1 | function testmpdpqp |
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2 | |
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3 | yalmip('clear') |
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4 | |
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5 | % Model data |
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6 | A = [2 -1;1 0]; |
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7 | B = [1;0]; |
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8 | C = [0.5 0.5]; |
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9 | nx = 2; % Number of states |
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10 | nu = 1; % Number of inputs |
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11 | |
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12 | % Prediction horizon |
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13 | N = 5; |
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14 | % States x(k), ..., x(k+N) |
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15 | x = sdpvar(repmat(nx,1,N),repmat(1,1,N)); |
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16 | % Inputs u(k), ..., u(k+N) (last one not used) u = sdpvar(repmat(nu,1,N),repmat(1,1,N)); |
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17 | u = sdpvar(repmat(nu,1,N),repmat(1,1,N)); |
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18 | |
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19 | F = set([]); |
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20 | obj = 0; |
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21 | for k = N-1:-1:1 |
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22 | % Feasible region |
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23 | F = F + set(-1 < u{k} < 1); |
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24 | F = F + set(-1 < C*x{k} < 1); |
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25 | F = F + set(-5 < x{k} < 5); |
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26 | F = F + set(-1 < C*x{k+1} < 1); |
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27 | F = F + set(-5 < x{k+1} < 5); |
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28 | % Dynamics |
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29 | F = F + set(x{k+1} == A*x{k}+B*u{k}); |
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30 | % Cost in value iteration |
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31 | obj = obj + x{k}'*x{k} + u{k}'*u{k}; |
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32 | end |
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33 | |
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34 | [sol{k},diagnost{k},Uz{k},J{k},Optimizer{k}] = solvemp(F,obj,[],x{k},u{k}); |
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35 | assign(x{k},[1;0.5]) |
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36 | |
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37 | mbg_asserttrue(diagnost{1}.problem == 0); |
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38 | mbg_asserttolequal(double(J{k}),3.82456140350877,1e-5); |
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39 | mbg_asserttolequal(double(Optimizer{k}),-0.99122807017544,1e-5); |
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40 | assign(x{k},[0;1.7]) |
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41 | mbg_asserttolequal(double(Optimizer{k}),1,1e-5); |
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42 | mbg_asserttolequal(double(J{k}),6.19300000000000,1e-5); |
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43 | |
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44 | |
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