Identification of all the elements in the system. Decentralized Controller.
This commit is contained in:
1039
matlab/cercalo_identification.m
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1039
matlab/cercalo_identification.m
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77
matlab/decentralized_control.m
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77
matlab/decentralized_control.m
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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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freqs = logspace(0, 3, 1000);
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% Load Plant
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load('mat/plant.mat', 'sys', 'Gi', 'Zc', 'Ga', 'Gc', 'Gn', 'Gd');
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% Diagonal Controller
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% Using =SISOTOOL=, a diagonal controller is designed.
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% The two SISO loop gains are shown in Fig. [[fig:diag_contr_loop_gain]].
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Kh = -0.25598*(s+112)*(s^2 + 15.93*s + 6.686e06)/((s^2*(s+352.5)*(1+s/2/pi/2000)));
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Kv = 10207*(s+55.15)*(s^2 + 17.45*s + 2.491e06)/(s^2*(s+491.2)*(s+7695));
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K = blkdiag(Kh, Kv);
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K.InputName = {'Rh', 'Rv'};
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K.OutputName = {'Uch', 'Ucv'};
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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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plot(freqs, abs(squeeze(freqresp(Kh*sys('Rh', 'Uch'), freqs, 'Hz'))), 'DisplayName', '$L_h = K_h G_{d,h}^{-1} G_{\frac{V_{p,h}}{\tilde{U}_{c,h}}} G_{i,h} $');
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plot(freqs, abs(squeeze(freqresp(Kv*sys('Rv', 'Ucv'), freqs, 'Hz'))), 'DisplayName', '$L_v = K_v G_{d,v}^{-1} G_{\frac{V_{p,v}}{\tilde{U}_{c,v}}} G_{i,v} $');
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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 [dB]');
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hold off;
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legend('location', 'northeast');
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% Phase
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ax2 = subaxis(2,1,2);
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hold on;
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plot(freqs, 180/pi*angle(squeeze(freqresp(Kh*sys('Rh', 'Uch'), freqs, 'Hz'))));
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plot(freqs, 180/pi*angle(squeeze(freqresp(Kv*sys('Rv', 'Ucv'), 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('Frequency [Hz]'); 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:diag_contr_loop_gain
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% #+CAPTION: Loop Gain using the Decentralized Diagonal Controller ([[./figs/diag_contr_loop_gain.png][png]], [[./figs/diag_contr_loop_gain.pdf][pdf]])
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% [[file:figs/diag_contr_loop_gain.png]]
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% We then close the loop and we look at the transfer function from the Newport rotation signal to the beam angle (Fig. [[fig:diag_contr_effect_newport]]).
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inputs = {'Uch', 'Ucv', 'Unh', 'Unv'};
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outputs = {'Vch', 'Vcv', 'Ich', 'Icv', 'Rh', 'Rv', 'Vph', 'Vpv'};
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sys_cl = connect(sys, -K, inputs, outputs);
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figure;
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hold on;
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set(gca,'ColorOrderIndex',1);
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plot(freqs, abs(squeeze(freqresp(sys('Rh', 'Unh'), freqs, 'Hz'))), '-', 'DisplayName', 'OL - $R_h/U_{n,h}$');
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set(gca,'ColorOrderIndex',1);
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plot(freqs, abs(squeeze(freqresp(sys_cl('Rh', 'Unh'), freqs, 'Hz'))), '--', 'DisplayName', 'CL - $R_h/U_{n,h}$');
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set(gca,'ColorOrderIndex',2);
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plot(freqs, abs(squeeze(freqresp(sys('Rv', 'Unv'), freqs, 'Hz'))), '-', 'DisplayName', 'OL - $R_v/U_{n,v}$');
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set(gca,'ColorOrderIndex',2);
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plot(freqs, abs(squeeze(freqresp(sys_cl('Rv', 'Unv'), freqs, 'Hz'))), '--', 'DisplayName', 'CL - $R_v/U_{n,v}$');
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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xlabel('Frequency [Hz]'); ylabel('Magnitude [dB]');
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hold off;
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xlim([freqs(1), freqs(end)]);
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legend('location', 'southeast');
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141
matlab/frf_processing.m
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141
matlab/frf_processing.m
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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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% Load Plant
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load('mat/plant.mat', 'Gi', 'Zc', 'Ga', 'Gc', 'Gn', 'Gd');
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% Test
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bodeFig({Ga*Zc*Gi}, struct('phase', true));
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% TODO Huddle Test
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% We load the data taken during the Huddle Test.
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load('mat/data_huddle_test.mat', ...
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't', 'Uch', 'Ucv', ...
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'Unh', 'Unv', ...
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'Vph', 'Vpv', ...
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'Vch', 'Vcv', ...
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'Vnh', 'Vnv', ...
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'Va');
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% We remove the first second of data where everything is settling down.
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t0 = 1;
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Uch(t<t0) = [];
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Ucv(t<t0) = [];
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Unh(t<t0) = [];
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Unv(t<t0) = [];
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Vph(t<t0) = [];
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Vpv(t<t0) = [];
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Vch(t<t0) = [];
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Vcv(t<t0) = [];
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Vnh(t<t0) = [];
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Vnv(t<t0) = [];
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Va(t<t0) = [];
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t(t<t0) = [];
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t = t - t(1); % We start at t=0
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figure;
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hold on;
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plot(t, Vph, 'DisplayName', '$Vp_h$');
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plot(t, Vpv, 'DisplayName', '$Vp_v$');
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hold off;
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xlabel('Time [s]');
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ylabel('Amplitude [V]');
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xlim([t(1), t(end)]);
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legend();
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% We compute the Power Spectral Density of the horizontal and vertical positions of the beam as measured by the 4 quadrant diode.
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[psd_Vph, f] = pwelch(Vph, hanning(ceil(1*fs)), [], [], fs);
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[psd_Vpv, ~] = pwelch(Vpv, hanning(ceil(1*fs)), [], [], fs);
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figure;
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hold on;
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plot(f, sqrt(psd_Vph), 'DisplayName', '$\Gamma_{Vp_h}$');
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plot(f, sqrt(psd_Vpv), 'DisplayName', '$\Gamma_{Vp_v}$');
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hold off;
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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xlabel('Frequency [Hz]'); ylabel('ASD $\left[\frac{V}{\sqrt{Hz}}\right]$')
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legend('Location', 'southwest');
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xlim([1, 1000]);
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figure;
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hold on;
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plot(t, Vch, 'DisplayName', '$Vc_h$');
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plot(t, Vcv, 'DisplayName', '$Vc_v$');
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hold off;
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xlabel('Time [s]');
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ylabel('Amplitude [V]');
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xlim([t(1), t(end)]);
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legend();
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% We compute the Power Spectral Density of the voltage across the inductance used for horizontal and vertical positioning of the Cercalo.
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[psd_Vch, f] = pwelch(Vch, hanning(ceil(1*fs)), [], [], fs);
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[psd_Vcv, ~] = pwelch(Vcv, hanning(ceil(1*fs)), [], [], fs);
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figure;
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hold on;
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plot(f, sqrt(psd_Vch), 'DisplayName', '$\Gamma_{Vc_h}$');
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plot(f, sqrt(psd_Vcv), 'DisplayName', '$\Gamma_{Vc_v}$');
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hold off;
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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xlabel('Frequency [Hz]'); ylabel('ASD $\left[\frac{V}{\sqrt{Hz}}\right]$')
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legend('Location', 'southwest');
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xlim([1, 1000]);
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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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% Load Plant
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load('mat/plant.mat', 'G');
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% RGA-Number
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freqs = logspace(2, 4, 1000);
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G_resp = freqresp(G, freqs, 'Hz');
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A = zeros(size(G_resp));
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RGAnum = zeros(1, length(freqs));
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for i = 1:length(freqs)
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A(:, :, i) = G_resp(:, :, i).*inv(G_resp(:, :, i))';
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RGAnum(i) = sum(sum(abs(A(:, :, i)-eye(2))));
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end
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% RGA = G0.*inv(G0)';
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figure;
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plot(freqs, RGAnum);
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set(gca, 'xscale', 'log');
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U = zeros(2, 2, length(freqs));
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S = zeros(2, 2, length(freqs))
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V = zeros(2, 2, length(freqs));
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for i = 1:length(freqs)
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[Ui, Si, Vi] = svd(G_resp(:, :, i));
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U(:, :, i) = Ui;
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S(:, :, i) = Si;
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V(:, :, i) = Vi;
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end
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% Rotation Matrix
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G0 = freqresp(G, 0);
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@@ -1,341 +0,0 @@
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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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% Excitation Data
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fs = 1e4;
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Ts = 1/fs;
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% We generate white noise with the "random number" simulink block, and we filter that noise.
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Gi = (1)/(1+s/2/pi/100);
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c2d(Gi, Ts, 'tustin')
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% Input / Output data
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% The identification data is loaded
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ux = load('mat/data_ux.mat', 't', 'ux', 'yx', 'yy');
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uy = load('mat/data_uy.mat', 't', 'uy', 'yx', 'yy');
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% We remove the first seconds where the Cercalo is turned on.
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i0x = 20*fs;
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i0y = 10*fs;
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ux.t = ux.t( i0x:end) - ux.t(i0x);
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ux.ux = ux.ux(i0x:end);
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ux.yx = ux.yx(i0x:end);
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ux.yy = ux.yy(i0x:end);
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uy.t = uy.t( i0y:end) - uy.t(i0x);
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uy.uy = uy.uy(i0y:end);
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uy.yx = uy.yx(i0y:end);
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uy.yy = uy.yy(i0y:end);
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ux.ux = ux.ux-mean(ux.ux);
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ux.yx = ux.yx-mean(ux.yx);
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ux.yy = ux.yy-mean(ux.yy);
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uy.ux = uy.ux-mean(uy.ux);
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uy.yx = uy.yx-mean(uy.yx);
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uy.yy = uy.yy-mean(uy.yy);
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figure;
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ax1 = subplot(1, 2, 1);
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plot(ux.t, ux.ux);
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xlabel('Time [s]');
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ylabel('Amplitude [V]');
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legend({'$u_x$'});
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ax2 = subplot(1, 2, 2);
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hold on;
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plot(ux.t, ux.yx, 'DisplayName', '$y_x$');
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plot(ux.t, ux.yy, 'DisplayName', '$y_y$');
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hold off;
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xlabel('Time [s]');
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ylabel('Amplitude [V]');
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legend()
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linkaxes([ax1,ax2],'x');
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xlim([ux.t(1), ux.t(end)])
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% #+NAME: fig:identification_ux
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% #+CAPTION: Identification signals when exciting the $x$ axis ([[./figs/identification_ux.png][png]], [[./figs/identification_ux.pdf][pdf]])
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% [[file:figs/identification_ux.png]]
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figure;
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ax1 = subplot(1, 2, 1);
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plot(uy.t, uy.uy);
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xlabel('Time [s]');
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ylabel('Amplitude [V]');
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legend({'$u_y$'});
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ax2 = subplot(1, 2, 2);
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hold on;
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plot(uy.t, uy.yy, 'DisplayName', '$y_y$');
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plot(uy.t, uy.yx, 'DisplayName', '$y_x$');
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hold off;
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xlabel('Time [s]');
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ylabel('Amplitude [V]');
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legend()
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linkaxes([ax1,ax2],'x');
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xlim([uy.t(1), uy.t(end)])
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% Estimation of the Frequency Response Function Matrix
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% We compute an estimate of the transfer functions.
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[tf_ux_yx, f] = tfestimate(ux.ux, ux.yx, hanning(ceil(1*fs)), [], [], fs);
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[tf_ux_yy, ~] = tfestimate(ux.ux, ux.yy, hanning(ceil(1*fs)), [], [], fs);
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[tf_uy_yx, ~] = tfestimate(uy.uy, uy.yx, hanning(ceil(1*fs)), [], [], fs);
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[tf_uy_yy, ~] = tfestimate(uy.uy, uy.yy, hanning(ceil(1*fs)), [], [], fs);
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figure;
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ax11 = subplot(2, 2, 1);
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hold on;
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plot(f, abs(tf_ux_yx))
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title('Frequency Response Function $\frac{y_x}{u_x}$')
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set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
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ylabel('Amplitude')
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hold off;
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ax12 = subplot(2, 2, 2);
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hold on;
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plot(f, abs(tf_uy_yx))
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title('Frequency Response Function $\frac{y_x}{u_y}$')
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set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
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hold off;
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ax21 = subplot(2, 2, 3);
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hold on;
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plot(f, abs(tf_ux_yy))
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title('Frequency Response Function $\frac{y_y}{u_x}$')
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set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
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ylabel('Amplitude')
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xlabel('Frequency [Hz]')
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hold off;
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ax22 = subplot(2, 2, 4);
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hold on;
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plot(f, abs(tf_uy_yy))
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title('Frequency Response Function $\frac{y_y}{u_y}$')
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set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
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xlabel('Frequency [Hz]')
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hold off;
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linkaxes([ax11,ax12,ax21,ax22],'x');
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xlim([10, 1000]);
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linkaxes([ax11,ax12,ax21,ax22],'y');
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ylim([1e-2, 1e3])
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% Coherence
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[coh_ux_yx, f] = mscohere(ux.ux, ux.yx, hanning(ceil(1*fs)), [], [], fs);
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[coh_ux_yy, ~] = mscohere(ux.ux, ux.yy, hanning(ceil(1*fs)), [], [], fs);
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[coh_uy_yx, ~] = mscohere(uy.uy, uy.yx, hanning(ceil(1*fs)), [], [], fs);
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[coh_uy_yy, ~] = mscohere(uy.uy, uy.yy, hanning(ceil(1*fs)), [], [], fs);
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figure;
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ax11 = subplot(2, 2, 1);
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hold on;
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plot(f, coh_ux_yx)
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set(gca, 'Xscale', 'log');
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title('Coherence $\frac{y_x}{u_x}$')
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ylabel('Coherence')
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hold off;
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ax12 = subplot(2, 2, 2);
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hold on;
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plot(f, coh_uy_yx)
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set(gca, 'Xscale', 'log');
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title('Coherence $\frac{y_x}{u_y}$')
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hold off;
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ax21 = subplot(2, 2, 3);
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hold on;
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plot(f, coh_ux_yy)
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set(gca, 'Xscale', 'log');
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title('Coherence $\frac{y_y}{u_x}$')
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ylabel('Coherence')
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xlabel('Frequency [Hz]')
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hold off;
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ax22 = subplot(2, 2, 4);
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hold on;
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plot(f, coh_uy_yy)
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set(gca, 'Xscale', 'log');
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title('Coherence $\frac{y_y}{u_y}$')
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xlabel('Frequency [Hz]')
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hold off;
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linkaxes([ax11,ax12,ax21,ax22],'x');
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xlim([10, 1000]);
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linkaxes([ax11,ax12,ax21,ax22],'y');
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ylim([0, 1])
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% Extraction of a transfer function matrix
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% First we define the initial guess for the resonance frequencies and the weights associated.
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freqs_res = [410, 250]; % [Hz]
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freqs_res_weights = [10, 10]; % [Hz]
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% From the number of resonance frequency we want to fit, we define the order =N= of the system we want to obtain.
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N = 2*length(freqs_res);
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% We then make an initial guess on the complex values of the poles.
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xi = 0.001; % Approximate modal damping
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poles = [2*pi*freqs_res*(xi + 1i), 2*pi*freqs_res*(xi - 1i)];
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||||
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||||
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% We then define the weight that will be used for the fitting.
|
||||
% Basically, we want more weight around the resonance and at low frequency (below the first resonance).
|
||||
% Also, we want more importance where we have a better coherence.
|
||||
|
||||
weight = ones(1, length(f));
|
||||
% weight = G_coh';
|
||||
|
||||
% alpha = 0.1;
|
||||
|
||||
% for freq_i = 1:length(freqs_res)
|
||||
% weight(f>(1-alpha)*freqs_res(freq_i) & omega<(1 + alpha)*2*pi*freqs_res(freq_i)) = freqs_res_weights(freq_i);
|
||||
% end
|
||||
|
||||
|
||||
|
||||
% Ignore data above some frequency.
|
||||
|
||||
weight(f>1000) = 0;
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(f, weight);
|
||||
plot(freqs_res, ones(size(freqs_res)), 'rx');
|
||||
hold off;
|
||||
xlabel('Frequency [Hz]');
|
||||
xlabel('Weight Amplitude');
|
||||
set(gca, 'xscale', 'log');
|
||||
xlim([f(1), f(end)]);
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:weights
|
||||
% #+CAPTION: Weights amplitude ([[./figs/weights.png][png]], [[./figs/weights.pdf][pdf]])
|
||||
% [[file:figs/weights.png]]
|
||||
|
||||
% When we set some options for =vfit3=.
|
||||
|
||||
opts = struct();
|
||||
|
||||
opts.stable = 1; % Enforce stable poles
|
||||
opts.asymp = 1; % Force D matrix to be null
|
||||
opts.relax = 1; % Use vector fitting with relaxed non-triviality constraint
|
||||
opts.skip_pole = 0; % Do NOT skip pole identification
|
||||
opts.skip_res = 0; % Do NOT skip identification of residues (C,D,E)
|
||||
opts.cmplx_ss = 0; % Create real state space model with block diagonal A
|
||||
|
||||
opts.spy1 = 0; % No plotting for first stage of vector fitting
|
||||
opts.spy2 = 0; % Create magnitude plot for fitting of f(s)
|
||||
|
||||
|
||||
|
||||
% We define the number of iteration.
|
||||
|
||||
Niter = 5;
|
||||
|
||||
|
||||
|
||||
% An we run the =vectfit3= algorithm.
|
||||
|
||||
for iter = 1:Niter
|
||||
[SER_ux_yx, poles, ~, fit_ux_yx] = vectfit3(tf_ux_yx.', 1i*2*pi*f, poles, weight, opts);
|
||||
end
|
||||
for iter = 1:Niter
|
||||
[SER_uy_yx, poles, ~, fit_uy_yx] = vectfit3(tf_uy_yx.', 1i*2*pi*f, poles, weight, opts);
|
||||
end
|
||||
for iter = 1:Niter
|
||||
[SER_ux_yy, poles, ~, fit_ux_yy] = vectfit3(tf_ux_yy.', 1i*2*pi*f, poles, weight, opts);
|
||||
end
|
||||
for iter = 1:Niter
|
||||
[SER_uy_yy, poles, ~, fit_uy_yy] = vectfit3(tf_uy_yy.', 1i*2*pi*f, poles, weight, opts);
|
||||
end
|
||||
|
||||
figure;
|
||||
ax11 = subplot(2, 2, 1);
|
||||
hold on;
|
||||
plot(f, abs(tf_ux_yx))
|
||||
plot(f, abs(fit_ux_yx))
|
||||
title('Frequency Response Function $\frac{y_x}{u_x}$')
|
||||
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
|
||||
ylabel('Amplitude')
|
||||
hold off;
|
||||
|
||||
ax12 = subplot(2, 2, 2);
|
||||
hold on;
|
||||
plot(f, abs(tf_uy_yx))
|
||||
plot(f, abs(fit_uy_yx))
|
||||
title('Frequency Response Function $\frac{y_x}{u_y}$')
|
||||
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
|
||||
hold off;
|
||||
|
||||
ax21 = subplot(2, 2, 3);
|
||||
hold on;
|
||||
plot(f, abs(tf_ux_yy))
|
||||
plot(f, abs(fit_ux_yy))
|
||||
title('Frequency Response Function $\frac{y_y}{u_x}$')
|
||||
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
|
||||
ylabel('Amplitude')
|
||||
xlabel('Frequency [Hz]')
|
||||
hold off;
|
||||
|
||||
ax22 = subplot(2, 2, 4);
|
||||
hold on;
|
||||
plot(f, abs(tf_uy_yy))
|
||||
plot(f, abs(fit_uy_yy))
|
||||
title('Frequency Response Function $\frac{y_y}{u_y}$')
|
||||
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
|
||||
xlabel('Frequency [Hz]')
|
||||
hold off;
|
||||
|
||||
linkaxes([ax11,ax12,ax21,ax22],'x');
|
||||
xlim([10, 1000]);
|
||||
linkaxes([ax11,ax12,ax21,ax22],'y');
|
||||
ylim([1e-2, 1e3])
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:identification_matrix_fit
|
||||
% #+CAPTION: Transfer Function Extraction of the FRF matrix ([[./figs/identification_matrix_fit.png][png]], [[./figs/identification_matrix_fit.pdf][pdf]])
|
||||
% [[file:figs/identification_matrix_fit.png]]
|
||||
|
||||
% And finally, we create the identified state space model:
|
||||
|
||||
G_ux_yx = minreal(ss(full(SER_ux_yx.A),SER_ux_yx.B,SER_ux_yx.C,SER_ux_yx.D));
|
||||
G_uy_yx = minreal(ss(full(SER_uy_yx.A),SER_uy_yx.B,SER_uy_yx.C,SER_uy_yx.D));
|
||||
G_ux_yy = minreal(ss(full(SER_ux_yy.A),SER_ux_yy.B,SER_ux_yy.C,SER_ux_yy.D));
|
||||
G_uy_yy = minreal(ss(full(SER_uy_yy.A),SER_uy_yy.B,SER_uy_yy.C,SER_uy_yy.D));
|
||||
|
||||
G = [G_ux_yx, G_uy_yx;
|
||||
G_ux_yy, G_uy_yy];
|
||||
|
||||
save('mat/plant.mat', 'G');
|
||||
98
matlab/run_test.m
Normal file
98
matlab/run_test.m
Normal file
@@ -0,0 +1,98 @@
|
||||
tg = slrt;
|
||||
|
||||
%%
|
||||
if tg.Connected == "Yes"
|
||||
if tg.Status == "stopped"
|
||||
%% Load the application
|
||||
tg.load('test_cercalo');
|
||||
|
||||
%% Run the application
|
||||
tg.start;
|
||||
pause(101);
|
||||
tg.stop;
|
||||
end
|
||||
end
|
||||
|
||||
%%
|
||||
f = SimulinkRealTime.openFTP(tg);
|
||||
mget(f, 'data/data_001.dat');
|
||||
close(f);
|
||||
|
||||
%% Convert the Data
|
||||
data = SimulinkRealTime.utils.getFileScopeData('data/data_001.dat').data;
|
||||
|
||||
Uch = data(:, 1);
|
||||
Ucv = data(:, 2);
|
||||
Unh = data(:, 3);
|
||||
Unv = data(:, 4);
|
||||
Vph = data(:, 5);
|
||||
Vpv = data(:, 6);
|
||||
Vch = data(:, 7);
|
||||
Vcv = data(:, 8);
|
||||
Vnh = data(:, 9);
|
||||
Vnv = data(:, 10);
|
||||
Va = data(:, 11);
|
||||
t = data(:, end);
|
||||
|
||||
%% Plot the data
|
||||
figure;
|
||||
subplot(2, 3, 1);
|
||||
hold on;
|
||||
plot(t, Ucv, 'DisplayName', 'Ucv');
|
||||
plot(t, Vpv, 'DisplayName', 'Vpv');
|
||||
hold off
|
||||
xlabel('Time [s]');
|
||||
ylabel('Voltage [V]');
|
||||
legend();
|
||||
|
||||
subplot(2, 3, 2);
|
||||
hold on;
|
||||
plot(t, Uch, 'DisplayName', 'Uch');
|
||||
plot(t, Vph, 'DisplayName', 'Vph');
|
||||
hold off
|
||||
xlabel('Time [s]');
|
||||
ylabel('Voltage [V]');
|
||||
legend();
|
||||
|
||||
subplot(2, 3, 3);
|
||||
hold on;
|
||||
plot(t, Vch, 'DisplayName', 'Vch');
|
||||
plot(t, Vcv, 'DisplayName', 'Vcv');
|
||||
hold off
|
||||
xlabel('Time [s]');
|
||||
ylabel('Voltage [V]');
|
||||
legend();
|
||||
|
||||
subplot(2, 3, 4);
|
||||
hold on;
|
||||
plot(t, Unh, 'DisplayName', 'Unh');
|
||||
plot(t, Vnh, 'DisplayName', 'Vnh');
|
||||
hold off
|
||||
xlabel('Time [s]');
|
||||
ylabel('Voltage [V]');
|
||||
legend();
|
||||
|
||||
subplot(2, 3, 5);
|
||||
hold on;
|
||||
plot(t, Unv, 'DisplayName', 'Unv');
|
||||
plot(t, Vnv, 'DisplayName', 'Vnv');
|
||||
hold off
|
||||
xlabel('Time [s]');
|
||||
ylabel('Voltage [V]');
|
||||
legend();
|
||||
|
||||
subplot(2, 3, 6);
|
||||
hold on;
|
||||
plot(t, Va, 'DisplayName', 'Va');
|
||||
hold off
|
||||
xlabel('Time [s]');
|
||||
ylabel('Distance [m]');
|
||||
legend();
|
||||
|
||||
%% Save
|
||||
save('mat/data_cal_pd_v.mat', 't', 'Uch', 'Ucv', ...
|
||||
'Unh', 'Unv', ...
|
||||
'Vph', 'Vpv', ...
|
||||
'Vch', 'Vcv', ...
|
||||
'Vnh', 'Vnv', ...
|
||||
'Va');
|
||||
1
matlab/setup.m
Normal file
1
matlab/setup.m
Normal file
@@ -0,0 +1 @@
|
||||
Ts = 1e-4; % [s]
|
||||
BIN
matlab/test_bench.slx
Normal file
BIN
matlab/test_bench.slx
Normal file
Binary file not shown.
BIN
matlab/test_cercalo.slx
Normal file
BIN
matlab/test_cercalo.slx
Normal file
Binary file not shown.
Reference in New Issue
Block a user