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9bb230d7c8
All of these code samples currently are mis-identified in my repositories. I'm donating them to the cause.
58 lines
2.2 KiB
Matlab
58 lines
2.2 KiB
Matlab
data = load_data('jason_adapt.mat');
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t0 = 0;
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t1 = 30;
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t2 = 60;
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t3 = 90;
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dataPlantOne = data(1:t1 / data.Ts);
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dataAdapting = data(t1 / data.Ts:t2 / data.Ts);
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dataPlantTwo = data(t2 / data.Ts:end);
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% k1, k2, k3, k4, tau, zetanm, wnm, zetafs, wfs
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% ron's guess
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guessPlantOne = [4.85, 1.79, 20, 20, 0.2, 0.707, 10, 0.707, 65];
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% best solution from ron's guess
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%guessPlantOne = [4.1129, 2.1327, 52.3747, 48.6997, 0.2, 0.707, 10, 0.707, 65];
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resultPlantOne = find_structural_gains(dataPlantOne, guessPlantOne, 1);
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[yh, fit, x0] = compare(dataPlantOne, resultPlantOne.fit);
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display(sprintf('The self validation VAF is %f.', fit(1, 1, 1)))
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% ron's guess
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guessPlantTwo = [3.36, 9.49, 20, 0, 0.2, 0.707, 10, 0.707, 65];
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% best solution from ron's guess
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%guessPlantTwo = [2.6686, 7.0431, 14.4623, 3.1532, 0.2, 0.707, 10, 0.707, 65];
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resultPlantTwo = find_structural_gains(dataPlantTwo, guessPlantTwo, 5);
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[yh, fit, x0] = compare(dataPlantTwo, resultPlantTwo.fit);
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display(sprintf('The self validation VAF is %f.', fit(1, 1, 1)))
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% compute the slope and offset for each gain for initial guesses
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kP1 = resultPlantOne.fit.par(1:4);
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kP2 = resultPlantTwo.fit.par(1:4);
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gainSlopeOffset = [t1 * eye(4), eye(4); t2 * eye(4), eye(4)] \ [kP1; kP2];
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aux.pars = guessPlantOne; % this only uses tau through wfs
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aux.timeDelay = true;
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aux.plantFirst = 1; % 1 / s
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aux.plantSecond = 5; % 5 / (s + 10)
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% compute the slope and offset of the plant for t1 < t < t2
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plantOneSlopeOffset = [t1, 1; t2, 1] \ [1; 0];
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plantTwoSlopeOffset = [t1, 1; t2, 1] \ [0; 1];
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aux.m = [plantOneSlopeOffset(1); plantTwoSlopeOffset(1)];
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aux.b = [plantOneSlopeOffset(2); plantTwoSlopeOffset(2)];
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%[dx, y] = adapting_structural_model(45, ones(8, 1), 10, gainSlopeOffset, {aux});
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% NOTE: 'FileArgument' has to be a cell array, the is why aux is in
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% brackets. Also, if you pass the parameters in as a vector here, as I have,
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% they get mapped to a structure and your ode file/function must accept each
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% parameter as individual arguments.
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mod = idnlgrey('adapting_structural_model', [1, 1, 8], gainSlopeOffset, ...
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zeros(8, 1), 0, 'FileArgument', {aux}, 'InputName', 'thetac', ...
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'OutputName', 'theta');
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fit = pem(dataAdapting, mod);
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compare(dataAdapting, fit);
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