Add H-Infinity synthesis to minimize the super sensor noise RMS
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@ -250,6 +250,336 @@ The optimal sensor fusion has permitted to reduced the RMS value of the estimati
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| Sensor 2 | 1.3e-03 |
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| Sensor 2 | 1.3e-03 |
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| Optimal Sensor Fusion | 1.5e-04 |
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| Optimal Sensor Fusion | 1.5e-04 |
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** H-Infinity Synthesis
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*** First idea
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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 $n_2$:
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\[ \Gamma_{n_{ss}}(\omega) = \min(\Gamma_{n_1}(\omega),\ \Gamma_{n_2}(\omega)) \]
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In order to obtain that ideal case, we need that the complementary filters be designed such that:
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\begin{align*}
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& H_1(j\omega) = 1 \text{ and } H_2(j\omega) = 0 \text{ at frequencies where } \Gamma_{n_1}(\omega) < \Gamma_{n_2}(\omega) \\
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& H_1(j\omega) = 0 \text{ and } H_2(j\omega) = 1 \text{ at frequencies where } \Gamma_{n_1}(\omega) > \Gamma_{n_2}(\omega)
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\end{align*}
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Which is indeed *impossible in practice*.
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We could try to use filters with the highest possible order to approximate that.
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However, we may have robustness and implementation problems.
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#+begin_src matlab
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omegac = 100*2*pi; G0 = 1e-5; Ginf = 1e-4;
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N1 = (Ginf*s/omegac + G0)/(s/omegac + 1)/(1 + s/2/pi/4000);
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omegac = 1*2*pi; G0 = 1e-3; Ginf = 1e-8;
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N2 = ((sqrt(Ginf)*s/omegac + sqrt(G0))/(s/omegac + 1))^2/(1 + s/2/pi/4000)^2;
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#+end_src
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#+begin_src matlab
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n = 5; w0 = 2*pi*10; G0 = 1/10; G1 = 10000; Gc = 1/2;
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W1 = (((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n;
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n = 5; w0 = 2*pi*8; G0 = 1000; G1 = 0.1; Gc = 1/2;
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W2 = (((1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (G0/Gc)^(1/n))/((1/G1)^(1/n)*(1/w0)*sqrt((1-(G0/Gc)^(2/n))/(1-(Gc/G1)^(2/n)))*s + (1/Gc)^(1/n)))^n;
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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, 1./abs(squeeze(freqresp(W1, freqs, 'Hz'))), '--', 'DisplayName', '$W_1 = \frac{N_1}{N_2}$');
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plot(freqs, 1./abs(squeeze(freqresp(W2, freqs, 'Hz'))), '--', 'DisplayName', '$W_2 = \frac{N_2}{N_1}$');
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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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#+begin_src matlab
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P = [W1 -W1;
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0 W2;
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1 0];
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#+end_src
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And we do the $\mathcal{H}_\infty$ synthesis using the =hinfsyn= command.
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#+begin_src matlab :results output replace :exports both
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[H2, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
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#+end_src
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#+RESULTS:
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#+begin_example
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[H2, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
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Resetting value of Gamma min based on D_11, D_12, D_21 terms
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Test bounds: 0.1000 < gamma <= 10500.0000
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gamma hamx_eig xinf_eig hamy_eig yinf_eig nrho_xy p/f
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1.050e+04 2.1e+01 -3.0e-07 7.8e+00 -1.3e-15 0.0000 p
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5.250e+03 2.1e+01 -1.5e-08 7.8e+00 -5.8e-14 0.0000 p
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2.625e+03 2.1e+01 2.5e-10 7.8e+00 -3.7e-12 0.0000 p
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1.313e+03 2.1e+01 -3.2e-11 7.8e+00 -7.3e-14 0.0000 p
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656.344 2.1e+01 -2.2e-10 7.8e+00 -1.1e-15 0.0000 p
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328.222 2.1e+01 -1.1e-10 7.8e+00 -1.2e-15 0.0000 p
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164.161 2.1e+01 -2.4e-08 7.8e+00 -8.9e-16 0.0000 p
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82.130 2.1e+01 2.0e-10 7.8e+00 -9.1e-31 0.0000 p
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41.115 2.1e+01 -6.8e-09 7.8e+00 -4.1e-13 0.0000 p
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20.608 2.1e+01 3.3e-10 7.8e+00 -1.4e-12 0.0000 p
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10.354 2.1e+01 -9.8e-09 7.8e+00 -1.8e-15 0.0000 p
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5.227 2.1e+01 -4.1e-09 7.8e+00 -2.5e-12 0.0000 p
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2.663 2.1e+01 2.7e-10 7.8e+00 -4.0e-14 0.0000 p
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1.382 2.1e+01 -3.2e+05# 7.8e+00 -3.5e-14 0.0000 f
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2.023 2.1e+01 -5.0e-10 7.8e+00 0.0e+00 0.0000 p
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1.702 2.1e+01 -2.4e+07# 7.8e+00 -1.6e-13 0.0000 f
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1.862 2.1e+01 -6.0e+08# 7.8e+00 -1.0e-12 0.0000 f
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1.942 2.1e+01 -2.8e-09 7.8e+00 -8.1e-14 0.0000 p
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1.902 2.1e+01 -2.5e-09 7.8e+00 -1.1e-13 0.0000 p
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1.882 2.1e+01 -9.3e-09 7.8e+00 -2.0e-15 0.0001 p
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1.872 2.1e+01 -1.3e+09# 7.8e+00 -3.6e-22 0.0000 f
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1.877 2.1e+01 -2.6e+09# 7.8e+00 -1.2e-13 0.0000 f
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1.880 2.1e+01 -5.6e+09# 7.8e+00 -1.4e-13 0.0000 f
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1.881 2.1e+01 -1.2e+10# 7.8e+00 -3.3e-12 0.0000 f
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1.882 2.1e+01 -3.2e+10# 7.8e+00 -8.5e-14 0.0001 f
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Gamma value achieved: 1.8824
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#+end_example
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#+begin_src matlab
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H1 = 1 - H2;
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#+end_src
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#+begin_src matlab :exports none
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figure;
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ax1 = subplot(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'))), '--', 'DisplayName', '$w_1$');
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set(gca,'ColorOrderIndex',2)
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plot(freqs, 1./abs(squeeze(freqresp(W2, freqs, 'Hz'))), '--', 'DisplayName', '$w_2$');
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set(gca,'ColorOrderIndex',1)
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plot(freqs, abs(squeeze(freqresp(H1, freqs, 'Hz'))), '-', 'DisplayName', '$H_1$');
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set(gca,'ColorOrderIndex',2)
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plot(freqs, abs(squeeze(freqresp(H2, freqs, 'Hz'))), '-', 'DisplayName', '$H_2$');
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hold off;
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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ylabel('Magnitude');
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set(gca, 'XTickLabel',[]);
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ylim([5e-4, 20]);
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legend('location', 'northeast');
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ax2 = subplot(2,1,2);
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hold on;
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set(gca,'ColorOrderIndex',1)
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plot(freqs, 180/pi*phase(squeeze(freqresp(H1, freqs, 'Hz'))), '-');
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set(gca,'ColorOrderIndex',2)
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plot(freqs, 180/pi*phase(squeeze(freqresp(H2, freqs, 'Hz'))), '-');
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hold off;
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xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
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set(gca, 'XScale', 'log');
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yticks([-360:90:360]);
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linkaxes([ax1,ax2],'x');
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xlim([freqs(1), freqs(end)]);
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xticks([0.1, 1, 10, 100, 1000]);
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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(N1, freqs, 'Hz'))).^2, '-', 'DisplayName', '$|N_1|^2$');
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plot(freqs, abs(squeeze(freqresp(N2, freqs, 'Hz'))).^2, '-', 'DisplayName', '$|N_2|^2$');
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plot(freqs, abs(squeeze(freqresp(N1*H1, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2, freqs, 'Hz'))).^2, 'k-', 'DisplayName', '$|N_1H_1|^2+|N_2H_2|^2$');
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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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#+begin_src matlab :exports results :results value table replace :tangle no :post addhdr(*this*)
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data2orgtable([norm([N1], 2);norm([N2], 2);norm([N1*H1 + N2*H2], 2)], {'Sensor 1', 'Sensor 2', 'Optimal Sensor Fusion'}, {'rms value'}, ' %.1e');
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#+end_src
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#+RESULTS:
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| | rms value |
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|-----------------------+-----------|
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| Sensor 1 | 1.1e-02 |
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| Sensor 2 | 1.3e-03 |
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| Optimal Sensor Fusion | 3.2e-04 |
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*** Second idea
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We have that:
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\[ \Phi_{n_{ss}} = \left|H_1\right|^2 \Phi_{n_1} + \left|H_2\right|^2 \Phi_{n_2} \]
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We model the Power Spectral Densities of the two sensors by weighting functions $N_1(s)$ and $N_2(s)$.
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\[ \Phi_{n_{ss}} = \left|H_1 N_1\right|^2 + \left|H_2\right N_2|^2 \]
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Then, at frequencies where $|H_1| < |H_2|$ we would like that $|N_1| = 1$ and $|N_2| = 0$ as we discussed before.
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Then $|H_1 N_1|^2 + |H_2 N_2|^2 = |H_1 N_1|^2$.
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However, if we design $H_2$ such that when $|N_1| < |N_2|$, we have that:
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\[ |H_2 N_2|^2 = \epsilon |H_1 N_1|^2 \]
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And we have:
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\begin{align*}
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\Phi_{n_{ss}} &= \left|H_1 N_1\right|^2 + |H_2 N_2|^2 \\
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&= (1 + \epsilon)\left|H_1 N_1\right|^2 \\
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&\approx \left|H_1 N_1\right|^2
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\end{align*}
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We have then that:
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\[ |H_2 N_2| = \sqrt{\epsilon} |H_1 N_1| \]
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We suppose that $|H_1| = 1$:
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\[ |H_2| = \sqrt{\epsilon} \frac{|N_1|}{|N_2|} \]
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We can use that for the maximum magnitude of $|H_2|$ at frequencies where $|N_1| < |N_2|$
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Similarly, we have that the maximum magnitude of $|H_1|$ at frequencies where $|N_1| > |N_2|$ is:
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\[ |H_1| = \sqrt{\epsilon} \frac{|N_2|}{|N_1|} \]
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We could take $\epsilon = 1$, then we increase the noise of the super sensor just by a factor $\sqrt{2}$ over the all bandwidth from the idea case.
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This could be used as weighting functions for the $\mathcal{H}_\infty$ synthesis of the complementary filters.
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#+begin_src matlab
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omegac = 100*2*pi; G0 = 1e-5; Ginf = 1e-4;
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N1 = (Ginf*s/omegac + G0)/(s/omegac + 1)/(1 + s/2/pi/4000);
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omegac = 1*2*pi; G0 = 1e-3; Ginf = 1e-8;
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N2 = ((sqrt(Ginf)*s/omegac + sqrt(G0))/(s/omegac + 1))^2/(1 + s/2/pi/4000)^2;
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#+end_src
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#+begin_src matlab
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W1 = N1/N2;
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W2 = N2/N1;
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W1 = W1/(1 + s/2/pi/1000); % this is added so that it is proper
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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(W1, freqs, 'Hz'))), '--', 'DisplayName', '$W_1 = \frac{N_1}{N_2}$');
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plot(freqs, abs(squeeze(freqresp(W2, freqs, 'Hz'))), '--', 'DisplayName', '$W_2 = \frac{N_2}{N_1}$');
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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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#+begin_src matlab
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P = [W1 -W1;
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0 W2;
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1 0];
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#+end_src
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And we do the $\mathcal{H}_\infty$ synthesis using the =hinfsyn= command.
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#+begin_src matlab :results output replace :exports both
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[H2, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
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#+end_src
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#+RESULTS:
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#+begin_example
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[H2, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
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Test bounds: 0.0000 < gamma <= 2625.0000
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gamma hamx_eig xinf_eig hamy_eig yinf_eig nrho_xy p/f
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2.625e+03 1.8e+01 -2.3e-12 6.3e+00 -1.7e-14 0.0000 p
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1.313e+03 1.8e+01 -3.3e-12 6.3e+00 -5.9e-13 0.0000 p
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656.250 1.8e+01 -1.8e-12 6.3e+00 -1.2e-13 0.0000 p
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328.125 1.8e+01 -8.3e-13 6.3e+00 -7.5e-14 0.0000 p
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164.063 1.8e+01 -3.8e-13 6.3e+00 -6.1e-14 0.0000 p
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82.031 1.8e+01 -3.3e-22 6.3e+00 -5.4e-14 0.0000 p
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41.016 1.8e+01 -3.4e-12 6.3e+00 -1.4e-16 0.0000 p
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20.508 1.8e+01 -6.4e-14 6.3e+00 -4.4e-13 0.0000 p
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10.254 1.8e+01 -1.4e-13 6.3e+00 -4.9e-14 0.0000 p
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5.127 1.7e+01 -1.1e-12 6.3e+00 -1.6e-13 0.0000 p
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2.563 1.7e+01 -6.5e-13 6.3e+00 -7.3e-14 0.0000 p
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1.282 1.4e+01 -8.9e+04# 6.3e+00 -1.6e-14 0.0000 f
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1.923 1.6e+01 -4.6e+05# 6.3e+00 -2.1e-13 0.0000 f
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2.243 1.7e+01 -1.9e+06# 6.3e+00 -9.9e-14 0.0000 f
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2.403 1.7e+01 -3.7e-11 6.3e+00 -2.1e-12 0.0000 p
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2.323 1.7e+01 -4.6e+06# 6.3e+00 -5.8e-14 0.0000 f
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2.363 1.7e+01 -1.2e+07# 6.3e+00 -3.3e-14 0.0000 f
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2.383 1.7e+01 -6.8e+07# 6.3e+00 -1.2e-13 0.0000 f
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2.393 1.7e+01 -5.5e-11 6.3e+00 -1.8e-12 0.0000 p
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2.388 1.7e+01 1.3e-22 6.3e+00 -2.5e-15 0.0000 p
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2.386 1.7e+01 -1.5e+08# 6.3e+00 -1.4e-13 0.0000 f
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2.387 1.7e+01 -4.0e+08# 6.3e+00 -1.0e-13 0.0000 f
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2.388 1.7e+01 -2.1e+09# 6.3e+00 -4.2e-13 0.0000 f
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|
Gamma value achieved: 2.3882
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|
#+end_example
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|
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|
#+begin_src matlab
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|
H1 = 1 - H2;
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||||||
|
#+end_src
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||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
figure;
|
||||||
|
|
||||||
|
ax1 = subplot(2,1,1);
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||||||
|
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',[]);
|
||||||
|
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(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]);
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :exports none
|
||||||
|
figure;
|
||||||
|
hold on;
|
||||||
|
plot(freqs, abs(squeeze(freqresp(N1, freqs, 'Hz'))).^2, '-', 'DisplayName', '$|N_1|^2$');
|
||||||
|
plot(freqs, abs(squeeze(freqresp(N2, freqs, 'Hz'))).^2, '-', 'DisplayName', '$|N_2|^2$');
|
||||||
|
plot(freqs, abs(squeeze(freqresp(N1*H1, freqs, 'Hz'))).^2+abs(squeeze(freqresp(N2*H2, freqs, 'Hz'))).^2, 'k-', 'DisplayName', '$|N_1H_1|^2+|N_2H_2|^2$');
|
||||||
|
set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
|
||||||
|
xlabel('Frequency [Hz]'); ylabel('Magnitude');
|
||||||
|
hold off;
|
||||||
|
xlim([freqs(1), freqs(end)]);
|
||||||
|
legend('location', 'northeast');
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+begin_src matlab :exports results :results value table replace :tangle no :post addhdr(*this*)
|
||||||
|
data2orgtable([norm([N1], 2);norm([N2], 2);norm([N1*H1 + N2*H2], 2)], {'Sensor 1', 'Sensor 2', 'Optimal Sensor Fusion'}, {'rms value'}, ' %.1e');
|
||||||
|
#+end_src
|
||||||
|
|
||||||
|
#+RESULTS:
|
||||||
|
| | rms value |
|
||||||
|
|-----------------------+-----------|
|
||||||
|
| Sensor 1 | 1.1e-02 |
|
||||||
|
| Sensor 2 | 1.3e-03 |
|
||||||
|
| Optimal Sensor Fusion | 1.9e-04 |
|
||||||
|
|
||||||
|
*** Conclusion
|
||||||
|
From the above two experiments with $\mathcal{H}_\infty$ it still seems that the $\mathcal{H}_2$ synthesis gives the complementary filters that permits to obtain the minimal super sensor noise (when measuring with the $\mathcal{H}_2$ norm)
|
||||||
|
|
||||||
* Robustness to sensor dynamics uncertainty
|
* Robustness to sensor dynamics uncertainty
|
||||||
:PROPERTIES:
|
:PROPERTIES:
|
||||||
:header-args:matlab+: :tangle matlab/comp_filter_robustness.m
|
:header-args:matlab+: :tangle matlab/comp_filter_robustness.m
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user