Update robustness analysis. Remove zip, add link directly to matlab
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matlab/figs/magnitude_wphi.pdf
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matlab/figs/magnitude_wphi.pdf
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matlab/figs/magnitude_wphi.png
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matlab/figs/magnitude_wphi.png
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@ -645,17 +645,8 @@ To do so, we model the uncertainty that we have on the sensor dynamics by multip
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The objective here is to design complementary filters $H_1(s)$ and $H_2(s)$ in order to minimize the dynamical uncertainty of the super sensor.
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** ZIP file containing the data and matlab files :ignore:
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#+begin_src bash :exports none :results none
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if [ matlab/comp_filter_robustness.m -nt data/comp_filter_robustness.zip ]; then
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cp matlab/comp_filter_robustness.m comp_filter_robustness.m;
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zip data/comp_filter_robustness \
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comp_filter_robustness.m
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rm comp_filter_robustness.m;
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fi
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#+end_src
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#+begin_note
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All the files (data and Matlab scripts) are accessible [[file:data/comp_filter_robustness.zip][here]].
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All the files (data and Matlab scripts) are accessible [[file:matlab/comp_filter_robustness.m][here]].
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#+end_note
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** Matlab Init :noexport:ignore:
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@ -667,10 +658,6 @@ The objective here is to design complementary filters $H_1(s)$ and $H_2(s)$ in o
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<<matlab-init>>
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#+end_src
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#+begin_src matlab
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freqs = logspace(-1, 3, 1000);
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#+end_src
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** Unknown sensor dynamics dynamics
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In practical systems, the sensor dynamics has always some level of uncertainty.
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Let's represent that with multiplicative input uncertainty as shown on figure [[fig:sensor_fusion_dynamic_uncertainty]].
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@ -746,6 +733,10 @@ Let's consider two ideal sensors except one sensor has not an expected unity gai
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Two pairs of complementary filters are designed and shown on figure [[fig:comp_filters_robustness_test]].
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The complementary filters shown in blue does not present a bump as the red ones but provides less sensor separation at high and low frequencies.
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#+begin_src matlab :exports none
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freqs = logspace(-1, 1, 1000);
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#+end_src
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#+begin_src matlab :exports none
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w0 = 2*pi;
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alpha = 2;
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@ -761,8 +752,6 @@ The complementary filters shown in blue does not present a bump as the red ones
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#+end_src
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#+begin_src matlab :exports none
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freqs = logspace(-1, 1, 1000);
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figure;
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% Magnitude
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ax1 = subaxis(2,1,1);
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@ -805,8 +794,6 @@ We then compute the bode plot of the super sensor transfer function $H_1(s)G_1(s
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We see that the blue complementary filters with a lower maximum norm permits to limit the phase lag introduced by the gain mismatch.
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#+begin_src matlab :exports none
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freqs = logspace(-1, 1, 1000);
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figure;
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% Magnitude
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ax1 = subaxis(2,1,1);
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@ -848,6 +835,11 @@ We want to merge two sensors:
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*** Dynamical uncertainty of the individual sensors
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We define the weights that are used to characterize the dynamic uncertainty of the sensors.
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#+begin_src matlab :exports none
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freqs = logspace(-1, 3, 1000);
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#+end_src
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#+begin_src matlab
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omegac = 100*2*pi; G0 = 0.1; Ginf = 10;
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w1 = (Ginf*s/omegac + G0)/(s/omegac + 1);
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@ -963,7 +955,7 @@ Then:
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\Longleftrightarrow & \left| w_1(j\omega) H_1(j\omega) w_\phi(j\omega) \right| + \left| w_2(j\omega) H_2(j\omega) w_\phi(j\omega) \right| < 1, \quad \forall\omega
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\end{align*}
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Which is approximately equivalent to (with an approximation of maximum $\sqrt{2}$):
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Which is approximately equivalent to (with an error of maximum $\sqrt{2}$):
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#+name: eq:hinf_conf_phase_uncertainty
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\begin{equation}
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\left\| \begin{matrix} w_1(s) w_\phi(s) H_1(s) \\ w_2(s) w_\phi(s) H_2(s) \end{matrix} \right\|_\infty < 1
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@ -973,7 +965,7 @@ On should not forget that at frequency where both sensors has unknown dynamics (
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Thus, at these frequencies, $|w_\phi|$ should be smaller than $1$.
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*** H-Infinity Synthesis
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Let's define $w_\phi(s)$ such that the maximum allowed phase uncertainty $\Delta\phi_\text{max}$ of the super sensor dynamics is $30\text{ deg}$ until frequency $\omega_0 = 500\text{ Hz}$
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Let's define $w_\phi(s)$ in order to bound the maximum allowed phase uncertainty $\Delta\phi_\text{max}$ of the super sensor dynamics.
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#+begin_src matlab
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Dphi = 20; % [deg]
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@ -985,6 +977,26 @@ Let's define $w_\phi(s)$ such that the maximum allowed phase uncertainty $\Delta
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W2 = w2*wphi;
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#+end_src
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#+begin_src matlab :exports none
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figure;
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hold on;
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plot(freqs, abs(squeeze(freqresp(wphi, freqs, 'Hz'))), '-', 'DisplayName', '$w_\phi(s)$');
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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xlabel('Frequency [Hz]'); ylabel('Magnitude');
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hold off;
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xlim([freqs(1), freqs(end)]);
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legend('location', 'northeast');
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#+end_src
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#+HEADER: :tangle no :exports results :results none :noweb yes
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#+begin_src matlab :var filepath="figs/magnitude_wphi.pdf" :var figsize="full-normal" :post pdf2svg(file=*this*, ext="png")
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<<plt-matlab>>
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#+end_src
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#+NAME: fig:magnitude_wphi
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#+CAPTION: Magnitude of the weght $w_\phi(s)$ that is used to bound the uncertainty of the super sensor ([[./figs/magnitude_wphi.png][png]], [[./figs/magnitude_wphi.pdf][pdf]])
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[[file:figs/magnitude_wphi.png]]
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#+begin_src matlab :exports none
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% We here compute the wanted maximum and minimum phase of the super sensor
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Dphimax = 180/pi*asin(1./abs(squeeze(freqresp(wphi, freqs, 'Hz'))));
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@ -1028,7 +1040,7 @@ The obtained upper bounds on the complementary filters in order to limit the pha
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#+end_src
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#+HEADER: :tangle no :exports results :results none :noweb yes
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#+begin_src matlab :var filepath="figs/upper_bounds_comp_filter_max_phase_uncertainty.pdf" :var figsize="full-tall" :post pdf2svg(file=*this*, ext="png")
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#+begin_src matlab :var filepath="figs/upper_bounds_comp_filter_max_phase_uncertainty.pdf" :var figsize="full-normal" :post pdf2svg(file=*this*, ext="png")
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<<plt-matlab>>
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#+end_src
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378
matlab/matlab/comp_filter_robustness.m
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378
matlab/matlab/comp_filter_robustness.m
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@ -0,0 +1,378 @@
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%% Clear Workspace and Close figures
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clear; close all; clc;
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%% Intialize Laplace variable
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s = zpk('s');
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% First Basic Example with gain mismatch
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% Let's consider two ideal sensors except one sensor has not an expected unity gain but a gain equal to $0.6$:
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% \begin{align*}
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% G_1(s) &= 1 \\
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% G_2(s) &= 0.6
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% \end{align*}
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G1 = 1;
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G2 = 0.6;
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% Two pairs of complementary filters are designed and shown on figure [[fig:comp_filters_robustness_test]].
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% The complementary filters shown in blue does not present a bump as the red ones but provides less sensor separation at high and low frequencies.
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freqs = logspace(-1, 1, 1000);
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w0 = 2*pi;
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alpha = 2;
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H1a = ((1+alpha)*(s/w0)+1)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
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H2a = (s/w0)^2*((s/w0)+1+alpha)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
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w0 = 2*pi;
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alpha = 0.1;
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H1b = ((1+alpha)*(s/w0)+1)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
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H2b = (s/w0)^2*((s/w0)+1+alpha)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
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figure;
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% Magnitude
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ax1 = subaxis(2,1,1);
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hold on;
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set(gca,'ColorOrderIndex',1); plot(freqs, abs(squeeze(freqresp(H1a, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',1); plot(freqs, abs(squeeze(freqresp(H2a, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',2); plot(freqs, abs(squeeze(freqresp(H1b, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',2); plot(freqs, abs(squeeze(freqresp(H2b, freqs, 'Hz'))));
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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set(gca, 'XTickLabel',[]);
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ylabel('Magnitude');
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hold off;
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% Phase
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ax2 = subaxis(2,1,2);
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hold on;
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set(gca,'ColorOrderIndex',1); plot(freqs, 180/pi*angle(squeeze(freqresp(H1a, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',1); plot(freqs, 180/pi*angle(squeeze(freqresp(H2a, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',2); plot(freqs, 180/pi*angle(squeeze(freqresp(H1b, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',2); plot(freqs, 180/pi*angle(squeeze(freqresp(H2b, freqs, 'Hz'))));
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set(gca,'xscale','log');
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yticks(-180:90:180);
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ylim([-180 180]);
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xlabel('Relative Frequency $\frac{\omega}{\omega_0}$'); ylabel('Phase [deg]');
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hold off;
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linkaxes([ax1,ax2],'x');
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xlim([freqs(1), freqs(end)]);
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% #+NAME: fig:comp_filters_robustness_test
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% #+CAPTION: The two complementary filters designed for the robustness test ([[./figs/comp_filters_robustness_test.png][png]], [[./figs/comp_filters_robustness_test.pdf][pdf]])
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% [[file:figs/comp_filters_robustness_test.png]]
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% We then compute the bode plot of the super sensor transfer function $H_1(s)G_1(s) + H_2(s)G_2(s)$ for both complementary filters pair (figure [[fig:tf_super_sensor_comp]]).
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% We see that the blue complementary filters with a lower maximum norm permits to limit the phase lag introduced by the gain mismatch.
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figure;
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% Magnitude
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ax1 = subaxis(2,1,1);
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hold on;
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set(gca,'ColorOrderIndex',1); plot(freqs, abs(squeeze(freqresp(H1a*G1 + H2a*G2, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',2); plot(freqs, abs(squeeze(freqresp(H1b*G1 + H2b*G2, freqs, 'Hz'))));
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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set(gca, 'XTickLabel',[]);
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ylabel('Magnitude');
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ylim([1e-1, 1e1]);
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hold off;
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% Phase
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ax2 = subaxis(2,1,2);
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hold on;
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set(gca,'ColorOrderIndex',1); plot(freqs, 180/pi*angle(squeeze(freqresp(H1a*G1 + H2a*G2, freqs, 'Hz'))));
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set(gca,'ColorOrderIndex',2); plot(freqs, 180/pi*angle(squeeze(freqresp(H1b*G1 + H2b*G2, freqs, 'Hz'))));
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set(gca,'xscale','log');
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yticks(-180:90:180);
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ylim([-180 180]);
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xlabel('Relative Frequency $\frac{\omega}{\omega_0}$'); ylabel('Phase [deg]');
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hold off;
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linkaxes([ax1,ax2],'x');
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xlim([freqs(1), freqs(end)]);
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% Dynamical uncertainty of the individual sensors
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% We define the weights that are used to characterize the dynamic uncertainty of the sensors.
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freqs = logspace(-1, 3, 1000);
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omegac = 100*2*pi; G0 = 0.1; Ginf = 10;
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w1 = (Ginf*s/omegac + G0)/(s/omegac + 1);
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omegac = 0.2*2*pi; G0 = 5; Ginf = 0.1;
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w2 = (Ginf*s/omegac + G0)/(s/omegac + 1);
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omegac = 5000*2*pi; G0 = 1; Ginf = 50;
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w2 = w2*(Ginf*s/omegac + G0)/(s/omegac + 1);
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% From the weights, we define the uncertain transfer functions of the sensors. Some of the uncertain dynamics of both sensors are shown on Fig. [[fig:uncertainty_dynamics_sensors]] with the upper and lower bounds on the magnitude and on the phase.
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G1 = 1 + w1*ultidyn('Delta',[1 1]);
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G2 = 1 + w2*ultidyn('Delta',[1 1]);
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% Few random samples of the sensor dynamics are computed
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G1s = usample(G1, 10);
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G2s = usample(G2, 10);
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% We here compute the maximum and minimum phase of both sensors
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Dphi1 = 180/pi*asin(abs(squeeze(freqresp(w1, freqs, 'Hz'))));
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Dphi2 = 180/pi*asin(abs(squeeze(freqresp(w2, freqs, 'Hz'))));
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Dphi1(abs(squeeze(freqresp(w1, freqs, 'Hz'))) > 1) = 190;
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Dphi2(abs(squeeze(freqresp(w2, freqs, 'Hz'))) > 1) = 190;
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figure;
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% Magnitude
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ax1 = subaxis(2,1,1);
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hold on;
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set(gca,'ColorOrderIndex',1);
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plot(freqs, 1 + abs(squeeze(freqresp(w1, freqs, 'Hz'))), '--');
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set(gca,'ColorOrderIndex',1);
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plot(freqs, max(1 - abs(squeeze(freqresp(w1, freqs, 'Hz'))), 0), '--');
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set(gca,'ColorOrderIndex',2);
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plot(freqs, 1 + abs(squeeze(freqresp(w2, freqs, 'Hz'))), '--');
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set(gca,'ColorOrderIndex',2);
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plot(freqs, max(1 - abs(squeeze(freqresp(w2, freqs, 'Hz'))), 0), '--');
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for i = 1:length(G1s)
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plot(freqs, abs(squeeze(freqresp(G1s(:, :, i, 1), freqs, 'Hz'))), '-', 'color', [0 0.4470 0.7410 0.4]);
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plot(freqs, abs(squeeze(freqresp(G2s(:, :, i, 1), freqs, 'Hz'))), '-', 'color', [0.8500 0.3250 0.0980 0.4]);
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end
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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set(gca, 'XTickLabel',[]);
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ylabel('Magnitude');
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ylim([1e-1, 10]);
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hold off;
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% Phase
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ax2 = subaxis(2,1,2);
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hold on;
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set(gca,'ColorOrderIndex',1);
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plot(freqs, Dphi1, '--');
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set(gca,'ColorOrderIndex',1);
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plot(freqs, -Dphi1, '--');
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set(gca,'ColorOrderIndex',2);
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plot(freqs, Dphi2, '--');
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set(gca,'ColorOrderIndex',2);
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plot(freqs, -Dphi2, '--');
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for i = 1:length(G1s)
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plot(freqs, 180/pi*angle(squeeze(freqresp(G1s(:, :, i, 1), freqs, 'Hz'))), '-', 'color', [0 0.4470 0.7410 0.4]);
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plot(freqs, 180/pi*angle(squeeze(freqresp(G2s(:, :, i, 1), freqs, 'Hz'))), '-', 'color', [0.8500 0.3250 0.0980 0.4]);
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end
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set(gca,'xscale','log');
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yticks(-180:90:180);
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ylim([-180 180]);
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xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
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hold off;
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linkaxes([ax1,ax2],'x');
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% H-Infinity Synthesis
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% Let's define $w_\phi(s)$ in order to bound the maximum allowed phase uncertainty $\Delta\phi_\text{max}$ of the super sensor dynamics.
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Dphi = 20; % [deg]
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n = 4; w0 = 2*pi*900; G0 = 1/sin(Dphi*pi/180); Ginf = 1/100; Gc = 1;
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wphi = (((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/Ginf)^(2/n)))*s + (G0/Gc)^(1/n))/((1/Ginf)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/Ginf)^(2/n)))*s + (1/Gc)^(1/n)))^n;
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W1 = w1*wphi;
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W2 = w2*wphi;
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figure;
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hold on;
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plot(freqs, abs(squeeze(freqresp(wphi, freqs, 'Hz'))), '-', 'DisplayName', '$w_\phi(s)$');
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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xlabel('Frequency [Hz]'); ylabel('Magnitude');
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hold off;
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xlim([freqs(1), freqs(end)]);
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legend('location', 'northeast');
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% #+NAME: fig:magnitude_wphi
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% #+CAPTION: Magnitude of the weght $w_\phi(s)$ that is used to bound the uncertainty of the super sensor ([[./figs/magnitude_wphi.png][png]], [[./figs/magnitude_wphi.pdf][pdf]])
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% [[file:figs/magnitude_wphi.png]]
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% We here compute the wanted maximum and minimum phase of the super sensor
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Dphimax = 180/pi*asin(1./abs(squeeze(freqresp(wphi, freqs, 'Hz'))));
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Dphimax(1./abs(squeeze(freqresp(wphi, freqs, 'Hz'))) > 1) = 190;
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figure;
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hold on;
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plot(freqs, Dphimax, 'k--');
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plot(freqs, -Dphimax, 'k--');
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set(gca, 'XScale', 'log');
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xlabel('Frequency [Hz]'); ylabel('Magnitude');
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hold off;
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xlim([freqs(1), freqs(end)]);
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ylim([-180 180]);
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yticks(-180:45:180);
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% #+NAME: fig:maximum_wanted_phase_uncertainty
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% #+CAPTION: Maximum wanted phase uncertainty using this weight ([[./figs/maximum_wanted_phase_uncertainty.png][png]], [[./figs/maximum_wanted_phase_uncertainty.pdf][pdf]])
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% [[file:figs/maximum_wanted_phase_uncertainty.png]]
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% The obtained upper bounds on the complementary filters in order to limit the phase uncertainty of the super sensor are represented in Fig. [[fig:upper_bounds_comp_filter_max_phase_uncertainty]].
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||||
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W1, freqs, 'Hz'))), '-', 'DisplayName', '$1/|w_1w_\phi|$');
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W2, freqs, 'Hz'))), '-', 'DisplayName', '$1/|w_2w_\phi|$');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:upper_bounds_comp_filter_max_phase_uncertainty
|
||||
% #+CAPTION: Upper bounds on the complementary filters set in order to limit the maximum phase uncertainty of the super sensor to 30 degrees until 500Hz ([[./figs/upper_bounds_comp_filter_max_phase_uncertainty.png][png]], [[./figs/upper_bounds_comp_filter_max_phase_uncertainty.pdf][pdf]])
|
||||
% [[file:figs/upper_bounds_comp_filter_max_phase_uncertainty.png]]
|
||||
|
||||
% The $\mathcal{H}_\infty$ synthesis is performed using the defined weights and the obtained complementary filters are shown in Fig. [[fig:comp_filter_hinf_uncertainty]].
|
||||
|
||||
|
||||
P = [W1 -W1;
|
||||
0 W2;
|
||||
1 0];
|
||||
|
||||
|
||||
|
||||
% And we do the $\mathcal{H}_\infty$ synthesis using the =hinfsyn= command.
|
||||
|
||||
[H2, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
|
||||
|
||||
|
||||
% #+RESULTS:
|
||||
% #+begin_example
|
||||
% [H2, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
% Resetting value of Gamma min based on D_11, D_12, D_21 terms
|
||||
|
||||
% Test bounds: 0.0447 < gamma <= 1.3318
|
||||
|
||||
% gamma hamx_eig xinf_eig hamy_eig yinf_eig nrho_xy p/f
|
||||
% 1.332 1.3e+01 -1.0e-14 1.3e+00 -2.6e-18 0.0000 p
|
||||
% 0.688 1.3e-11# ******** 1.3e+00 -6.7e-15 ******** f
|
||||
% 1.010 1.1e+01 -1.5e-14 1.3e+00 -2.5e-14 0.0000 p
|
||||
% 0.849 6.9e-11# ******** 1.3e+00 -2.3e-14 ******** f
|
||||
% 0.930 5.2e-12# ******** 1.3e+00 -6.1e-18 ******** f
|
||||
% 0.970 5.6e-11# ******** 1.3e+00 -2.3e-14 ******** f
|
||||
% 0.990 5.0e-11# ******** 1.3e+00 -1.7e-17 ******** f
|
||||
% 1.000 2.1e-10# ******** 1.3e+00 0.0e+00 ******** f
|
||||
% 1.005 1.9e-10# ******** 1.3e+00 -3.7e-14 ******** f
|
||||
% 1.008 1.1e+01 -9.1e-15 1.3e+00 0.0e+00 0.0000 p
|
||||
% 1.006 1.2e-09# ******** 1.3e+00 -6.9e-16 ******** f
|
||||
% 1.007 1.1e+01 -4.6e-15 1.3e+00 -1.8e-16 0.0000 p
|
||||
|
||||
% Gamma value achieved: 1.0069
|
||||
% #+end_example
|
||||
|
||||
|
||||
H1 = 1 - H2;
|
||||
|
||||
figure;
|
||||
|
||||
ax1 = subplot(2,1,1);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W1, freqs, 'Hz'))), '--', 'DisplayName', '$W_1$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W2, freqs, 'Hz'))), '--', 'DisplayName', '$W_2$');
|
||||
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, abs(squeeze(freqresp(H1, freqs, 'Hz'))), '-', 'DisplayName', '$H_1$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, abs(squeeze(freqresp(H2, freqs, 'Hz'))), '-', 'DisplayName', '$H_2$');
|
||||
|
||||
hold off;
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
ylabel('Magnitude');
|
||||
set(gca, 'XTickLabel',[]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
ax2 = subplot(2,1,2);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 180/pi*phase(squeeze(freqresp(H1, freqs, 'Hz'))), '-');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 180/pi*phase(squeeze(freqresp(H2, freqs, 'Hz'))), '-');
|
||||
hold off;
|
||||
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||
set(gca, 'XScale', 'log');
|
||||
yticks([-360:90:360]);
|
||||
|
||||
linkaxes([ax1,ax2],'x');
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
xticks([0.1, 1, 10, 100, 1000]);
|
||||
|
||||
% Super sensor uncertainty
|
||||
% We can now compute the uncertainty of the super sensor. The result is shown in Fig. [[fig:super_sensor_uncertainty_bode_plot]].
|
||||
|
||||
|
||||
Gss = G1*H1 + G2*H2;
|
||||
|
||||
Gsss = usample(Gss, 20);
|
||||
|
||||
% We here compute the maximum and minimum phase of the super sensor
|
||||
Dphiss = 180/pi*asin(abs(squeeze(freqresp(w1*H1, freqs, 'Hz')))+abs(squeeze(freqresp(w2*H2, freqs, 'Hz'))));
|
||||
Dphiss(abs(squeeze(freqresp(w1*H1, freqs, 'Hz')))+abs(squeeze(freqresp(w2*H2, freqs, 'Hz'))) > 1) = 190;
|
||||
|
||||
figure;
|
||||
% Magnitude
|
||||
ax1 = subaxis(2,1,1);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1);
|
||||
plot(freqs, 1 + abs(squeeze(freqresp(w1, freqs, 'Hz'))), '--');
|
||||
set(gca,'ColorOrderIndex',1);
|
||||
plot(freqs, max(1 - abs(squeeze(freqresp(w1, freqs, 'Hz'))), 0), '--');
|
||||
set(gca,'ColorOrderIndex',2);
|
||||
plot(freqs, 1 + abs(squeeze(freqresp(w2, freqs, 'Hz'))), '--');
|
||||
set(gca,'ColorOrderIndex',2);
|
||||
plot(freqs, max(1 - abs(squeeze(freqresp(w2, freqs, 'Hz'))), 0), '--');
|
||||
plot(freqs, 1 + abs(squeeze(freqresp(w1*H1+w2*H2, freqs, 'Hz'))), 'k--');
|
||||
plot(freqs, max(1 - abs(squeeze(freqresp(w1*H1+w2*H2, freqs, 'Hz'))), 0), 'k--');
|
||||
for i = 1:length(Gsss)
|
||||
plot(freqs, abs(squeeze(freqresp(Gsss(:, :, i, 1), freqs, 'Hz'))), '-', 'color', [0 0 0 0.2]);
|
||||
end
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
set(gca, 'XTickLabel',[]);
|
||||
ylabel('Magnitude');
|
||||
ylim([1e-1, 10]);
|
||||
hold off;
|
||||
|
||||
% Phase
|
||||
ax2 = subaxis(2,1,2);
|
||||
hold on;
|
||||
% plot(freqs, Dphimax, 'r-');
|
||||
% plot(freqs, -Dphimax, 'r-');
|
||||
set(gca,'ColorOrderIndex',1);
|
||||
plot(freqs, Dphi1, '--');
|
||||
set(gca,'ColorOrderIndex',1);
|
||||
plot(freqs, -Dphi1, '--');
|
||||
set(gca,'ColorOrderIndex',2);
|
||||
plot(freqs, Dphi2, '--');
|
||||
set(gca,'ColorOrderIndex',2);
|
||||
plot(freqs, -Dphi2, '--');
|
||||
plot(freqs, Dphiss, 'k--');
|
||||
plot(freqs, -Dphiss, 'k--');
|
||||
for i = 1:length(Gsss)
|
||||
plot(freqs, 180/pi*angle(squeeze(freqresp(Gsss(:, :, i, 1), freqs, 'Hz'))), '-', 'color', [0 0 0 0.2]);
|
||||
end
|
||||
set(gca,'xscale','log');
|
||||
yticks(-180:90:180);
|
||||
ylim([-180 180]);
|
||||
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||
hold off;
|
||||
linkaxes([ax1,ax2],'x');
|
||||
166
matlab/matlab/comp_filters_analytical.m
Normal file
166
matlab/matlab/comp_filters_analytical.m
Normal file
@ -0,0 +1,166 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
|
||||
s = zpk('s');
|
||||
|
||||
freqs = logspace(-1, 3, 1000);
|
||||
|
||||
% Analytical 1st order complementary filters
|
||||
% First order complementary filters are defined with following equations:
|
||||
% \begin{align}
|
||||
% H_L(s) = \frac{1}{1 + \frac{s}{\omega_0}}\\
|
||||
% H_H(s) = \frac{\frac{s}{\omega_0}}{1 + \frac{s}{\omega_0}}
|
||||
% \end{align}
|
||||
|
||||
% Their bode plot is shown figure [[fig:comp_filter_1st_order]].
|
||||
|
||||
|
||||
w0 = 2*pi; % [rad/s]
|
||||
|
||||
Hh1 = (s/w0)/((s/w0)+1);
|
||||
Hl1 = 1/((s/w0)+1);
|
||||
|
||||
freqs = logspace(-2, 2, 1000);
|
||||
|
||||
figure;
|
||||
% Magnitude
|
||||
ax1 = subaxis(2,1,1);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, abs(squeeze(freqresp(Hh1, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, abs(squeeze(freqresp(Hl1, freqs, 'Hz'))));
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
set(gca, 'XTickLabel',[]);
|
||||
ylabel('Magnitude');
|
||||
hold off;
|
||||
% Phase
|
||||
ax2 = subaxis(2,1,2);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, 180/pi*angle(squeeze(freqresp(Hh1, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, 180/pi*angle(squeeze(freqresp(Hl1, freqs, 'Hz'))));
|
||||
set(gca,'xscale','log');
|
||||
yticks(-180:90:180);
|
||||
ylim([-180 180]);
|
||||
xlabel('Relative Frequency $\frac{\omega}{\omega_0}$'); ylabel('Phase [deg]');
|
||||
hold off;
|
||||
linkaxes([ax1,ax2],'x');
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
|
||||
% Second Order Complementary Filters
|
||||
% We here use analytical formula for the complementary filters $H_L$ and $H_H$.
|
||||
|
||||
% The first two formulas that are used to generate complementary filters are:
|
||||
% \begin{align*}
|
||||
% H_L(s) &= \frac{(1+\alpha) (\frac{s}{\omega_0})+1}{\left((\frac{s}{\omega_0})+1\right) \left((\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1\right)}\\
|
||||
% H_H(s) &= \frac{(\frac{s}{\omega_0})^2 \left((\frac{s}{\omega_0})+1+\alpha\right)}{\left((\frac{s}{\omega_0})+1\right) \left((\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1\right)}
|
||||
% \end{align*}
|
||||
% where:
|
||||
% - $\omega_0$ is the blending frequency in rad/s.
|
||||
% - $\alpha$ is used to change the shape of the filters:
|
||||
% - Small values for $\alpha$ will produce high magnitude of the filters $|H_L(j\omega)|$ and $|H_H(j\omega)|$ near $\omega_0$ but smaller value for $|H_L(j\omega)|$ above $\approx 1.5 \omega_0$ and for $|H_H(j\omega)|$ below $\approx 0.7 \omega_0$
|
||||
% - A large $\alpha$ will do the opposite
|
||||
|
||||
% This is illustrated on figure [[fig:comp_filters_param_alpha]].
|
||||
% The slope of those filters at high and low frequencies is $-2$ and $2$ respectively for $H_L$ and $H_H$.
|
||||
|
||||
|
||||
freqs_study = logspace(-2, 2, 10000);
|
||||
alphas = [0.1, 1, 10];
|
||||
w0 = 2*pi*1;
|
||||
|
||||
figure;
|
||||
ax1 = subaxis(2,1,1);
|
||||
hold on;
|
||||
for i = 1:length(alphas)
|
||||
alpha = alphas(i);
|
||||
Hh2 = (s/w0)^2*((s/w0)+1+alpha)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
|
||||
Hl2 = ((1+alpha)*(s/w0)+1)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
|
||||
set(gca,'ColorOrderIndex',i);
|
||||
plot(freqs_study, abs(squeeze(freqresp(Hh2, freqs_study, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',i);
|
||||
plot(freqs_study, abs(squeeze(freqresp(Hl2, freqs_study, 'Hz'))));
|
||||
end
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
set(gca, 'XTickLabel',[]);
|
||||
ylabel('Magnitude');
|
||||
hold off;
|
||||
ylim([1e-3, 20]);
|
||||
% Phase
|
||||
ax2 = subaxis(2,1,2);
|
||||
hold on;
|
||||
for i = 1:length(alphas)
|
||||
alpha = alphas(i);
|
||||
Hh2 = (s/w0)^2*((s/w0)+1+alpha)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
|
||||
Hl2 = ((1+alpha)*(s/w0)+1)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
|
||||
set(gca,'ColorOrderIndex',i);
|
||||
plot(freqs_study, 180/pi*angle(squeeze(freqresp(Hh2, freqs_study, 'Hz'))), 'DisplayName', sprintf('$\\alpha = %g$', alpha));
|
||||
set(gca,'ColorOrderIndex',i);
|
||||
plot(freqs_study, 180/pi*angle(squeeze(freqresp(Hl2, freqs_study, 'Hz'))), 'HandleVisibility', 'off');
|
||||
end
|
||||
set(gca,'xscale','log');
|
||||
yticks(-180:90:180);
|
||||
ylim([-180 180]);
|
||||
xlabel('Relative Frequency $\frac{\omega}{\omega_0}$'); ylabel('Phase [deg]');
|
||||
legend('Location', 'northeast');
|
||||
hold off;
|
||||
linkaxes([ax1,ax2],'x');
|
||||
xlim([freqs_study(1), freqs_study(end)]);
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:comp_filters_param_alpha
|
||||
% #+CAPTION: Effect of the parameter $\alpha$ on the shape of the generated second order complementary filters ([[./figs/comp_filters_param_alpha.png][png]], [[./figs/comp_filters_param_alpha.pdf][pdf]])
|
||||
% [[file:figs/comp_filters_param_alpha.png]]
|
||||
|
||||
% We now study the maximum norm of the filters function of the parameter $\alpha$. As we saw that the maximum norm of the filters is important for the robust merging of filters.
|
||||
|
||||
alphas = logspace(-2, 2, 100);
|
||||
w0 = 2*pi*1;
|
||||
infnorms = zeros(size(alphas));
|
||||
|
||||
for i = 1:length(alphas)
|
||||
alpha = alphas(i);
|
||||
Hh2 = (s/w0)^2*((s/w0)+1+alpha)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
|
||||
Hl2 = ((1+alpha)*(s/w0)+1)/(((s/w0)+1)*((s/w0)^2 + alpha*(s/w0) + 1));
|
||||
infnorms(i) = norm(Hh2, 'inf');
|
||||
end
|
||||
|
||||
figure;
|
||||
plot(alphas, infnorms)
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('$\alpha$'); ylabel('$\|H_1\|_\infty$');
|
||||
|
||||
% Third Order Complementary Filters
|
||||
% The following formula gives complementary filters with slopes of $-3$ and $3$:
|
||||
% \begin{align*}
|
||||
% H_L(s) &= \frac{\left(1+(\alpha+1)(\beta+1)\right) (\frac{s}{\omega_0})^2 + (1+\alpha+\beta)(\frac{s}{\omega_0}) + 1}{\left(\frac{s}{\omega_0} + 1\right) \left( (\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1 \right) \left( (\frac{s}{\omega_0})^2 + \beta (\frac{s}{\omega_0}) + 1 \right)}\\
|
||||
% H_H(s) &= \frac{(\frac{s}{\omega_0})^3 \left( (\frac{s}{\omega_0})^2 + (1+\alpha+\beta) (\frac{s}{\omega_0}) + (1+(\alpha+1)(\beta+1)) \right)}{\left(\frac{s}{\omega_0} + 1\right) \left( (\frac{s}{\omega_0})^2 + \alpha (\frac{s}{\omega_0}) + 1 \right) \left( (\frac{s}{\omega_0})^2 + \beta (\frac{s}{\omega_0}) + 1 \right)}
|
||||
% \end{align*}
|
||||
|
||||
% The parameters are:
|
||||
% - $\omega_0$ is the blending frequency in rad/s
|
||||
% - $\alpha$ and $\beta$ that are used to change the shape of the filters similarly to the parameter $\alpha$ for the second order complementary filters
|
||||
|
||||
% The filters are defined below and the result is shown on figure [[fig:complementary_filters_third_order]].
|
||||
|
||||
|
||||
alpha = 1;
|
||||
beta = 10;
|
||||
w0 = 2*pi*14;
|
||||
|
||||
Hh3_ana = (s/w0)^3 * ((s/w0)^2 + (1+alpha+beta)*(s/w0) + (1+(alpha+1)*(beta+1)))/((s/w0 + 1)*((s/w0)^2+alpha*(s/w0)+1)*((s/w0)^2+beta*(s/w0)+1));
|
||||
Hl3_ana = ((1+(alpha+1)*(beta+1))*(s/w0)^2 + (1+alpha+beta)*(s/w0) + 1)/((s/w0 + 1)*((s/w0)^2+alpha*(s/w0)+1)*((s/w0)^2+beta*(s/w0)+1));
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, abs(squeeze(freqresp(Hl3_ana, freqs, 'Hz'))), '-', 'DisplayName', '$H_L$ - Analytical');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, abs(squeeze(freqresp(Hh3_ana, freqs, 'Hz'))), '-', 'DisplayName', '$H_H$ - Analytical');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
ylim([1e-3, 10]);
|
||||
xticks([0.1, 1, 10, 100, 1000]);
|
||||
legend('location', 'northeast');
|
||||
53
matlab/matlab/comp_filters_design.m
Normal file
53
matlab/matlab/comp_filters_design.m
Normal file
@ -0,0 +1,53 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
|
||||
s = zpk('s');
|
||||
|
||||
% Matlab
|
||||
|
||||
omega0 = 1*2*pi; % [rad/s]
|
||||
tau = 1/omega0; % [s]
|
||||
|
||||
% From cite:corke04_inert_visual_sensin_system_small_auton_helic
|
||||
HL1 = 1/(s/omega0 + 1); HH1 = s/omega0/(s/omega0 + 1);
|
||||
|
||||
% From cite:jensen13_basic_uas
|
||||
HL2 = (2*omega0*s + omega0^2)/(s+omega0)^2; HH2 = s^2/(s+omega0)^2;
|
||||
|
||||
% From cite:shaw90_bandw_enhan_posit_measur_using_measur_accel
|
||||
HL3 = (3*tau*s + 1)/(tau*s + 1)^3; HH3 = (tau^3*s^3 + 3*tau^2*s^2)/(tau*s + 1)^3;
|
||||
|
||||
freqs = logspace(-1, 1, 1000);
|
||||
|
||||
figure;
|
||||
% Magnitude
|
||||
ax1 = subaxis(2,1,1);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, abs(squeeze(freqresp(HH1, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, abs(squeeze(freqresp(HL1, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',2); plot(freqs, abs(squeeze(freqresp(HH2, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',2); plot(freqs, abs(squeeze(freqresp(HL2, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',3); plot(freqs, abs(squeeze(freqresp(HH3, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',3); plot(freqs, abs(squeeze(freqresp(HL3, freqs, 'Hz'))));
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
set(gca, 'XTickLabel',[]);
|
||||
ylabel('Magnitude');
|
||||
hold off;
|
||||
ylim([1e-2 2]);
|
||||
% Phase
|
||||
ax2 = subaxis(2,1,2);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, 180/pi*angle(squeeze(freqresp(HH1, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',1); plot(freqs, 180/pi*angle(squeeze(freqresp(HL1, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',2); plot(freqs, 180/pi*angle(squeeze(freqresp(HH2, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',2); plot(freqs, 180/pi*angle(squeeze(freqresp(HL2, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',3); plot(freqs, 180/pi*angle(squeeze(freqresp(HH3, freqs, 'Hz'))));
|
||||
set(gca,'ColorOrderIndex',3); plot(freqs, 180/pi*angle(squeeze(freqresp(HL3, freqs, 'Hz'))));
|
||||
set(gca,'xscale','log');
|
||||
yticks(-180:90:180);
|
||||
ylim([-180 180]);
|
||||
xlabel('Relative Frequency $\frac{\omega}{\omega_0}$'); ylabel('Phase [deg]');
|
||||
hold off;
|
||||
linkaxes([ax1,ax2],'x');
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
58
matlab/matlab/feedback_generate_comp_filters.m
Normal file
58
matlab/matlab/feedback_generate_comp_filters.m
Normal file
@ -0,0 +1,58 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
|
||||
s = zpk('s');
|
||||
|
||||
freqs = logspace(-2, 2, 1000);
|
||||
|
||||
% Loop Gain Design
|
||||
% Let's first define the loop gain $L$.
|
||||
|
||||
wc = 2*pi*1;
|
||||
alpha = 2;
|
||||
|
||||
L = (wc/s)^2 * (s/(wc/alpha) + 1)/(s/wc + alpha);
|
||||
|
||||
figure;
|
||||
|
||||
ax1 = subplot(2,1,1);
|
||||
plot(freqs, abs(squeeze(freqresp(L, freqs, 'Hz'))), '-');
|
||||
ylabel('Magnitude');
|
||||
set(gca, 'XScale', 'log');
|
||||
set(gca, 'YScale', 'log');
|
||||
|
||||
ax2 = subplot(2,1,2);
|
||||
plot(freqs, 180/pi*phase(squeeze(freqresp(L, freqs, 'Hz'))), '--');
|
||||
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||
set(gca, 'XScale', 'log');
|
||||
ylim([-180, 0]);
|
||||
yticks([-360:90:360]);
|
||||
|
||||
linkaxes([ax1,ax2],'x');
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
xticks([0.1, 1, 10, 100, 1000]);
|
||||
|
||||
% Complementary Filters Obtained
|
||||
% We then compute the resulting low pass and high pass filters.
|
||||
|
||||
Hl = L/(L + 1);
|
||||
Hh = 1/(L + 1);
|
||||
|
||||
alphas = [1, 2, 10];
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
for i = 1:length(alphas)
|
||||
alpha = alphas(i);
|
||||
L = (wc/s)^2 * (s/(wc/alpha) + 1)/(s/wc + alpha);
|
||||
Hl = L/(L + 1);
|
||||
Hh = 1/(L + 1);
|
||||
set(gca,'ColorOrderIndex',i)
|
||||
plot(freqs, abs(squeeze(freqresp(Hl, freqs, 'Hz'))), 'DisplayName', sprintf('$\\alpha = %.0f$', alpha));
|
||||
set(gca,'ColorOrderIndex',i)
|
||||
plot(freqs, abs(squeeze(freqresp(Hh, freqs, 'Hz'))), 'HandleVisibility', 'off');
|
||||
end
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Amplitude')
|
||||
legend('location', 'northeast');
|
||||
94
matlab/matlab/h_inf_synthesis_complementary_filters.m
Normal file
94
matlab/matlab/h_inf_synthesis_complementary_filters.m
Normal file
@ -0,0 +1,94 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
|
||||
s = zpk('s');
|
||||
|
||||
freqs = logspace(-1, 3, 1000);
|
||||
|
||||
% Weights
|
||||
|
||||
|
||||
omegab = 2*pi*9;
|
||||
wH = (omegab)^2/(s + omegab*sqrt(1e-5))^2;
|
||||
omegab = 2*pi*28;
|
||||
wL = (s + omegab/(4.5)^(1/3))^3/(s*(1e-4)^(1/3) + omegab)^3;
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(wL, freqs, 'Hz'))), '-', 'DisplayName', '$w_L$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(wH, freqs, 'Hz'))), '-', 'DisplayName', '$w_H$');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
ylim([1e-3, 10]);
|
||||
xticks([0.1, 1, 10, 100, 1000]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
% H-Infinity Synthesis
|
||||
% We define the generalized plant $P$ on matlab.
|
||||
|
||||
P = [0 wL;
|
||||
wH -wH;
|
||||
1 0];
|
||||
|
||||
|
||||
|
||||
% And we do the $\mathcal{H}_\infty$ synthesis using the =hinfsyn= command.
|
||||
|
||||
[Hl_hinf, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
|
||||
|
||||
|
||||
% #+RESULTS:
|
||||
% #+begin_example
|
||||
% [Hl_hinf, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
% Test bounds: 0.0000 < gamma <= 1.7285
|
||||
|
||||
% gamma hamx_eig xinf_eig hamy_eig yinf_eig nrho_xy p/f
|
||||
% 1.729 4.1e+01 8.4e-12 1.8e-01 0.0e+00 0.0000 p
|
||||
% 0.864 3.9e+01 -5.8e-02# 1.8e-01 0.0e+00 0.0000 f
|
||||
% 1.296 4.0e+01 8.4e-12 1.8e-01 0.0e+00 0.0000 p
|
||||
% 1.080 4.0e+01 8.5e-12 1.8e-01 0.0e+00 0.0000 p
|
||||
% 0.972 3.9e+01 -4.2e-01# 1.8e-01 0.0e+00 0.0000 f
|
||||
% 1.026 4.0e+01 8.5e-12 1.8e-01 0.0e+00 0.0000 p
|
||||
% 0.999 3.9e+01 8.5e-12 1.8e-01 0.0e+00 0.0000 p
|
||||
% 0.986 3.9e+01 -1.2e+00# 1.8e-01 0.0e+00 0.0000 f
|
||||
% 0.993 3.9e+01 -8.2e+00# 1.8e-01 0.0e+00 0.0000 f
|
||||
% 0.996 3.9e+01 8.5e-12 1.8e-01 0.0e+00 0.0000 p
|
||||
% 0.994 3.9e+01 8.5e-12 1.8e-01 0.0e+00 0.0000 p
|
||||
% 0.993 3.9e+01 -3.2e+01# 1.8e-01 0.0e+00 0.0000 f
|
||||
|
||||
% Gamma value achieved: 0.9942
|
||||
% #+end_example
|
||||
|
||||
% We then define the high pass filter $H_H = 1 - H_L$. The bode plot of both $H_L$ and $H_H$ is shown on figure [[fig:hinf_filters_results]].
|
||||
|
||||
Hh_hinf = 1 - Hl_hinf;
|
||||
|
||||
% Obtained Complementary Filters
|
||||
|
||||
% The obtained complementary filters are shown on figure [[fig:hinf_filters_results]].
|
||||
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(wL, freqs, 'Hz'))), '--', 'DisplayName', '$w_L$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(wH, freqs, 'Hz'))), '--', 'DisplayName', '$w_H$');
|
||||
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, abs(squeeze(freqresp(Hl_hinf, freqs, 'Hz'))), '-', 'DisplayName', '$H_L$ - $\mathcal{H}_\infty$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, abs(squeeze(freqresp(Hh_hinf, freqs, 'Hz'))), '-', 'DisplayName', '$H_H$ - $\mathcal{H}_\infty$');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
ylim([1e-3, 10]);
|
||||
xticks([0.1, 1, 10, 100, 1000]);
|
||||
legend('location', 'northeast');
|
||||
432
matlab/matlab/optimal_comp_filters.m
Normal file
432
matlab/matlab/optimal_comp_filters.m
Normal file
@ -0,0 +1,432 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
|
||||
s = zpk('s');
|
||||
|
||||
freqs = logspace(-1, 3, 1000);
|
||||
|
||||
% Noise of the sensors
|
||||
% Let's define the noise characteristics of the two sensors by choosing $N_1$ and $N_2$:
|
||||
% - Sensor 1 characterized by $N_1(s)$ has low noise at low frequency (for instance a geophone)
|
||||
% - Sensor 2 characterized by $N_2(s)$ has low noise at high frequency (for instance an accelerometer)
|
||||
|
||||
|
||||
omegac = 100*2*pi; G0 = 1e-5; Ginf = 1e-4;
|
||||
N1 = (Ginf*s/omegac + G0)/(s/omegac + 1)/(1 + s/2/pi/100);
|
||||
|
||||
omegac = 1*2*pi; G0 = 1e-3; Ginf = 1e-8;
|
||||
N2 = ((sqrt(Ginf)*s/omegac + sqrt(G0))/(s/omegac + 1))^2/(1 + s/2/pi/4000)^2;
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(freqs, abs(squeeze(freqresp(N1, freqs, 'Hz'))), '-', 'DisplayName', '$N_1$');
|
||||
plot(freqs, abs(squeeze(freqresp(N2, freqs, 'Hz'))), '-', 'DisplayName', '$N_2$');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
% H-Two Synthesis
|
||||
% As $\tilde{n}_1$ and $\tilde{n}_2$ are normalized white noise: $\Phi_{\tilde{n}_1}(\omega) = \Phi_{\tilde{n}_2}(\omega) = 1$ and we have:
|
||||
% \[ \sigma_{\hat{x}} = \sqrt{\int_0^\infty |H_1 N_1|^2(\omega) + |H_2 N_2|^2(\omega) d\omega} = \left\| \begin{matrix} H_1 N_1 \\ H_2 N_2 \end{matrix} \right\|_2 \]
|
||||
% 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.
|
||||
|
||||
% For that, we use the $\mathcal{H}_2$ Synthesis.
|
||||
|
||||
% We use the generalized plant architecture shown on figure [[fig:h_infinity_optimal_comp_filters]].
|
||||
|
||||
% #+name: fig:h_infinity_optimal_comp_filters
|
||||
% #+caption: $\mathcal{H}_2$ Synthesis - Generalized plant used for the optimal generation of complementary filters
|
||||
% [[file:figs-tikz/h_infinity_optimal_comp_filters.png]]
|
||||
|
||||
% The transfer function from $[n_1, n_2]$ to $\hat{x}$ is:
|
||||
% \[ \begin{bmatrix} N_1 H_1 \\ N_2 (1 - H_1) \end{bmatrix} \]
|
||||
% If we define $H_2 = 1 - H_1$, we obtain:
|
||||
% \[ \begin{bmatrix} N_1 H_1 \\ N_2 H_2 \end{bmatrix} \]
|
||||
|
||||
% Thus, if we minimize the $\mathcal{H}_2$ norm of this transfer function, we minimize the RMS value of $\hat{x}$.
|
||||
|
||||
% We define the generalized plant $P$ on matlab as shown on figure [[fig:h_infinity_optimal_comp_filters]].
|
||||
|
||||
P = [0 N2 1;
|
||||
N1 -N2 0];
|
||||
|
||||
|
||||
|
||||
% And we do the $\mathcal{H}_2$ synthesis using the =h2syn= command.
|
||||
|
||||
[H1, ~, gamma] = h2syn(P, 1, 1);
|
||||
|
||||
|
||||
|
||||
% Finally, we define $H_2(s) = 1 - H_1(s)$.
|
||||
|
||||
H2 = 1 - H1;
|
||||
|
||||
|
||||
|
||||
% The complementary filters obtained are shown on figure [[fig:htwo_comp_filters]].
|
||||
|
||||
% The PSD of the noise of the individual sensor and of the super sensor are shown in Fig. [[fig:psd_sensors_htwo_synthesis]].
|
||||
|
||||
% The Cumulative Power Spectrum (CPS) is shown on Fig. [[fig:cps_h2_synthesis]].
|
||||
|
||||
% The obtained RMS value of the super sensor is lower than the RMS value of the individual sensors.
|
||||
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(freqs, abs(squeeze(freqresp(H1, freqs, 'Hz'))), '-', 'DisplayName', '$H_1$');
|
||||
plot(freqs, abs(squeeze(freqresp(H2, freqs, 'Hz'))), '-', 'DisplayName', '$H_2$');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:htwo_comp_filters
|
||||
% #+CAPTION: Obtained complementary filters using the $\mathcal{H}_2$ Synthesis ([[./figs/htwo_comp_filters.png][png]], [[./figs/htwo_comp_filters.pdf][pdf]])
|
||||
% [[file:figs/htwo_comp_filters.png]]
|
||||
|
||||
|
||||
PSD_S1 = abs(squeeze(freqresp(N1, freqs, 'Hz'))).^2;
|
||||
PSD_S2 = abs(squeeze(freqresp(N2, freqs, 'Hz'))).^2;
|
||||
PSD_H2 = abs(squeeze(freqresp(N1*H1, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2, freqs, 'Hz'))).^2;
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(freqs, PSD_S1, '-', 'DisplayName', '$\Phi_{\hat{x}_1}$');
|
||||
plot(freqs, PSD_S2, '-', 'DisplayName', '$\Phi_{\hat{x}_2}$');
|
||||
plot(freqs, PSD_H2, 'k-', 'DisplayName', '$\Phi_{\hat{x}_{\mathcal{H}_2}}$');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Power Spectral Density');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:psd_sensors_htwo_synthesis
|
||||
% #+CAPTION: Power Spectral Density of the estimated $\hat{x}$ using the two sensors alone and using the optimally fused signal ([[./figs/psd_sensors_htwo_synthesis.png][png]], [[./figs/psd_sensors_htwo_synthesis.pdf][pdf]])
|
||||
% [[file:figs/psd_sensors_htwo_synthesis.png]]
|
||||
|
||||
|
||||
CPS_S1 = 1/pi*cumtrapz(2*pi*freqs, PSD_S1);
|
||||
CPS_S2 = 1/pi*cumtrapz(2*pi*freqs, PSD_S2);
|
||||
CPS_H2 = 1/pi*cumtrapz(2*pi*freqs, PSD_H2);
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(freqs, CPS_S1, '-', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_1} = %.1e$', sqrt(CPS_S1(end))));
|
||||
plot(freqs, CPS_S2, '-', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_2} = %.1e$', sqrt(CPS_S2(end))));
|
||||
plot(freqs, CPS_H2, 'k-', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_{\\mathcal{H}_2}} = %.1e$', sqrt(CPS_H2(end))));
|
||||
set(gca, 'YScale', 'log'); set(gca, 'XScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Cumulative Power Spectrum');
|
||||
hold off;
|
||||
xlim([2e-1, freqs(end)]);
|
||||
ylim([1e-10 1e-5]);
|
||||
legend('location', 'southeast');
|
||||
|
||||
% H-Infinity Synthesis - method A
|
||||
% Another objective that we may have is that the noise of the super sensor $n_{SS}$ is following the minimum of the noise of the two sensors $n_1$ and $n_2$:
|
||||
% \[ \Gamma_{n_{ss}}(\omega) = \min(\Gamma_{n_1}(\omega),\ \Gamma_{n_2}(\omega)) \]
|
||||
|
||||
% In order to obtain that ideal case, we need that the complementary filters be designed such that:
|
||||
% \begin{align*}
|
||||
% & |H_1(j\omega)| = 1 \text{ and } |H_2(j\omega)| = 0 \text{ at frequencies where } \Gamma_{n_1}(\omega) < \Gamma_{n_2}(\omega) \\
|
||||
% & |H_1(j\omega)| = 0 \text{ and } |H_2(j\omega)| = 1 \text{ at frequencies where } \Gamma_{n_1}(\omega) > \Gamma_{n_2}(\omega)
|
||||
% \end{align*}
|
||||
|
||||
% Which is indeed impossible in practice.
|
||||
|
||||
% We could try to approach that with the $\mathcal{H}_\infty$ synthesis by using high order filters.
|
||||
|
||||
% As shown on Fig. [[fig:noise_characteristics_sensors]], the frequency where the two sensors have the same noise level is around 9Hz.
|
||||
% We will thus choose weighting functions such that the merging frequency is around 9Hz.
|
||||
|
||||
% The weighting functions used as well as the obtained complementary filters are shown in Fig. [[fig:weights_comp_filters_Hinfa]].
|
||||
|
||||
|
||||
n = 5; w0 = 2*pi*10; G0 = 1/10; G1 = 10000; Gc = 1/2;
|
||||
W1a = (((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 = 5; w0 = 2*pi*8; G0 = 1000; G1 = 0.1; Gc = 1/2;
|
||||
W2a = (((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;
|
||||
|
||||
P = [W1a -W1a;
|
||||
0 W2a;
|
||||
1 0];
|
||||
|
||||
|
||||
|
||||
% And we do the $\mathcal{H}_\infty$ synthesis using the =hinfsyn= command.
|
||||
|
||||
[H2a, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
|
||||
|
||||
|
||||
% #+RESULTS:
|
||||
% #+begin_example
|
||||
% [H2a, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
% Resetting value of Gamma min based on D_11, D_12, D_21 terms
|
||||
|
||||
% Test bounds: 0.1000 < gamma <= 10500.0000
|
||||
|
||||
% gamma hamx_eig xinf_eig hamy_eig yinf_eig nrho_xy p/f
|
||||
% 1.050e+04 2.1e+01 -3.0e-07 7.8e+00 -1.3e-15 0.0000 p
|
||||
% 5.250e+03 2.1e+01 -1.5e-08 7.8e+00 -5.8e-14 0.0000 p
|
||||
% 2.625e+03 2.1e+01 2.5e-10 7.8e+00 -3.7e-12 0.0000 p
|
||||
% 1.313e+03 2.1e+01 -3.2e-11 7.8e+00 -7.3e-14 0.0000 p
|
||||
% 656.344 2.1e+01 -2.2e-10 7.8e+00 -1.1e-15 0.0000 p
|
||||
% 328.222 2.1e+01 -1.1e-10 7.8e+00 -1.2e-15 0.0000 p
|
||||
% 164.161 2.1e+01 -2.4e-08 7.8e+00 -8.9e-16 0.0000 p
|
||||
% 82.130 2.1e+01 2.0e-10 7.8e+00 -9.1e-31 0.0000 p
|
||||
% 41.115 2.1e+01 -6.8e-09 7.8e+00 -4.1e-13 0.0000 p
|
||||
% 20.608 2.1e+01 3.3e-10 7.8e+00 -1.4e-12 0.0000 p
|
||||
% 10.354 2.1e+01 -9.8e-09 7.8e+00 -1.8e-15 0.0000 p
|
||||
% 5.227 2.1e+01 -4.1e-09 7.8e+00 -2.5e-12 0.0000 p
|
||||
% 2.663 2.1e+01 2.7e-10 7.8e+00 -4.0e-14 0.0000 p
|
||||
% 1.382 2.1e+01 -3.2e+05# 7.8e+00 -3.5e-14 0.0000 f
|
||||
% 2.023 2.1e+01 -5.0e-10 7.8e+00 0.0e+00 0.0000 p
|
||||
% 1.702 2.1e+01 -2.4e+07# 7.8e+00 -1.6e-13 0.0000 f
|
||||
% 1.862 2.1e+01 -6.0e+08# 7.8e+00 -1.0e-12 0.0000 f
|
||||
% 1.942 2.1e+01 -2.8e-09 7.8e+00 -8.1e-14 0.0000 p
|
||||
% 1.902 2.1e+01 -2.5e-09 7.8e+00 -1.1e-13 0.0000 p
|
||||
% 1.882 2.1e+01 -9.3e-09 7.8e+00 -2.0e-15 0.0001 p
|
||||
% 1.872 2.1e+01 -1.3e+09# 7.8e+00 -3.6e-22 0.0000 f
|
||||
% 1.877 2.1e+01 -2.6e+09# 7.8e+00 -1.2e-13 0.0000 f
|
||||
% 1.880 2.1e+01 -5.6e+09# 7.8e+00 -1.4e-13 0.0000 f
|
||||
% 1.881 2.1e+01 -1.2e+10# 7.8e+00 -3.3e-12 0.0000 f
|
||||
% 1.882 2.1e+01 -3.2e+10# 7.8e+00 -8.5e-14 0.0001 f
|
||||
|
||||
% Gamma value achieved: 1.8824
|
||||
% #+end_example
|
||||
|
||||
|
||||
H1a = 1 - H2a;
|
||||
|
||||
figure;
|
||||
|
||||
ax1 = subplot(2,1,1);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W1a, freqs, 'Hz'))), '--', 'DisplayName', '$w_1$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W2a, freqs, 'Hz'))), '--', 'DisplayName', '$w_2$');
|
||||
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, abs(squeeze(freqresp(H1a, freqs, 'Hz'))), '-', 'DisplayName', '$H_1$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, abs(squeeze(freqresp(H2a, freqs, 'Hz'))), '-', 'DisplayName', '$H_2$');
|
||||
|
||||
hold off;
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
ylabel('Magnitude');
|
||||
set(gca, 'XTickLabel',[]);
|
||||
ylim([5e-4, 20]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
ax2 = subplot(2,1,2);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 180/pi*phase(squeeze(freqresp(H1a, freqs, 'Hz'))), '-');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 180/pi*phase(squeeze(freqresp(H2a, freqs, 'Hz'))), '-');
|
||||
hold off;
|
||||
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||
set(gca, 'XScale', 'log');
|
||||
yticks([-360:90:360]);
|
||||
|
||||
linkaxes([ax1,ax2],'x');
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
xticks([0.1, 1, 10, 100, 1000]);
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:weights_comp_filters_Hinfa
|
||||
% #+CAPTION: Weights and Complementary Fitlers obtained ([[./figs/weights_comp_filters_Hinfa.png][png]], [[./figs/weights_comp_filters_Hinfa.pdf][pdf]])
|
||||
% [[file:figs/weights_comp_filters_Hinfa.png]]
|
||||
|
||||
% We then compute the Power Spectral Density as well as the Cumulative Power Spectrum.
|
||||
|
||||
|
||||
PSD_Ha = abs(squeeze(freqresp(N1*H1a, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2a, freqs, 'Hz'))).^2;
|
||||
CPS_Ha = 1/pi*cumtrapz(2*pi*freqs, PSD_Ha);
|
||||
|
||||
% H-Infinity Synthesis - method B
|
||||
% We have that:
|
||||
% \[ \Phi_{\hat{x}}(\omega) = \left|H_1(j\omega) N_1(j\omega)\right|^2 + \left|H_2(j\omega) N_2(j\omega)\right|^2 \]
|
||||
|
||||
% Then, at frequencies where $|H_1(j\omega)| < |H_2(j\omega)|$ we would like that $|N_1(j\omega)| = 1$ and $|N_2(j\omega)| = 0$ as we discussed before.
|
||||
% Then $|H_1 N_1|^2 + |H_2 N_2|^2 = |N_1|^2$.
|
||||
|
||||
% We know that this is impossible in practice. A more realistic choice is to design $H_2(s)$ such that when $|N_2(j\omega)| > |N_1(j\omega)|$, we have that:
|
||||
% \[ |H_2 N_2|^2 = \epsilon |H_1 N_1|^2 \]
|
||||
|
||||
% Which is equivalent to have (by supposing $|H_1| \approx 1$):
|
||||
% \[ |H_2| = \sqrt{\epsilon} \frac{|N_1|}{|N_2|} \]
|
||||
|
||||
% And we have:
|
||||
% \begin{align*}
|
||||
% \Phi_{\hat{x}} &= \left|H_1 N_1\right|^2 + |H_2 N_2|^2 \\
|
||||
% &= (1 + \epsilon) \left| H_1 N_1 \right|^2 \\
|
||||
% &\approx \left|N_1\right|^2
|
||||
% \end{align*}
|
||||
|
||||
% Similarly, we design $H_1(s)$ such that at frequencies where $|N_1| > |N_2|$:
|
||||
% \[ |H_1| = \sqrt{\epsilon} \frac{|N_2|}{|N_1|} \]
|
||||
|
||||
% For instance, is we take $\epsilon = 1$, then the PSD of $\hat{x}$ is increased by just by a factor $\sqrt{2}$ over the all frequencies from the idea case.
|
||||
|
||||
% We use this as the weighting functions for the $\mathcal{H}_\infty$ synthesis of the complementary filters.
|
||||
|
||||
% The weighting function and the obtained complementary filters are shown in Fig. [[fig:weights_comp_filters_Hinfb]].
|
||||
|
||||
|
||||
epsilon = 2;
|
||||
|
||||
W1b = 1/epsilon*N1/N2;
|
||||
W2b = 1/epsilon*N2/N1;
|
||||
|
||||
W1b = W1b/(1 + s/2/pi/1000); % this is added so that it is proper
|
||||
|
||||
P = [W1b -W1b;
|
||||
0 W2b;
|
||||
1 0];
|
||||
|
||||
|
||||
|
||||
% And we do the $\mathcal{H}_\infty$ synthesis using the =hinfsyn= command.
|
||||
|
||||
[H2b, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
|
||||
|
||||
|
||||
% #+RESULTS:
|
||||
% #+begin_example
|
||||
% [H2b, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
|
||||
% Test bounds: 0.0000 < gamma <= 32.8125
|
||||
|
||||
% gamma hamx_eig xinf_eig hamy_eig yinf_eig nrho_xy p/f
|
||||
% 32.812 1.8e+01 3.4e-10 6.3e+00 -2.9e-13 0.0000 p
|
||||
% 16.406 1.8e+01 3.4e-10 6.3e+00 -1.2e-15 0.0000 p
|
||||
% 8.203 1.8e+01 3.3e-10 6.3e+00 -2.6e-13 0.0000 p
|
||||
% 4.102 1.8e+01 3.3e-10 6.3e+00 -2.1e-13 0.0000 p
|
||||
% 2.051 1.7e+01 3.4e-10 6.3e+00 -7.2e-16 0.0000 p
|
||||
% 1.025 1.6e+01 -1.3e+06# 6.3e+00 -8.3e-14 0.0000 f
|
||||
% 1.538 1.7e+01 3.4e-10 6.3e+00 -2.0e-13 0.0000 p
|
||||
% 1.282 1.7e+01 3.4e-10 6.3e+00 -7.9e-17 0.0000 p
|
||||
% 1.154 1.7e+01 3.6e-10 6.3e+00 -1.8e-13 0.0000 p
|
||||
% 1.089 1.7e+01 -3.4e+06# 6.3e+00 -1.7e-13 0.0000 f
|
||||
% 1.122 1.7e+01 -1.0e+07# 6.3e+00 -3.2e-13 0.0000 f
|
||||
% 1.138 1.7e+01 -1.3e+08# 6.3e+00 -1.8e-13 0.0000 f
|
||||
% 1.146 1.7e+01 3.2e-10 6.3e+00 -3.0e-13 0.0000 p
|
||||
% 1.142 1.7e+01 5.5e-10 6.3e+00 -2.8e-13 0.0000 p
|
||||
% 1.140 1.7e+01 -1.5e-10 6.3e+00 -2.3e-13 0.0000 p
|
||||
% 1.139 1.7e+01 -4.8e+08# 6.3e+00 -6.2e-14 0.0000 f
|
||||
% 1.139 1.7e+01 1.3e-09 6.3e+00 -8.9e-17 0.0000 p
|
||||
|
||||
% Gamma value achieved: 1.1390
|
||||
% #+end_example
|
||||
|
||||
|
||||
H1b = 1 - H2b;
|
||||
|
||||
figure;
|
||||
|
||||
ax1 = subplot(2,1,1);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W1b, freqs, 'Hz'))), '--', 'DisplayName', '$w_1$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 1./abs(squeeze(freqresp(W2b, freqs, 'Hz'))), '--', 'DisplayName', '$w_2$');
|
||||
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, abs(squeeze(freqresp(H1b, freqs, 'Hz'))), '-', 'DisplayName', '$H_1$');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, abs(squeeze(freqresp(H2b, freqs, 'Hz'))), '-', 'DisplayName', '$H_2$');
|
||||
|
||||
hold off;
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
ylabel('Magnitude');
|
||||
set(gca, 'XTickLabel',[]);
|
||||
ylim([5e-4, 20]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
ax2 = subplot(2,1,2);
|
||||
hold on;
|
||||
set(gca,'ColorOrderIndex',1)
|
||||
plot(freqs, 180/pi*phase(squeeze(freqresp(H1b, freqs, 'Hz'))), '-');
|
||||
set(gca,'ColorOrderIndex',2)
|
||||
plot(freqs, 180/pi*phase(squeeze(freqresp(H2b, freqs, 'Hz'))), '-');
|
||||
hold off;
|
||||
xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
|
||||
set(gca, 'XScale', 'log');
|
||||
yticks([-360:90:360]);
|
||||
|
||||
linkaxes([ax1,ax2],'x');
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
xticks([0.1, 1, 10, 100, 1000]);
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:weights_comp_filters_Hinfb
|
||||
% #+CAPTION: Weights and Complementary Fitlers obtained ([[./figs/weights_comp_filters_Hinfb.png][png]], [[./figs/weights_comp_filters_Hinfb.pdf][pdf]])
|
||||
% [[file:figs/weights_comp_filters_Hinfb.png]]
|
||||
|
||||
|
||||
PSD_Hb = abs(squeeze(freqresp(N1*H1b, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2b, freqs, 'Hz'))).^2;
|
||||
CPS_Hb = 1/pi*cumtrapz(2*pi*freqs, PSD_Hb);
|
||||
|
||||
|
||||
|
||||
% #+name: tab:rms_results
|
||||
% #+caption: RMS value of the estimation error when using the sensor individually and when using the two sensor merged using the optimal complementary filters
|
||||
% #+RESULTS:
|
||||
% | | rms value |
|
||||
% |--------------+-----------|
|
||||
% | Sensor 1 | 1.3e-03 |
|
||||
% | Sensor 2 | 1.3e-03 |
|
||||
% | H2 Fusion | 1.2e-04 |
|
||||
% | H-Infinity a | 2.4e-04 |
|
||||
% | H-Infinity b | 1.4e-04 |
|
||||
|
||||
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(freqs, PSD_S1, '-', 'DisplayName', '$\Phi_{\hat{x}_1}$');
|
||||
plot(freqs, PSD_S2, '-', 'DisplayName', '$\Phi_{\hat{x}_2}$');
|
||||
plot(freqs, PSD_H2, 'k-', 'DisplayName', '$\Phi_{\hat{x}_{\mathcal{H}_2}}$');
|
||||
plot(freqs, PSD_Ha, 'k--', 'DisplayName', '$\Phi_{\hat{x}_{\mathcal{H}_\infty},a}$');
|
||||
plot(freqs, PSD_Hb, 'k-.', 'DisplayName', '$\Phi_{\hat{x}_{\mathcal{H}_\infty},b}$');
|
||||
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Power Spectral Density');
|
||||
hold off;
|
||||
xlim([freqs(1), freqs(end)]);
|
||||
legend('location', 'northeast');
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:comparison_psd_noise
|
||||
% #+CAPTION: Comparison of the obtained Power Spectral Density using the three methods ([[./figs/comparison_psd_noise.png][png]], [[./figs/comparison_psd_noise.pdf][pdf]])
|
||||
% [[file:figs/comparison_psd_noise.png]]
|
||||
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(freqs, CPS_S1, '-', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_1} = %.1e$', sqrt(CPS_S1(end))));
|
||||
plot(freqs, CPS_S2, '-', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_2} = %.1e$', sqrt(CPS_S2(end))));
|
||||
plot(freqs, CPS_H2, 'k-', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_{\\mathcal{H}_2}} = %.1e$', sqrt(CPS_H2(end))));
|
||||
plot(freqs, CPS_Ha, 'k--', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_{\\mathcal{H}_\\infty, a}} = %.1e$', sqrt(CPS_Ha(end))));
|
||||
plot(freqs, CPS_Hb, 'k-.', 'DisplayName', sprintf('$\\sigma_{\\hat{x}_{\\mathcal{H}_\\infty, b}} = %.1e$', sqrt(CPS_Hb(end))));
|
||||
set(gca, 'YScale', 'log'); set(gca, 'XScale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Cumulative Power Spectrum');
|
||||
hold off;
|
||||
xlim([2e-1, freqs(end)]);
|
||||
ylim([1e-10 1e-5]);
|
||||
legend('location', 'southeast');
|
||||
Loading…
Reference in New Issue
Block a user