Simulation Prototy pe algorithm skeleto n algorit hm 1 Analog-Digit al_conver sion 3 Digit al-Analog-conversion 6 Or ganize-dat a-for -est imat ion Par amet er 工 f tune t hen th_design:=th_est imat Designcalcu lat ions 11 Or ganize_dat a 12 Compute_as_much_as_possible_of cont r sIgna Parameter_update Mola code y(t)/p(t Recursive Least Squares Code (W):=P*EC. ph new(den): =lambda+trans(new(w))*EC. phi; est imat ed new( K): =new(w/de new(EC.e): =EC. val-trans(Ec. phi)*EC. theta; 1 amb da+phig*矿 (EC. thet a) k new(P): =(P-new(K*trans(w) th est imat ed h est imat ed kke (P-可*矿y/den)/1ambd C K J. Ast ro m and B.WittenmarkSimulation 0 20 40 60 80 100 −1 0 1 0 20 40 60 80 100 −4 −2 0 2 Time Time uc y u 0 20 40 60 80 100 0 1 2 0 5 10 15 20 0 1 2 Time Time ^a ^ b a^ ^ b Prototype algorithm Skeleton algorithm 1 Analog_Digital_conversion 2 Compute_control_signal 3 Digital_Analog_conversion 4 If estimate then 5 Filter_data 6 Organize_data_for_estimation 7 Parameter_update 8 If tune then 9 th_design:=th_estimate 10 Design_calculations 11 Organize_data 12 Compute_as_much_as_possible_of_ control_signal| Parameter update Estimated model y(t) = 'T (t) Recursive Least Squares Code eps = y - phi'*th_estimated w = P*phi den = lambda + phi'*w k = w/den th_estimated = th_estimated + k*eps P = (P - w*w'/den)/lambda Omola code new(w):=P*EC.phi; new(den):=lambda+trans(new(w))*EC.phi; new(K):=new(w/den); new(EC.e):=EC.val-trans(EC.phi)*EC.theta; new(EC.theta):=EC.theta+new(K*EC.e); new(P):=(P-new(K*trans(w) c K. J. Åström and B. Wittenmark 8