{myred}{rgb}{0.8500, 0.325, 0.098}
|
\definecolor{myred}{rgb}{0.8500, 0.325, 0.098}
|
||||||
|
|
||||||
@ -174,12 +151,12 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
\node[below left] at (supersensor.north east) {Super Sensor};
|
\node[below left] at (supersensor.north east) {Super Sensor};
|
||||||
|
|
||||||
\node[fit={(add1.west |- G1inv.south) (n1.north -| G1inv.east)}, fill=myblue!20!white, draw, inner sep=6pt] (sensor1cal) {};
|
\node[fit={(add1.west |- G1inv.south) (n1.north -| G1inv.east)}, fill=myblue!20!white, draw, inner sep=6pt] (sensor1cal) {};
|
||||||
\node[below left] at (sensor1cal.north east) {Calibration};
|
\node[below left, align=right] at (sensor1cal.north east) {{\tiny Normalized}\\[-0.5em]{\tiny sensor}};
|
||||||
\node[fit={(add1.west |- G1.south) (n1.north -| G1.east)}, fill=myblue!30!white, draw, inner sep=3pt] (sensor1) {};
|
\node[fit={(add1.west |- G1.south) (n1.north -| G1.east)}, fill=myblue!30!white, draw, inner sep=3pt] (sensor1) {};
|
||||||
\node[below left] at (sensor1.north east) {Sensor 1};
|
\node[below left] at (sensor1.north east) {Sensor 1};
|
||||||
|
|
||||||
\node[fit={(add2.west |- G2inv.south) (n2.north -| G2inv.east)}, fill=myred!20!white, draw, inner sep=6pt] (sensor2cal) {};
|
\node[fit={(add2.west |- G2inv.south) (n2.north -| G2inv.east)}, fill=myred!20!white, draw, inner sep=6pt] (sensor2cal) {};
|
||||||
\node[below left] at (sensor2cal.north east) {Calibration};
|
\node[below left, align=right] at (sensor2cal.north east) {{\tiny Normalized}\\[-0.5em]{\tiny sensor}};
|
||||||
\node[fit={(add2.west |- G2.south) (n2.north -| G2.east)}, fill=myred!30!white, draw, inner sep=3pt] (sensor2) {};
|
\node[fit={(add2.west |- G2.south) (n2.north -| G2.east)}, fill=myred!30!white, draw, inner sep=3pt] (sensor2) {};
|
||||||
\node[below left] at (sensor2.north east) {Sensor 2};
|
\node[below left] at (sensor2.north east) {Sensor 2};
|
||||||
\end{scope}
|
\end{scope}
|
||||||
@ -191,6 +168,71 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
#+RESULTS:
|
#+RESULTS:
|
||||||
[[file:figs/fusion_super_sensor.png]]
|
[[file:figs/fusion_super_sensor.png]]
|
||||||
|
|
||||||
|
* Sensor Model with Uncertainty
|
||||||
|
#+begin_src latex :file sensor_model_uncertainty.pdf
|
||||||
|
\begin{tikzpicture}
|
||||||
|
\node[branch] (input) at (0,0) {};
|
||||||
|
\node[block, above right= 0.4 and 0.4 of input](W1){$w_1(s)$};
|
||||||
|
\node[block, right=0.4 of W1](delta1){$\Delta_1(s)$};
|
||||||
|
\node[addb] (addu) at ($(delta1.east|-input) + (0.4, 0)$) {};
|
||||||
|
\node[addb, right=0.4 of addu] (addn) {};
|
||||||
|
\node[block, right=0.4 of addn] (G1) {$\hat{G}_1(s)$};
|
||||||
|
\node[block, right=0.8 of G1](G1inv){$\hat{G}_1^{-1}(s)$};
|
||||||
|
|
||||||
|
\draw[->] ($(input)+(-0.7, 0)$) node[above right]{$x$} -- (addu);
|
||||||
|
\draw[->] (input.center) |- (W1.west);
|
||||||
|
\draw[->] (W1.east) -- (delta1.west);
|
||||||
|
\draw[->] (delta1.east) -| (addu.north);
|
||||||
|
\draw[->] (addu.east) -- (addn.west);
|
||||||
|
\draw[->] (addn.east) -- (G1.west);
|
||||||
|
\draw[<-] (addn.north) -- ++(0, 0.7)node[below right](n1){$n_1$};
|
||||||
|
\draw[->] (G1.east) -- (G1inv.west) node[above left]{$\tilde{x}_1$};
|
||||||
|
\draw[->] (G1inv.east) -- ++(0.8, 0) node[above left]{$\hat{x}_1$};
|
||||||
|
|
||||||
|
\begin{scope}[on background layer]
|
||||||
|
\node[fit={(input.west |- G1inv.south) (delta1.north -| G1inv.east)}, fill=black!10!white, draw, inner sep=6pt] (sensor1cal) {};
|
||||||
|
\node[below left, align=right] at (sensor1cal.north east) {{\tiny Normalized}\\[-0.5em]{\tiny sensor}};
|
||||||
|
|
||||||
|
\node[fit={(input.west |- G1.south) (delta1.north -| G1.east)}, fill=black!20!white, draw, inner sep=3pt] (sensor1) {};
|
||||||
|
\node[below left] at (sensor1.north east) {Sensor};
|
||||||
|
\end{scope}
|
||||||
|
\end{tikzpicture}
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+name: fig:sensor_model_uncertainty
|
||||||
|
#+caption: Input Uncertainty
|
||||||
|
#+RESULTS:
|
||||||
|
[[file:figs/sensor_model_uncertainty.png]]
|
||||||
|
|
||||||
|
* Sensor Model with Uncertainty - Simplified
|
||||||
|
#+begin_src latex :file sensor_model_uncertainty_simplified.pdf
|
||||||
|
\begin{tikzpicture}
|
||||||
|
\node[branch] (input) at (0,0) {};
|
||||||
|
\node[block, above right= 0.4 and 0.4 of input](W1){$w_1(s)$};
|
||||||
|
\node[block, right=0.4 of W1](delta1){$\Delta_1(s)$};
|
||||||
|
\node[addb] (addu) at ($(delta1.east|-input) + (0.4, 0)$) {};
|
||||||
|
\node[addb, right=0.4 of addu] (addn) {};
|
||||||
|
|
||||||
|
\draw[->] ($(input)+(-0.7, 0)$) node[above right]{$x$} -- (addu);
|
||||||
|
\draw[->] (input.center) |- (W1.west);
|
||||||
|
\draw[->] (W1.east) -- (delta1.west);
|
||||||
|
\draw[->] (delta1.east) -| (addu.north);
|
||||||
|
\draw[->] (addu.east) -- (addn.west);
|
||||||
|
\draw[<-] (addn.north) -- ++(0, 0.7)node[below right](n1){$n_1$};
|
||||||
|
\draw[->] (addn.east) -- ++(0.9, 0) node[above left]{$\hat{x}_1$};
|
||||||
|
|
||||||
|
\begin{scope}[on background layer]
|
||||||
|
\node[fit={(input.west |- addu.south) ($(delta1.north -| addn.east) + (0.1, 0.4)$)}, fill=black!10!white, draw, inner sep=6pt] (sensor1cal) {};
|
||||||
|
\node[below] at (sensor1cal.north) {Normalized Sensor};
|
||||||
|
\end{scope}
|
||||||
|
\end{tikzpicture}
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+name: fig:sensor_model_uncertainty_simplified
|
||||||
|
#+caption: Input Uncertainty
|
||||||
|
#+RESULTS:
|
||||||
|
[[file:figs/sensor_model_uncertainty_simplified.png]]
|
||||||
|
|
||||||
* Sensor fusion architecture with sensor dynamics uncertainty
|
* Sensor fusion architecture with sensor dynamics uncertainty
|
||||||
#+begin_src latex :file sensor_fusion_dynamic_uncertainty.pdf :tangle figs/fusion_super_sensor.tex
|
#+begin_src latex :file sensor_fusion_dynamic_uncertainty.pdf :tangle figs/fusion_super_sensor.tex
|
||||||
\definecolor{myblue}{rgb}{0, 0.447, 0.741}
|
\definecolor{myblue}{rgb}{0, 0.447, 0.741}
|
||||||
@ -199,10 +241,10 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
\begin{tikzpicture}
|
\begin{tikzpicture}
|
||||||
\node[branch] (x) at (0, 0);
|
\node[branch] (x) at (0, 0);
|
||||||
|
|
||||||
\node[branch, above right=0.9 and 0.3 of x] (input1) {};
|
\node[branch, above right=1.0 and 0.3 of x] (input1) {};
|
||||||
\node[branch, below right=0.9 and 0.3 of x] (input2) {};
|
\node[branch, below right=1.0 and 0.3 of x] (input2) {};
|
||||||
\node[block, above right= 0.4 and 0.4 of input1](W1){$w_1(s)$};
|
\node[block, above right= 0.4 and 0.3 of input1](W1){$w_1(s)$};
|
||||||
\node[block, above right= 0.4 and 0.4 of input2](W2){$w_2(s)$};
|
\node[block, above right= 0.4 and 0.3 of input2](W2){$w_2(s)$};
|
||||||
\node[block, right=0.4 of W1](delta1){$\Delta_1(s)$};
|
\node[block, right=0.4 of W1](delta1){$\Delta_1(s)$};
|
||||||
\node[block, right=0.4 of W2](delta2){$\Delta_2(s)$};
|
\node[block, right=0.4 of W2](delta2){$\Delta_2(s)$};
|
||||||
\node[addb] (addu1) at ($(delta1.east|-input1) + (0.4, 0)$) {};
|
\node[addb] (addu1) at ($(delta1.east|-input1) + (0.4, 0)$) {};
|
||||||
@ -222,12 +264,12 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
\draw[->] (W1.east) -- (delta1.west);
|
\draw[->] (W1.east) -- (delta1.west);
|
||||||
\draw[->] (delta1.east) -| (addu1.north);
|
\draw[->] (delta1.east) -| (addu1.north);
|
||||||
\draw[->] (addu1.east) -- (addn1.west);
|
\draw[->] (addu1.east) -- (addn1.west);
|
||||||
\draw[<-] (addn1.north) -- ++(0, 0.7)node[below right](n1){$n_1$};
|
\draw[<-] (addn1.north) -- ++(0, 0.6)node[below right](n1){$n_1$};
|
||||||
\draw[->] (input2.center) |- (W2.west);
|
\draw[->] (input2.center) |- (W2.west);
|
||||||
\draw[->] (W2.east) -- (delta2.west);
|
\draw[->] (W2.east) -- (delta2.west);
|
||||||
\draw[->] (delta2.east) -| (addu2.north);
|
\draw[->] (delta2.east) -| (addu2.north);
|
||||||
\draw[->] (addu2.east) -- (addn2.west);
|
\draw[->] (addu2.east) -- (addn2.west);
|
||||||
\draw[<-] (addn2.north) -- ++(0, 0.7)node[below right](n2){$n_2$};
|
\draw[<-] (addn2.north) -- ++(0, 0.6)node[below right](n2){$n_2$};
|
||||||
|
|
||||||
\draw[->] (addn1.east) -- (H1.west) node[above left]{$\hat{x}_1$};
|
\draw[->] (addn1.east) -- (H1.west) node[above left]{$\hat{x}_1$};
|
||||||
\draw[->] (addn2.east) -- (H2.west) node[above left]{$\hat{x}_2$};
|
\draw[->] (addn2.east) -- (H2.west) node[above left]{$\hat{x}_2$};
|
||||||
@ -240,10 +282,10 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
\node[below left] at (supersensor.north east) {Super Sensor};
|
\node[below left] at (supersensor.north east) {Super Sensor};
|
||||||
|
|
||||||
\node[fit={(input1.west |- addu1.south) ($(delta1.north -| addn1.east) + (0.1, 0.0)$)}, fill=myblue!20!white, draw, inner sep=6pt] (sensor1cal) {};
|
\node[fit={(input1.west |- addu1.south) ($(delta1.north -| addn1.east) + (0.1, 0.0)$)}, fill=myblue!20!white, draw, inner sep=6pt] (sensor1cal) {};
|
||||||
\node[below left] at (sensor1cal.north east) {Sensor 1};
|
\node[below left, align=right] at (sensor1cal.north east) {{\tiny Normalized}\\[-0.5em]{\tiny sensor 1}};
|
||||||
|
|
||||||
\node[fit={(input2.west |- addu2.south) ($(delta2.north -| addn1.east) + (0.1, 0.0)$)}, fill=myred!20!white, draw, inner sep=6pt] (sensor2cal) {};
|
\node[fit={(input2.west |- addu2.south) ($(delta2.north -| addn1.east) + (0.1, 0.0)$)}, fill=myred!20!white, draw, inner sep=6pt] (sensor2cal) {};
|
||||||
\node[below left] at (sensor2cal.north east) {Sensor 2};
|
\node[below left, align=right] at (sensor2cal.north east) {{\tiny Normalized}\\[-0.5em]{\tiny sensor 2}};
|
||||||
\end{scope}
|
\end{scope}
|
||||||
\end{tikzpicture}
|
\end{tikzpicture}
|
||||||
#+end_src
|
#+end_src
|
||||||
@ -332,86 +374,10 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
#+RESULTS:
|
#+RESULTS:
|
||||||
[[file:figs/h_infinity_robust_fusion.png]]
|
[[file:figs/h_infinity_robust_fusion.png]]
|
||||||
|
|
||||||
* Frequency response of the weighting functions and complementary filters obtained using $\mathcal{H}_\infty$ synthesis
|
|
||||||
#+begin_src latex :file hinf_synthesis_results.pdf :tangle figs/hinf_synthesis_results.tex :exports both
|
|
||||||
\setlength\fwidth{6.5cm}
|
|
||||||
\setlength\fheight{6cm}
|
|
||||||
|
|
||||||
\begin{tikzpicture}
|
|
||||||
\begin{axis}[%
|
|
||||||
width=1.0\fwidth,
|
|
||||||
height=0.5\fheight,
|
|
||||||
at={(0.0\fwidth, 0.47\fheight)},
|
|
||||||
scale only axis,
|
|
||||||
xmode=log,
|
|
||||||
xmin=0.1,
|
|
||||||
xmax=1000,
|
|
||||||
xtick={0.1, 1, 10, 100, 1000},
|
|
||||||
xticklabels={{}},
|
|
||||||
xminorticks=true,
|
|
||||||
ymode=log,
|
|
||||||
ymin=0.0005,
|
|
||||||
ymax=20,
|
|
||||||
ytick={0.001, 0.01, 0.1, 1, 10},
|
|
||||||
yminorticks=true,
|
|
||||||
ylabel={Magnitude},
|
|
||||||
xminorgrids,
|
|
||||||
yminorgrids,
|
|
||||||
]
|
|
||||||
\addplot [color=mycolor1, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=H1, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_filters_results.csv};
|
|
||||||
|
|
||||||
\addplot [color=mycolor2, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=H2, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_filters_results.csv};
|
|
||||||
|
|
||||||
\addplot [color=mycolor1, dashed, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=W1, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_weights.csv};
|
|
||||||
|
|
||||||
\addplot [color=mycolor2, dashed, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=W2, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_weights.csv};
|
|
||||||
\end{axis}
|
|
||||||
|
|
||||||
\begin{axis}[%
|
|
||||||
width=1.0\fwidth,
|
|
||||||
height=0.45\fheight,
|
|
||||||
at={(0.0\fwidth, 0.0\fheight)},
|
|
||||||
scale only axis,
|
|
||||||
xmode=log,
|
|
||||||
xmin=0.1,
|
|
||||||
xmax=1000,
|
|
||||||
xtick={0.1, 1, 10, 100, 1000},
|
|
||||||
xminorticks=true,
|
|
||||||
xlabel={Frequency [Hz]},
|
|
||||||
ymin=-200,
|
|
||||||
ymax=200,
|
|
||||||
ytick={-180, -90, 0, 90, 180},
|
|
||||||
ylabel={Phase [deg]},
|
|
||||||
xminorgrids,
|
|
||||||
legend style={at={(1,1.1)}, outer sep=2pt , anchor=north east, legend cell align=left, align=left, draw=black, nodes={scale=0.7, transform shape}},
|
|
||||||
]
|
|
||||||
\addlegendimage{color=mycolor1, dashed, line width=1.5pt}
|
|
||||||
\addlegendentry{$W_1^{-1}$};
|
|
||||||
\addlegendimage{color=mycolor2, dashed, line width=1.5pt}
|
|
||||||
\addlegendentry{$W_2^{-1}$};
|
|
||||||
\addplot [color=mycolor1, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H1p, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_filters_results.csv};
|
|
||||||
\addlegendentry{$H_1$};
|
|
||||||
\addplot [color=mycolor2, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H2p, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_filters_results.csv};
|
|
||||||
\addlegendentry{$H_2$};
|
|
||||||
\end{axis}
|
|
||||||
\end{tikzpicture}
|
|
||||||
#+end_src
|
|
||||||
|
|
||||||
#+name: fig:hinf_synthesis_results
|
|
||||||
#+caption: Frequency response of the weighting functions and complementary filters obtained using $\mathcal{H}_\infty$ synthesis ([[./figs/hinf_synthesis_results.png][png]], [[./figs/hinf_synthesis_results.pdf][pdf]], [[./figs/hinf_synthesis_results.tex][tex]]).
|
|
||||||
#+RESULTS:
|
|
||||||
[[file:figs/hinf_synthesis_results.png]]
|
|
||||||
|
|
||||||
* Architecture for $\mathcal{H}_\infty$ synthesis of three complementary filters
|
* Architecture for $\mathcal{H}_\infty$ synthesis of three complementary filters
|
||||||
#+begin_src latex :file comp_filter_three_hinf.pdf :tangle figs/comp_filter_three_hinf.tex
|
#+begin_src latex :file comp_filter_three_hinf.pdf :tangle figs/comp_filter_three_hinf.tex
|
||||||
\begin{tikzpicture}
|
\begin{tikzpicture}
|
||||||
\node[block={5.0cm}{3.5cm}, fill=black!20!white, dashed] (P) {};
|
\node[block={5.0cm}{4.5cm}, fill=black!20!white] (P) {};
|
||||||
\node[above] at (P.north) {$P(s)$};
|
\node[above] at (P.north) {$P(s)$};
|
||||||
|
|
||||||
\coordinate[] (inputw) at ($(P.south west)!0.8!(P.north west) + (-0.7, 0)$);
|
\coordinate[] (inputw) at ($(P.south west)!0.8!(P.north west) + (-0.7, 0)$);
|
||||||
@ -453,241 +419,3 @@ Configuration file is accessible [[file:config.org][here]].
|
|||||||
#+RESULTS:
|
#+RESULTS:
|
||||||
[[file:figs/comp_filter_three_hinf.png]]
|
[[file:figs/comp_filter_three_hinf.png]]
|
||||||
|
|
||||||
* Frequency response of the weighting functions and three complementary filters obtained using $\mathcal{H}_\infty$ synthesis
|
|
||||||
#+begin_src latex :file hinf_three_synthesis_results.pdf :tangle figs/hinf_three_synthesis_results.tex :exports both
|
|
||||||
\setlength\fwidth{6.5cm}
|
|
||||||
\setlength\fheight{6cm}
|
|
||||||
|
|
||||||
\begin{tikzpicture}
|
|
||||||
\begin{axis}[%
|
|
||||||
width=1.0\fwidth,
|
|
||||||
height=0.55\fheight,
|
|
||||||
at={(0.0\fwidth, 0.42\fheight)},
|
|
||||||
scale only axis,
|
|
||||||
xmode=log,
|
|
||||||
xmin=0.1,
|
|
||||||
xmax=100,
|
|
||||||
xticklabels={{}},
|
|
||||||
xminorticks=true,
|
|
||||||
ymode=log,
|
|
||||||
ymin=0.0005,
|
|
||||||
ymax=20,
|
|
||||||
ytick={0.001, 0.01, 0.1, 1, 10},
|
|
||||||
yminorticks=true,
|
|
||||||
ylabel={Magnitude},
|
|
||||||
xminorgrids,
|
|
||||||
yminorgrids,
|
|
||||||
legend columns=2,
|
|
||||||
legend style={
|
|
||||||
/tikz/column 2/.style={
|
|
||||||
column sep=5pt,
|
|
||||||
},
|
|
||||||
at={(1,0)}, outer sep=2pt , anchor=south east, legend cell align=left, align=left, draw=black, nodes={scale=0.7, transform shape}
|
|
||||||
},
|
|
||||||
]
|
|
||||||
\addplot [color=mycolor1, dashed, line width=1.5pt]
|
|
||||||
table [x=freqs, y=W1, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_weights.csv};
|
|
||||||
\addlegendentry{${W_1}^{-1}$};
|
|
||||||
\addplot [color=mycolor1, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H1, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_results.csv};
|
|
||||||
\addlegendentry{$H_1$};
|
|
||||||
|
|
||||||
|
|
||||||
\addplot [color=mycolor2, dashed, line width=1.5pt]
|
|
||||||
table [x=freqs, y=W2, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_weights.csv};
|
|
||||||
\addlegendentry{${W_2}^{-1}$};
|
|
||||||
\addplot [color=mycolor2, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H2, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_results.csv};
|
|
||||||
\addlegendentry{$H_2$};
|
|
||||||
|
|
||||||
\addplot [color=mycolor3, dashed, line width=1.5pt]
|
|
||||||
table [x=freqs, y=W3, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_weights.csv};
|
|
||||||
\addlegendentry{${W_3}^{-1}$};
|
|
||||||
\addplot [color=mycolor3, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H3, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_results.csv};
|
|
||||||
\addlegendentry{$H_3$};
|
|
||||||
\end{axis}
|
|
||||||
|
|
||||||
\begin{axis}[%
|
|
||||||
width=1.0\fwidth,
|
|
||||||
height=0.4\fheight,
|
|
||||||
at={(0.0\fwidth, 0.0\fheight)},
|
|
||||||
scale only axis,
|
|
||||||
xmode=log,
|
|
||||||
xmin=0.1,
|
|
||||||
xmax=100,
|
|
||||||
xminorticks=true,
|
|
||||||
xlabel={Frequency [Hz]},
|
|
||||||
ymin=-240,
|
|
||||||
ymax=240,
|
|
||||||
ytick={-180, -90, 0, 90, 180},
|
|
||||||
ylabel={Phase [deg]},
|
|
||||||
xminorgrids,
|
|
||||||
]
|
|
||||||
|
|
||||||
\addplot [color=mycolor1, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H1p, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_results.csv};
|
|
||||||
|
|
||||||
\addplot [color=mycolor2, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H2p, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_results.csv};
|
|
||||||
|
|
||||||
\addplot [color=mycolor3, line width=1.5pt]
|
|
||||||
table [x=freqs, y=H3p, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/mathinf_three_results.csv};
|
|
||||||
\end{axis}
|
|
||||||
\end{tikzpicture}
|
|
||||||
#+end_src
|
|
||||||
|
|
||||||
#+name: fig:hinf_three_synthesis_results
|
|
||||||
#+caption: Frequency response of the weighting functions and three complementary filters obtained using $\mathcal{H}_\infty$ synthesis ([[./figs/hinf_three_synthesis_results.png][png]], [[./figs/hinf_three_synthesis_results.pdf][pdf]], [[./figs/hinf_three_synthesis_results.tex][tex]]).
|
|
||||||
#+RESULTS:
|
|
||||||
[[file:figs/hinf_three_synthesis_results.png]]
|
|
||||||
|
|
||||||
* Specifications and weighting functions magnitude used for $\mathcal{H}_\infty$ synthesis
|
|
||||||
#+begin_src latex :file ligo_weights.pdf :tangle figs/ligo_weights.tex :exports both
|
|
||||||
\setlength\fwidth{6.5cm}
|
|
||||||
\setlength\fheight{3.2cm}
|
|
||||||
|
|
||||||
\begin{tikzpicture}
|
|
||||||
\begin{axis}[%
|
|
||||||
width=1.0\fwidth,
|
|
||||||
height=1.0\fheight,
|
|
||||||
at={(0.0\fwidth, 0.0\fheight)},
|
|
||||||
scale only axis,
|
|
||||||
separate axis lines,
|
|
||||||
every outer x axis line/.append style={black},
|
|
||||||
every x tick label/.append style={font=\color{black}},
|
|
||||||
every x tick/.append style={black},
|
|
||||||
xmode=log,
|
|
||||||
xmin=0.001,
|
|
||||||
xmax=1,
|
|
||||||
xminorticks=true,
|
|
||||||
xlabel={Frequency [Hz]},
|
|
||||||
every outer y axis line/.append style={black},
|
|
||||||
every y tick label/.append style={font=\color{black}},
|
|
||||||
every y tick/.append style={black},
|
|
||||||
ymode=log,
|
|
||||||
ymin=0.005,
|
|
||||||
ymax=20,
|
|
||||||
yminorticks=true,
|
|
||||||
ylabel={Magnitude},
|
|
||||||
axis background/.style={fill=white},
|
|
||||||
xmajorgrids,
|
|
||||||
xminorgrids,
|
|
||||||
ymajorgrids,
|
|
||||||
yminorgrids,
|
|
||||||
legend style={at={(0,1)}, outer sep=2pt, anchor=north west, legend cell align=left, align=left, draw=black, nodes={scale=0.7, transform shape}}
|
|
||||||
]
|
|
||||||
|
|
||||||
\addplot [color=mycolor1, line width=1.5pt]
|
|
||||||
table [x=freqs, y=wHm, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matligo_weights.csv};
|
|
||||||
\addlegendentry{$|w_H|^{-1}$}
|
|
||||||
|
|
||||||
\addplot [color=mycolor2, line width=1.5pt]
|
|
||||||
table [x=freqs, y=wLm, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matligo_weights.csv};
|
|
||||||
\addlegendentry{$|w_L|^{-1}$}
|
|
||||||
|
|
||||||
\addplot [color=black, dotted, line width=1.5pt]
|
|
||||||
table[row sep=crcr]{%
|
|
||||||
0.0005 0.008\\
|
|
||||||
0.008 0.008\\
|
|
||||||
};
|
|
||||||
\addlegendentry{Specifications}
|
|
||||||
|
|
||||||
\addplot [color=black, dotted, line width=1.5pt, forget plot]
|
|
||||||
table[row sep=crcr]{%
|
|
||||||
0.008 0.008\\
|
|
||||||
0.04 1\\
|
|
||||||
};
|
|
||||||
\addplot [color=black, dotted, line width=1.5pt, forget plot]
|
|
||||||
table[row sep=crcr]{%
|
|
||||||
0.04 3\\
|
|
||||||
0.1 3\\
|
|
||||||
};
|
|
||||||
\addplot [color=black, dotted, line width=1.5pt]
|
|
||||||
table[row sep=crcr]{%
|
|
||||||
0.1 0.045\\
|
|
||||||
2 0.045\\
|
|
||||||
};
|
|
||||||
\end{axis}
|
|
||||||
\end{tikzpicture}
|
|
||||||
#+end_src
|
|
||||||
|
|
||||||
#+name: fig:ligo_weights
|
|
||||||
#+caption: Specifications and weighting functions magnitude used for $\mathcal{H}_\infty$ synthesis ([[./figs/ligo_weights.png][png]], [[./figs/ligo_weights.pdf][pdf]], [[./figs/ligo_weights.tex][tex]]).
|
|
||||||
#+RESULTS:
|
|
||||||
[[file:figs/ligo_weights.png]]
|
|
||||||
|
|
||||||
* Comparison of the FIR filters (solid) with the filters obtained with $\mathcal{H}_\infty$ synthesis (dashed)
|
|
||||||
#+begin_src latex :file comp_fir_ligo_hinf.pdf :tangle figs/comp_fir_ligo_hinf.tex :exports both
|
|
||||||
\setlength\fwidth{6.5cm}
|
|
||||||
\setlength\fheight{6.8cm}
|
|
||||||
|
|
||||||
\begin{tikzpicture}
|
|
||||||
\begin{axis}[%
|
|
||||||
width=1.0\fwidth,
|
|
||||||
height=0.60\fheight,
|
|
||||||
at={(0.0\fwidth, 0.32\fheight)},
|
|
||||||
scale only axis,
|
|
||||||
xmode=log,
|
|
||||||
xmin=0.001,
|
|
||||||
xmax=1,
|
|
||||||
xtick={0.001,0.01,0.1,1},
|
|
||||||
xticklabels={{}},
|
|
||||||
xminorticks=true,
|
|
||||||
ymode=log,
|
|
||||||
ymin=0.002,
|
|
||||||
ymax=5,
|
|
||||||
ytick={0.001, 0.01, 0.1, 1, 10},
|
|
||||||
yminorticks=true,
|
|
||||||
ylabel={Magnitude},
|
|
||||||
xminorgrids,
|
|
||||||
yminorgrids,
|
|
||||||
legend style={at={(1,0)}, outer sep=2pt, anchor=south east, legend cell align=left, align=left, draw=black, nodes={scale=0.7, transform shape}}
|
|
||||||
]
|
|
||||||
\addplot [color=mycolor1, line width=1.5pt]
|
|
||||||
table [x=freqs, y=Hhm, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_hinf.csv};
|
|
||||||
\addlegendentry{$H_H(s)$ - $\mathcal{H}_\infty$}
|
|
||||||
\addplot [color=mycolor1, dashed, line width=1.5pt]
|
|
||||||
table [x=freqs, y=Hhm, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_fir.csv};
|
|
||||||
\addlegendentry{$H_H(s)$ - FIR}
|
|
||||||
\addplot [color=mycolor2, line width=1.5pt]
|
|
||||||
table [x=freqs, y=Hlm, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_hinf.csv};
|
|
||||||
\addlegendentry{$H_L(s)$ - $\mathcal{H}_\infty$}
|
|
||||||
\addplot [color=mycolor2, dashed, line width=1.5pt]
|
|
||||||
table [x=freqs, y=Hlm, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_fir.csv};
|
|
||||||
\addlegendentry{$H_L(s)$ - FIR}
|
|
||||||
\end{axis}
|
|
||||||
|
|
||||||
\begin{axis}[%
|
|
||||||
width=1.0\fwidth,
|
|
||||||
height=0.3\fheight,
|
|
||||||
at={(0.0\fwidth, 0.0\fheight)},
|
|
||||||
scale only axis,
|
|
||||||
xmode=log,
|
|
||||||
xmin=0.001,
|
|
||||||
xmax=1,
|
|
||||||
xtick={0.001, 0.01, 0.1, 1},
|
|
||||||
xminorticks=true,
|
|
||||||
xlabel={Frequency [Hz]},
|
|
||||||
ymin=-180,
|
|
||||||
ymax=180,
|
|
||||||
ytick={-180, -90, 0, 90, 180},
|
|
||||||
ylabel={Phase [deg]},
|
|
||||||
xminorgrids,
|
|
||||||
]
|
|
||||||
\addplot [color=mycolor1, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=Hhp, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_hinf.csv};
|
|
||||||
\addplot [color=mycolor1, dashed, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=Hhp, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_fir.csv};
|
|
||||||
\addplot [color=mycolor2, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=Hlp, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_hinf.csv};
|
|
||||||
\addplot [color=mycolor2, dashed, line width=1.5pt, forget plot]
|
|
||||||
table [x=freqs, y=Hlp, col sep=comma] {/home/thomas/Cloud/thesis/papers/dehaeze19_desig_compl_filte/matlab/matcomp_ligo_fir.csv};
|
|
||||||
\end{axis}
|
|
||||||
\end{tikzpicture}
|
|
||||||
#+end_src
|
|
||||||
|
|
||||||
#+name: fig:comp_fir_ligo_hinf
|
|
||||||
#+caption: Comparison of the FIR filters (solid) with the filters obtained with $\mathcal{H}_\infty$ synthesis (dashed) ([[./figs/comp_fir_ligo_hinf.png][png]], [[./figs/comp_fir_ligo_hinf.pdf][pdf]], [[./figs/comp_fir_ligo_hinf.tex][tex]]).
|
|
||||||
#+RESULTS:
|
|
||||||
[[file:figs/comp_fir_ligo_hinf.png]]
|
|
||||||
|