Add test about Hinf synthesis
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@ -42,11 +42,11 @@
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\bibliographystyle{IEEEtran}
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:END:
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* LaTeX Config :noexport:
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* LaTeX Config :noexport:
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#+begin_src latex :tangle config.tex
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#+end_src
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* Build :noexport:
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* Build :noexport:
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#+NAME: startblock
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#+BEGIN_SRC emacs-lisp :results none
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(add-to-list 'org-latex-classes
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@ -74,7 +74,7 @@
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(setq org-latex-with-hyperref nil)
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#+END_SRC
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* Abstract :ignore:
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* Abstract :ignore:
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#+begin_abstract
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Abstract text to be done
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#+end_abstract
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@ -97,17 +97,17 @@
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** Sensor Model
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Let's consider a sensor measuring a physical quantity $x$ (Figure [[fig:sensor_model]]).
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Let's consider a sensor measuring a physical quantity $x$ (Figure ref:fig:sensor_model_noise).
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The sensor has an internal dynamics which is here modelled with a Linear Time Invariant (LTI) system transfer function $G_i(s)$.
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The noise of sensor can be described by the Power Spectral Density (PSD) $\Phi_{n_i}(\omega)$.
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This is approximated by shaping a white noise with unitary PSD $\tilde{n}_i$ eqref:eq:unitary_sensor_noise_psd with a LTI transfer function $N_i(s)$:
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\begin{equation}
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\begin{aligned}
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\Phi_{n_i}(\omega) &= \left| N_i(j\omega) \right|^2 \Phi_{\tilde{n}_i}(\omega) \\
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&= \left| N_i(j\omega) \right|^2
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\end{aligned}
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\begin{aligned}
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\Phi_{n_i}(\omega) &= \left| N_i(j\omega) \right|^2 \Phi_{\tilde{n}_i}(\omega) \\
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&= \left| N_i(j\omega) \right|^2
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\end{aligned}
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\end{equation}
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#+name: eq:unitary_sensor_noise_psd
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@ -120,19 +120,19 @@ The output of the sensor $v_i$:
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v_i = \left( G_i \right) x + \left( G_i N_i \right) \tilde{n}_i
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\end{equation}
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In order to obtain an estimate $\hat{x}_i$ of $x$, a model $\hat{G}_i$ of the (true) sensor dynamics $G_i$ is inverted and applied at the output (Figure [[fig:sensor_model]]):
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In order to obtain an estimate $\hat{x}_i$ of $x$, a model $\hat{G}_i$ of the (true) sensor dynamics $G_i$ is inverted and applied at the output (Figure ref:fig:sensor_model_noise):
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\begin{equation}
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\hat{x}_i = \left( \hat{G}_i^{-1} G_i \right) x + \left( \hat{G}_i^{-1} G_i N_i \right) \tilde{n}_i
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\end{equation}
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#+name: fig:sensor_model
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#+name: fig:sensor_model_noise
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#+caption: Sensor Model
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#+attr_latex: :scale 1
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[[file:figs/sensor_model.pdf]]
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[[file:figs/sensor_model_noise.pdf]]
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** Sensor Fusion Architecture
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Let's now consider two sensors measuring the same physical quantity $x$ but with different dynamics $(G_1, G_2)$ and noise characteristics $(N_1, N_2)$ (Figure [[fig:sensor_fusion_noise_arch]]).
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Let's now consider two sensors measuring the same physical quantity $x$ but with different dynamics $(G_1, G_2)$ and noise characteristics $(N_1, N_2)$ (Figure ref:fig:sensor_fusion_noise_arch).
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The noise sources $\tilde{n}_1$ and $\tilde{n}_2$ are considered to be uncorrelated.
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@ -190,7 +190,7 @@ And the goal is the minimize the Root Mean Square (RMS) value of $n$:
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Thus, the goal is to design $H_1(s)$ and $H_2(s)$ such that $H_1(s) + H_2(s) = 1$ and such that $\sigma_n$ is minimized.
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This can be cast into an $\mathcal{H}_2$ synthesis problem by considering the following generalized plant (also represented in Figure [[fig:h_two_optimal_fusion]]):
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This can be cast into an $\mathcal{H}_2$ synthesis problem by considering the following generalized plant (also represented in Figure ref:fig:h_two_optimal_fusion):
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\begin{equation}
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\begin{pmatrix}
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z_1 \\ z_2 \\ v
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@ -230,28 +230,38 @@ We then have that the $\mathcal{H}_2$ synthesis applied on $P_{\mathcal{H}_2}$ g
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** Representation of Sensor Dynamical Uncertainty
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Suppose that the sensor dynamics $G_i(s)$ can be modelled by a nominal d
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\begin{equation}
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G_i(s) = \hat{G}_i(s) \left( 1 + w_i(s) \Delta_i(s) \right); \quad |\Delta_i(j\omega)| < 1 \forall \omega
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\end{equation}
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In Section ref:sec:optimal_fusion, the model $\hat{G}_i(s)$ of the sensor was considered to be perfect.
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In reality, there are always uncertainty (neglected dynamics) associated with the estimation of the sensor dynamics.
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The Uncertainty on the sensor dynamics $G_i(s)$ is here modelled by (input) multiplicative uncertainty:
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\begin{equation}
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G_i(s) = \hat{G}_i(s) \left( 1 + W_i(s) \Delta_i(s) \right); \quad |\Delta_i(j\omega)| < 1 \forall \omega
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\end{equation}
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where $\hat{G}_i(s)$ is the nominal model, $W_i$ a weight representing the size of the uncertainty at each frequency, and $\Delta_i$ is any complex perturbation such that $\left\| \Delta_i \right\|_\infty < 1$.
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The sensor can then be represented as shown in Figure ref:fig:sensor_model_uncertainty.
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#+name: fig:sensor_model_uncertainty
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#+caption: Sensor Model including Dynamical Uncertainty
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#+attr_latex: :scale 1
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[[file:figs/sensor_model_uncertainty.pdf]]
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** Sensor Fusion Architecture
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Let's consider the sensor fusion architecture shown in Figure ref:fig:sensor_fusion_arch_uncertainty where the dynamical uncertainties of both sensors are included.
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The super sensor estimate is then:
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\begin{equation}
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\begin{split}
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\hat{x} = \Big( {} & H_1 \hat{G}_1^{-1} \hat{G}_1 (1 + w_1 \Delta_1) \\
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+ & H_2 \hat{G}_2^{-1} \hat{G}_2 (1 + w_2 \Delta_2) \Big) x
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\end{split}
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\begin{aligned}
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\hat{x} &= \Big( H_1 \hat{G}_1^{-1} \hat{G}_1 (1 + W_1 \Delta_1) \\
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& \quad + H_2 \hat{G}_2^{-1} \hat{G}_2 (1 + W_2 \Delta_2) \Big) x \\
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&= \Big( H_1 (1 + W_1 \Delta_1) + H_2 (1 + W_2 \Delta_2) \Big) x
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\end{aligned}
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\end{equation}
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with $\Delta_i$ is any transfer function satisfying $\| \Delta_i \|_\infty < 1$.
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Suppose the model inversion is equal to the nominal model:
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As $H_1$ and $H_2$ are complementary filters, we finally have:
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\begin{equation}
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\hat{G}_i = G_i
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\end{equation}
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\begin{equation}
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\hat{x} = \left( 1 + H_1 w_1 \Delta_1 + H_2 w_2 \Delta_2 \right) x
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\hat{x} = \left( 1 + H_1 W_1 \Delta_1 + H_2 W_2 \Delta_2 \right) x, \quad \|\Delta_i\|_\infty<1
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\end{equation}
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#+name: fig:sensor_fusion_arch_uncertainty
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@ -261,16 +271,48 @@ Suppose the model inversion is equal to the nominal model:
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** Super Sensor Dynamical Uncertainty
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The uncertainty set of the transfer function from $\hat{x}$ to $x$ at frequency $\omega$ is bounded in the complex plane by a circle centered on 1 and with a radius equal to $|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|$.
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The uncertainty set of the transfer function from $\hat{x}$ to $x$ at frequency $\omega$ is bounded in the complex plane by a circle centered on 1 and 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.
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And we can see that the dynamical uncertainty of the super sensor is equal to the sum of the individual sensor uncertainties filtered out by the complementary filters.
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#+name: fig:uncertainty_set_super_sensor
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#+caption: Super Sensor model uncertainty displayed in the complex plane
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#+attr_latex: :scale 1
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[[file:figs/uncertainty_set_super_sensor.pdf]]
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** $\mathcal{H_\infty}$ Synthesis of Complementary Filters
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# Some comments on the weights
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At frequencies where $\left|W_i(j\omega)\right| > 1$ the uncertainty exceeds $100\%$ and sensor fusion is impossible.
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** $\mathcal{H_\infty}$ Synthesis of Complementary Filters
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In order for the fusion to be "robust", meaning no phase drop will be induced in the super sensor dynamics,
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The goal is to design two complementary filters $H_1(s)$ and $H_2(s)$ such that the super sensor noise uncertainty is kept reasonably small.
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This problem can be dealt with an $\mathcal{H}_\infty$ synthesis problem by considering the following generalized plant:
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\begin{equation}
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\begin{pmatrix}
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z_1 \\ z_2 \\ v
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\end{pmatrix} = \underbrace{\begin{bmatrix}
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W_1 & W_1 \\
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0 & W_2 \\
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1 & 0
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\end{bmatrix}}_{P_{\mathcal{H}_\infty}} \begin{pmatrix}
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w \\ u
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\end{pmatrix}
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\end{equation}
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Applying the $\mathcal{H}_\infty$ synthesis on $P_{\mathcal{H}_\infty}$ will generate a filter $H_2(s)$ such that the $\mathcal{H}_\infty$ norm from $w$ to $(z_1,z_2)$ is minimized:
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#+NAME: eq:Hinf_norm
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\begin{equation}
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\left\| \begin{matrix} z_1/w \\ z_2/w \end{matrix} \right\|_\infty = \left\| \begin{matrix} W_1 (1 - H_2) \\ W_2 H_2 \end{matrix} \right\|_\infty
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\end{equation}
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The $\mathcal{H}_\infty$ norm of Eq. eqref:eq:Hinf_norm is equals to $\sigma_n$ by defining $H_1(s)$ to be the complementary filter of $H_2(s)$:
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\begin{equation}
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H_1(s) = 1 - H_2(s)
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\end{equation}
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In order to minimize the super sensor dynamical uncertainty
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#+name: fig:h_infinity_robust_fusion
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#+caption: Generalized plant $P_{\mathcal{H}_\infty}$ used for the $\mathcal{H}_\infty$ synthesis of complementary filters
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@ -279,11 +321,19 @@ In order to minimize the super sensor dynamical uncertainty
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** Example
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# Comments on the choice of the weights => we cannot ask for less uncertainty than both sensors
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* Optimal and Robust Sensor Fusion: Mixed $\mathcal{H}_2/\mathcal{H}_\infty$ Synthesis
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<<sec:optimal_robust_fusion>>
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** Sensor Fusion Architecture
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#+name: fig:sensor_model_noise_uncertainty
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#+caption: Sensor Model including Noise and Dynamical Uncertainty
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#+attr_latex: :scale 1
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[[file:figs/sensor_model_noise_uncertainty.pdf]]
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#+name: fig:sensor_fusion_arch_full
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#+caption: Super Sensor Fusion with both sensor noise and sensor model uncertainty
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#+attr_latex: :scale 1
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paper/paper.pdf
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paper/paper.tex
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paper/paper.tex
@ -1,4 +1,4 @@
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% Created 2020-09-22 mar. 11:10
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% Created 2020-09-22 mar. 21:58
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% Intended LaTeX compiler: pdflatex
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\documentclass[conference]{IEEEtran}
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\usepackage[utf8]{inputenc}
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@ -50,32 +50,34 @@ Complementary Filters, Sensor Fusion, H-Infinity Synthesis
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\end{IEEEkeywords}
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\section{Introduction}
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\label{sec:orgfaa194e}
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\label{sec:orgc2fc7e2}
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\label{sec:introduction}
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Section \ref{sec:optimal_fusion}
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Section \ref{sec:robust_fusion}
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Section \ref{sec:optimal_robust_fusion}
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Section \ref{sec:experimental_validation}
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\begin{itemize}
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\item Section \ref{sec:optimal_fusion}
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\item Section \ref{sec:robust_fusion}
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\item Section \ref{sec:optimal_robust_fusion}
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\item Section \ref{sec:experimental_validation}
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\end{itemize}
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\section{Optimal Super Sensor Noise: \(\mathcal{H}_2\) Synthesis}
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\label{sec:org08f9f0e}
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\label{sec:org2031a7c}
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\label{sec:optimal_fusion}
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\subsection{Sensor Model}
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\label{sec:orgaa5ec56}
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\label{sec:org32da471}
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Let's consider a sensor measuring a physical quantity \(x\) (Figure \ref{fig:sensor_model}).
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Let's consider a sensor measuring a physical quantity \(x\) (Figure \ref{fig:sensor_model_noise}).
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The sensor has an internal dynamics which is here modelled with a Linear Time Invariant (LTI) system transfer function \(G_i(s)\).
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The noise of sensor can be described by the Power Spectral Density (PSD) \(\Phi_{n_i}(\omega)\).
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This is approximated by shaping a white noise with unitary PSD \(\tilde{n}_i\) \eqref{eq:unitary_sensor_noise_psd} with a LTI transfer function \(N_i(s)\):
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\begin{equation}
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\begin{aligned}
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\Phi_{n_i}(\omega) &= \left| N_i(j\omega) \right|^2 \Phi_{\tilde{n}_i}(\omega) \\
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&= \left| N_i(j\omega) \right|^2
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\end{aligned}
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\begin{aligned}
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\Phi_{n_i}(\omega) &= \left| N_i(j\omega) \right|^2 \Phi_{\tilde{n}_i}(\omega) \\
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&= \left| N_i(j\omega) \right|^2
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\end{aligned}
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\end{equation}
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\begin{equation}
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@ -88,19 +90,19 @@ The output of the sensor \(v_i\):
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v_i = \left( G_i \right) x + \left( G_i N_i \right) \tilde{n}_i
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\end{equation}
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In order to obtain an estimate \(\hat{x}_i\) of \(x\), a model \(\hat{G}_i\) of the (true) sensor dynamics \(G_i\) is inverted and applied at the output (Figure \ref{fig:sensor_model}):
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In order to obtain an estimate \(\hat{x}_i\) of \(x\), a model \(\hat{G}_i\) of the (true) sensor dynamics \(G_i\) is inverted and applied at the output (Figure \ref{fig:sensor_model_noise}):
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\begin{equation}
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\hat{x}_i = \left( \hat{G}_i^{-1} G_i \right) x + \left( \hat{G}_i^{-1} G_i N_i \right) \tilde{n}_i
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\end{equation}
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\begin{figure}[htbp]
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\centering
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\includegraphics[scale=1]{figs/sensor_model.pdf}
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\caption{\label{fig:sensor_model}Sensor Model}
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\includegraphics[scale=1]{figs/sensor_model_noise.pdf}
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\caption{\label{fig:sensor_model_noise}Sensor Model}
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\end{figure}
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\subsection{Sensor Fusion Architecture}
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\label{sec:org17e7387}
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\label{sec:orgf3af62a}
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Let's now consider two sensors measuring the same physical quantity \(x\) but with different dynamics \((G_1, G_2)\) and noise characteristics \((N_1, N_2)\) (Figure \ref{fig:sensor_fusion_noise_arch}).
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@ -138,7 +140,7 @@ In such case, the super sensor estimate \(\hat{x}\) is equal to \(x\) plus the n
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\end{equation}
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\subsection{Super Sensor Noise}
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\label{sec:orgb010f68}
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\label{sec:orga39f54c}
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Let's note \(n\) the super sensor noise.
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\begin{equation}
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n = \left( H_1 N_1 \right) \tilde{n}_1 + \left( H_2 N_2 \right) \tilde{n}_2
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@ -152,31 +154,42 @@ As the noise of both sensors are considered to be uncorrelated, the PSD of the s
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It is clear that the PSD of the super sensor depends on the norm of the complementary filters.
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\subsection{\(\mathcal{H}_2\) Synthesis of Complementary Filters}
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\label{sec:orgf1d735c}
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The goal is to design \(H_1(s)\) and \(H_2(s)\) such that the effect of the noise sources \(\tilde{n}_1\) and \(\tilde{n}_2\) has the smallest possible effect on the noise \(n\) of the estimation \(\hat{x}\).
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\label{sec:org536193f}
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The goal is to design \(H_1(s)\) and \(H_2(s)\) such that the effect of the noise sources \(\tilde{n}_1\) and \(\tilde{n}_2\) has the smallest possible effect on the noise \(n\) of the estimation \(\hat{x}\).
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And the goal is the minimize the Root Mean Square (RMS) value of \(n\):
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\begin{equation}
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\label{eq:rms_value_estimation}
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\sigma_{n} = \sqrt{\int_0^\infty \Phi_{\hat{n}}(\omega) d\omega} = \left\| \begin{matrix} H_1 N_1 \\ H_2 N_2 \end{matrix} \right\|_2
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\sigma_{n} = \sqrt{\int_0^\infty \Phi_{n}(\omega) d\omega} = \left\| \begin{matrix} H_1 N_1 \\ H_2 N_2 \end{matrix} \right\|_2
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\end{equation}
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Thus, the goal is to design \(H_1(s)\) and \(H_2(s)\) such that \(H_1(s) + H_2(s) = 1\) and such that \(\left\| \begin{matrix} H_1 N_1 \\ H_2 N_2 \end{matrix} \right\|_2\) is minimized.
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Thus, the goal is to design \(H_1(s)\) and \(H_2(s)\) such that \(H_1(s) + H_2(s) = 1\) and such that \(\sigma_n\) is minimized.
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This can be cast into an \(\mathcal{H}_2\) synthesis problem by considering the following generalized plant (also represented in Figure \ref{fig:h_two_optimal_fusion}):
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\begin{equation}
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\begin{pmatrix}
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z_1 \\ z_2 \\ v
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\end{pmatrix} = \begin{bmatrix}
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\end{pmatrix} = \underbrace{\begin{bmatrix}
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N_1 & N_1 \\
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0 & N_2 \\
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1 & 0
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\end{bmatrix} \begin{pmatrix}
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\end{bmatrix}}_{P_{\mathcal{H}_2}} \begin{pmatrix}
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w \\ u
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\end{pmatrix}
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\end{equation}
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The \(\mathcal{H}_2\) synthesis of the complementary filters thus minimized the RMS value of the super sensor noise.
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Applying the \(\mathcal{H}_2\) synthesis on \(P_{\mathcal{H}_2}\) will generate a filter \(H_2(s)\) such that the \(\mathcal{H}_2\) norm from \(w\) to \((z_1,z_2)\) is minimized:
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\begin{equation}
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\label{eq:H2_norm}
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\left\| \begin{matrix} z_1/w \\ z_2/w \end{matrix} \right\|_2 = \left\| \begin{matrix} N_1 (1 - H_2) \\ N_2 H_2 \end{matrix} \right\|_2
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\end{equation}
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The \(\mathcal{H}_2\) norm of Eq. \eqref{eq:H2_norm} is equals to \(\sigma_n\) by defining \(H_1(s)\) to be the complementary filter of \(H_2(s)\):
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\begin{equation}
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H_1(s) = 1 - H_2(s)
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\end{equation}
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We then have that the \(\mathcal{H}_2\) synthesis applied on \(P_{\mathcal{H}_2}\) generates two complementary filters \(H_1(s)\) and \(H_2(s)\) such that the RMS value of super sensor noise is minimized.
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\begin{figure}[htbp]
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\centering
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@ -185,41 +198,52 @@ The \(\mathcal{H}_2\) synthesis of the complementary filters thus minimized the
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\end{figure}
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\subsection{Example}
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\label{sec:org074433c}
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\label{sec:orgd689dc3}
|
||||
|
||||
\subsection{Robustness Problem}
|
||||
\label{sec:org21dc09f}
|
||||
\label{sec:orgc57d2ad}
|
||||
|
||||
\section{Robust Sensor Fusion: \(\mathcal{H}_\infty\) Synthesis}
|
||||
\label{sec:org2041184}
|
||||
\label{sec:orgeed5209}
|
||||
\label{sec:robust_fusion}
|
||||
|
||||
\subsection{Representation of Sensor Dynamical Uncertainty}
|
||||
\label{sec:orgfd12a50}
|
||||
\label{sec:org7f4d435}
|
||||
|
||||
Suppose that the sensor dynamics \(G_i(s)\) can be modelled by a nominal d
|
||||
In Section \ref{sec:optimal_fusion}, the model \(\hat{G}_i(s)\) of the sensor was considered to be perfect.
|
||||
In reality, there are always uncertainty (neglected dynamics) associated with the estimation of the sensor dynamics.
|
||||
|
||||
The Uncertainty on the sensor dynamics \(G_i(s)\) is here modelled by (input) multiplicative uncertainty:
|
||||
\begin{equation}
|
||||
G_i(s) = \hat{G}_i(s) \left( 1 + w_i(s) \Delta_i(s) \right); \quad |\Delta_i(j\omega)| < 1 \forall \omega
|
||||
G_i(s) = \hat{G}_i(s) \left( 1 + W_i(s) \Delta_i(s) \right); \quad |\Delta_i(j\omega)| < 1 \forall \omega
|
||||
\end{equation}
|
||||
where \(\hat{G}_i(s)\) is the nominal model, \(W_i\) a weight representing the size of the uncertainty at each frequency, and \(\Delta_i\) is any complex perturbation such that \(\left\| \Delta_i \right\|_\infty < 1\).
|
||||
|
||||
The sensor can then be represented as shown in Figure \ref{fig:sensor_model_uncertainty}.
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[scale=1]{figs/sensor_model_uncertainty.pdf}
|
||||
\caption{\label{fig:sensor_model_uncertainty}Sensor Model including Dynamical Uncertainty}
|
||||
\end{figure}
|
||||
|
||||
\subsection{Sensor Fusion Architecture}
|
||||
\label{sec:org11c9d00}
|
||||
\label{sec:orgd4a5727}
|
||||
Let's consider the sensor fusion architecture shown in Figure \ref{fig:sensor_fusion_arch_uncertainty} where the dynamical uncertainties of both sensors are included.
|
||||
|
||||
The super sensor estimate is then:
|
||||
\begin{equation}
|
||||
\begin{split}
|
||||
\hat{x} = \Big( {} & H_1 \hat{G}_1^{-1} \hat{G}_1 (1 + w_1 \Delta_1) \\
|
||||
+ & H_2 \hat{G}_2^{-1} \hat{G}_2 (1 + w_2 \Delta_2) \Big) x
|
||||
\end{split}
|
||||
\begin{aligned}
|
||||
\hat{x} &= \Big( H_1 \hat{G}_1^{-1} \hat{G}_1 (1 + W_1 \Delta_1) \\
|
||||
& \quad + H_2 \hat{G}_2^{-1} \hat{G}_2 (1 + W_2 \Delta_2) \Big) x \\
|
||||
&= \Big( H_1 (1 + W_1 \Delta_1) + H_2 (1 + W_2 \Delta_2) \Big) x
|
||||
\end{aligned}
|
||||
\end{equation}
|
||||
with \(\Delta_i\) is any transfer function satisfying \(\| \Delta_i \|_\infty < 1\).
|
||||
|
||||
Suppose the model inversion is equal to the nominal model:
|
||||
As \(H_1\) and \(H_2\) are complementary filters, we finally have:
|
||||
\begin{equation}
|
||||
\hat{G}_i = G_i
|
||||
\end{equation}
|
||||
|
||||
\begin{equation}
|
||||
\hat{x} = \left( 1 + H_1 w_1 \Delta_1 + H_2 w_2 \Delta_2 \right) x
|
||||
\hat{x} = \left( 1 + H_1 W_1 \Delta_1 + H_2 W_2 \Delta_2 \right) x, \quad \|\Delta_i\|_\infty<1
|
||||
\end{equation}
|
||||
|
||||
\begin{figure}[htbp]
|
||||
@ -229,9 +253,12 @@ Suppose the model inversion is equal to the nominal model:
|
||||
\end{figure}
|
||||
|
||||
\subsection{Super Sensor Dynamical Uncertainty}
|
||||
\label{sec:org6673a25}
|
||||
\label{sec:org7eede13}
|
||||
|
||||
The uncertainty set of the transfer function from \(\hat{x}\) to \(x\) at frequency \(\omega\) is bounded in the complex plane by a circle centered on 1 and with a radius equal to \(|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|\).
|
||||
The uncertainty set of the transfer function from \(\hat{x}\) to \(x\) at frequency \(\omega\) is bounded in the complex plane by a circle centered on 1 and 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}.
|
||||
|
||||
|
||||
And we can see that the dynamical uncertainty of the super sensor is equal to the sum of the individual sensor uncertainties filtered out by the complementary filters.
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
@ -239,10 +266,38 @@ The uncertainty set of the transfer function from \(\hat{x}\) to \(x\) at freque
|
||||
\caption{\label{fig:uncertainty_set_super_sensor}Super Sensor model uncertainty displayed in the complex plane}
|
||||
\end{figure}
|
||||
|
||||
\subsection{\(\mathcal{H_\infty}\) Synthesis of Complementary Filters}
|
||||
\label{sec:org41ccb1e}
|
||||
At frequencies where \(\left|W_i(j\omega)\right| > 1\) the uncertainty exceeds \(100\%\) and sensor fusion is impossible.
|
||||
|
||||
\subsection{\(\mathcal{H_\infty}\) Synthesis of Complementary Filters}
|
||||
\label{sec:org0b02610}
|
||||
In order for the fusion to be ``robust'', meaning no phase drop will be induced in the super sensor dynamics,
|
||||
|
||||
The goal is to design two complementary filters \(H_1(s)\) and \(H_2(s)\) such that the super sensor noise uncertainty is kept reasonably small.
|
||||
|
||||
This problem can be dealt with an \(\mathcal{H}_\infty\) synthesis problem by considering the following generalized plant:
|
||||
\begin{equation}
|
||||
\begin{pmatrix}
|
||||
z_1 \\ z_2 \\ v
|
||||
\end{pmatrix} = \underbrace{\begin{bmatrix}
|
||||
W_1 & W_1 \\
|
||||
0 & W_2 \\
|
||||
1 & 0
|
||||
\end{bmatrix}}_{P_{\mathcal{H}_\infty}} \begin{pmatrix}
|
||||
w \\ u
|
||||
\end{pmatrix}
|
||||
\end{equation}
|
||||
|
||||
Applying the \(\mathcal{H}_\infty\) synthesis on \(P_{\mathcal{H}_\infty}\) will generate a filter \(H_2(s)\) such that the \(\mathcal{H}_\infty\) norm from \(w\) to \((z_1,z_2)\) is minimized:
|
||||
\begin{equation}
|
||||
\label{eq:Hinf_norm}
|
||||
\left\| \begin{matrix} z_1/w \\ z_2/w \end{matrix} \right\|_\infty = \left\| \begin{matrix} W_1 (1 - H_2) \\ W_2 H_2 \end{matrix} \right\|_\infty
|
||||
\end{equation}
|
||||
|
||||
The \(\mathcal{H}_\infty\) norm of Eq. \eqref{eq:Hinf_norm} is equals to \(\sigma_n\) by defining \(H_1(s)\) to be the complementary filter of \(H_2(s)\):
|
||||
\begin{equation}
|
||||
H_1(s) = 1 - H_2(s)
|
||||
\end{equation}
|
||||
|
||||
In order to minimize the super sensor dynamical uncertainty
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
@ -251,14 +306,22 @@ In order to minimize the super sensor dynamical uncertainty
|
||||
\end{figure}
|
||||
|
||||
\subsection{Example}
|
||||
\label{sec:orgba594da}
|
||||
\label{sec:orgfe98b6f}
|
||||
|
||||
|
||||
\section{Optimal and Robust Sensor Fusion: Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
|
||||
\label{sec:orgc07eeab}
|
||||
\label{sec:org9114fff}
|
||||
\label{sec:optimal_robust_fusion}
|
||||
|
||||
\subsection{Sensor Fusion Architecture}
|
||||
\label{sec:orgddd6d33}
|
||||
\label{sec:org7816cc1}
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[scale=1]{figs/sensor_model_noise_uncertainty.pdf}
|
||||
\caption{\label{fig:sensor_model_noise_uncertainty}Sensor Model including Noise and Dynamical Uncertainty}
|
||||
\end{figure}
|
||||
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
@ -267,10 +330,10 @@ In order to minimize the super sensor dynamical uncertainty
|
||||
\end{figure}
|
||||
|
||||
\subsection{Synthesis Objective}
|
||||
\label{sec:org79824da}
|
||||
\label{sec:org39451fc}
|
||||
|
||||
\subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
|
||||
\label{sec:org247ac1c}
|
||||
\label{sec:orga8ff805}
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
@ -279,30 +342,30 @@ In order to minimize the super sensor dynamical uncertainty
|
||||
\end{figure}
|
||||
|
||||
\subsection{Example}
|
||||
\label{sec:org7af2158}
|
||||
\label{sec:orga353d87}
|
||||
|
||||
\section{Experimental Validation}
|
||||
\label{sec:orgb54c59b}
|
||||
\label{sec:orgb00dce4}
|
||||
\label{sec:experimental_validation}
|
||||
|
||||
\subsection{Experimental Setup}
|
||||
\label{sec:org40eadad}
|
||||
\label{sec:orgc725d26}
|
||||
|
||||
\subsection{Sensor Noise and Dynamical Uncertainty}
|
||||
\label{sec:orgb81743f}
|
||||
\label{sec:org0b05001}
|
||||
|
||||
\subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
|
||||
\label{sec:orgb2232ac}
|
||||
\label{sec:org9c0559a}
|
||||
|
||||
\subsection{Super Sensor Noise and Dynamical Uncertainty}
|
||||
\label{sec:orgd80a558}
|
||||
\label{sec:orgc629276}
|
||||
|
||||
\section{Conclusion}
|
||||
\label{sec:org0da5eb6}
|
||||
\label{sec:orgdd3a6b6}
|
||||
\label{sec:conclusion}
|
||||
|
||||
\section{Acknowledgment}
|
||||
\label{sec:orge5b9b80}
|
||||
\label{sec:orge958f77}
|
||||
|
||||
\bibliography{ref}
|
||||
\end{document}
|
||||
|
Loading…
Reference in New Issue
Block a user