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gplearn4quadpaper.m
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% gp modelling results for quadrotor control
% translational and rotational subsystems
clear; clc; close all;
set(0, 'DefaultAxesFontName', 'Arial');
set(0, 'DefaultTextFontName', 'Arial');
mpciterations = 189; % 180 199 170 260
loadType = 'self0109'; % self0109 original
trainsize = 189;
tempRandSequences = randperm(mpciterations);
opt.dsSampleRange = sort(tempRandSequences(1:trainsize));
opt.dsTestRange = 1:mpciterations;
opt.state = 1:9;
opt.in = 7:9;
opt.out = 10:15;
pathtype = 'Lorenz'; % Lorenz Elliptical
subsys = 'rotate'; % trans rotate
switch subsys
case 'trans';
filename = strcat('ds_nmpc_', pathtype, '_trans');
titletxt ={'Translational Subsystem -- Traning Errors',...
'Traning Size = 189'};
ylb = {'X[m]','Y[m]','Z[m]'};
case 'rotate';
filename = strcat('ds_nmpc_', pathtype, '_rotate');
titletxt ={'Rotational Subsystem -- Traning Errors',...
'Traning Size = 189'};
ylb = {'\phi[rad]','\theta[rad]','\psi[rad]'};
end
trainds = loadSampleData(filename,loadType,opt);
dynmodel.inputs = trainds.proData(opt.dsSampleRange,opt.state);
dynmodel.targets = trainds.proData(opt.dsSampleRange,opt.out);
dynmodel.train = @train;
dynmodel. iNum =3;
dynmodel. sNum =6;
dynmodel. oNum =6;
tic
plant = [];
[dynmodel nlml] = dynmodel.train(dynmodel, plant, 1000);
toc % counting dynamic model learning time
traindata.inputs = trainds.proData(opt.dsSampleRange,opt.state);
traindata.targets = trainds.proData(opt.dsSampleRange,opt.out);
testdata.inputs = trainds.proData(opt.dsTestRange,opt.state);
testdata.targets = trainds.proData(opt.dsTestRange,opt.out);
S0size = dynmodel. sNum + dynmodel. iNum;
Ssize = dynmodel. sNum;
s= diag(ones(1,Ssize)*0.1.^2);
s0= zeros(S0size,S0size,trainsize);
for i=1:trainsize
s0(1:Ssize,1:Ssize,i)=s;
[trainpred(i,:) s]= gp0d(dynmodel, traindata.inputs(i,:)', s0(:,:,i));
end
switch loadType
case 'self0109';
range1 = trainds.r1;
range2 = trainds.r2;
trainpred = normalmatrix('reverse',trainpred,range1(opt.out,:),range2);
reff = normalmatrix('reverse',traindata.targets,range1(opt.out,:),range2);
case 'Gauss01';
otherwise;
reff = traindata.targets;
end
trainMSE = sum(sum((trainpred-reff).^2))/trainsize
figure(1);
for i=1:3
subplot(3,1,i);
plot(trainpred(:,2*i-1)-reff(:,2*i-1), 'k', 'LineWidth', 1.5);
ylabel(ylb{i});
xlim([0 trainsize]);
if i==1
title(titletxt);
else
end
end
xlabel('sample time k');
s= diag([ones(1,Ssize)*0.1].^2);
s1= zeros(S0size,S0size,mpciterations);
for i=1:mpciterations
s1(1:Ssize,1:Ssize,i)=s;
[testpred(i,:) s]= gp0d(dynmodel, testdata.inputs(i,:)', s1(:,:,i));
end
switch loadType
case 'self0109';
range1 = trainds.r1;
range2 = trainds.r2;
testpred = normalmatrix('reverse',testpred,range1(opt.out,:),range2);
reff = normalmatrix('reverse',testdata.targets,range1(opt.out,:),range2);
case 'Gauss01';
otherwise;
reff = testdata.targets;
end
testMSE = sum(sum((testpred-reff).^2))/mpciterations
figure(2);
labstr = {' X',' Y',' Z'};
for i=1:3
subplot(3,1,i);
j=2*(i-1)+1;
b1=bar(1:mpciterations, reshape(s1(j,j,:), mpciterations,1), 1); hold on;
set(b1,'FaceColor', [0 0 255]/255, 'EdgeColor', [0 0 0]/255);
ylabel(strcat('variances on',labstr{i}));
end
xlabel('sampling time k');
tempvar = sum(reshape(s1(1,1,:), mpciterations,1))+...
sum(reshape(s1(3,3,:), mpciterations,1))+...
sum(reshape(s1(5,5,:), mpciterations,1));
avrgVAR = tempvar/189/3
figure(3);
for i=1:3
subplot(3,1,i);
plot(testpred(:, 2*i-1), 'b-'); hold on;
plot(reff(:,2*i-1), 'k--');
end