diff --git a/control_act.m b/control_act.m index b0f17a3..1ff786b 100644 --- a/control_act.m +++ b/control_act.m @@ -31,9 +31,10 @@ function [u_corr, U_corr_history, q_pred] = ucorr(t, q, sim_data) elseif eq(pred_hor, 1) T_inv = decouple_matrix(q_act, sim_data); ut = utrack(t, q_act, sim_data); + + % minimize v + H = 2*eye(2); - H = 2 * (T_inv') * T_inv; - %H = eye(2); f = zeros(2,1); A = [T_inv; -T_inv]; %A = [eye(2); -eye(2)]; @@ -121,19 +122,14 @@ function [u_corr, U_corr_history, q_pred] = ucorr(t, q, sim_data) % vectors in u_corr times the number of elements [2] in each vector) A_deq = []; b_deq = []; - H1 = []; for k=1:pred_hor T_inv = T_inv_pred(:,:,k); u_track = u_track_pred(:,:,k); d = T_inv*u_track; - H1 = blkdiag(H1, T_inv); - H2 = blkdiag(H2, T_inv'); A_deq = blkdiag(A_deq, [T_inv; -T_inv]); b_deq = [b_deq; s_ - d; s_ + d]; end - - H = H1'*H1; %A_deq = kron(eye(pred_hor), [eye(2); -eye(2)]); %A_deq @@ -143,7 +139,8 @@ function [u_corr, U_corr_history, q_pred] = ucorr(t, q, sim_data) % squared norm of u_corr. H must be identity, % PREDICTION_HORIZON*size(u_corr) %H = eye(pred_hor*2)*2; - %H = kron(eye(pred_hor), [1,0;0,0]); + H = kron(eye(pred_hor), 2*eye(2)); + %H = kron(eye(pred_hor), 2*ones(2,2)); % no linear terms f = zeros(pred_hor*2, 1); diff --git a/tesi.m b/tesi.m index 27483a9..54c551b 100644 --- a/tesi.m +++ b/tesi.m @@ -8,6 +8,7 @@ ROBOT = 'diffdrive' TESTS = ["circle/start_center"] % main + s_ = size(TESTS); for i = 1:length(TESTS) clearvars -except i s_ TESTS ROBOT @@ -24,7 +25,6 @@ for i = 1:length(TESTS) sim_data.(fn{1}) = test_data.(fn{1}); end - sim_data.r = 0.06 % set trajectory and starting conditions sim_data.q0 = set_initial_conditions(sim_data.INITIAL_CONDITIONS); [ref dref] = set_trajectory(sim_data.TRAJECTORY, sim_data);