switch to ub, lb constraints
since reformulating the constraints A and b are no longer needed, this is easierdiffdrive-costfun
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2408bdf472
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6dd6af2b8c
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@ -30,19 +30,16 @@ function [u_corr, U_corr_history, q_pred] = ucorr(t, q, sim_data)
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if eq(pred_hor, 0)
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return
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elseif eq(pred_hor, 1)
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H = eye(2);
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H = eye(2)*2;
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f = zeros(2,1);
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T_inv = decouple_matrix(q_act, sim_data);
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ut = utrack(t, q_act, sim_data);
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%A = [T_inv; -T_inv];
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A = [eye(2); -eye(2)];
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d = T_inv*ut;
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b = [s_-d;s_+d];
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% solve qp problem
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options = optimoptions('quadprog', 'Display', 'off');
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u_corr = quadprog(H, f, A, b, [],[],[],[],[],options);
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u_corr = quadprog(H, f, [], [], [],[], -s_ - d, s_-d, [], options);
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q_pred = q_act;
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U_corr_history(:,:,1) = u_corr;
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@ -113,34 +110,25 @@ function [u_corr, U_corr_history, q_pred] = ucorr(t, q, sim_data)
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put in matrix (Ax <= b) form
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%}
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% box constrains
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% A becomes sort of block-diagonal
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% A will be at most PREDICTION_HORIZON * 2 * 2 (2: size of T_inv; 2:
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% accounting for T_inv and -T_inv) by PREDICTION_HORIZON (number of
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% vectors in u_corr times the number of elements [2] in each vector)
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b_deq = [];
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% box constraints
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lb = [];
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ub = [];
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for k=1:pred_hor
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T_inv = T_inv_pred(:,:,k);
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u_track = u_track_pred(:,:,k);
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d = T_inv*u_track;
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b_deq = [b_deq; s_ - d; s_ + d];
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lb = [lb; -s_-d];
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ub = [ub; s_-d];
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end
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A_deq = kron(eye(pred_hor), [eye(2); -eye(2)]);
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%A_deq
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%b_deq
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% unknowns
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% squared norm of u_corr. H must be identity,
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% PREDICTION_HORIZON*size(u_corr)
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H = eye(pred_hor*2)*2;
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% no linear terms
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f = zeros(pred_hor*2, 1);
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% solve qp problem
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options = optimoptions('quadprog', 'Display', 'off');
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U_corr = quadprog(H, f, A_deq, b_deq, [],[],[],[],[],options);
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%U_corr = lsqnonlin(@(pred_hor) ones(pred_hor, 1), U_corr_history(:,:,1), [], [], A_deq, b_deq, [], []);
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U_corr = quadprog(H, f, [], [], [],[],lb,ub,[],options);
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% reshape the vector of vectors to be an array, each element being
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% u_corr_j as a 2x1 vector
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