## Figures.m ## Makes figures for the rc_PPP exasmple. ## $Log$ system_name = "rcPPP"; ## Uncomment the following the first time ## (Or do ./Make rcPPP in this directory) ## MTT stuff for the system simulation ##system("Make rcPPP"); t_s=[0:0.1:10]; u = [ones(1,length(t_s));ones(1,length(t_s))]; x_0 = rcPPP_state; par = rcPPP_numpar; ## Simulate the system tick=time; [y,x] = rcPPP_sim(x_0,u,t_s,par); Elapsed = time-tick plot(t_s,y,t_s,x); ## Simulate the system to give just the final few point t_s1 = [9:0.1:10]; tick=time; [y,x] = rcPPP_sim(x_0,u,t_s1,par); Elapsed = time-tick plot(t_s1,y,t_s1,x); ## Sensitivity system simulation parameters x_0s = srcPPP_state; pars = srcPPP_numpar; sympars = srcPPP_sympar; ## Simulate the sensitivity system sensitivities = [sympars.ppp_1s,sympars.ppp_2s,sympars.rs] tick=time; [y,ys] = srcPPP_sim(x_0s,u,t_s,[sympars.r,2.0],sensitivities); Elapsed = time-tick plot(t_s,y,t_s,ys); ### PPP parameters A_w = 0; A_u = ppp_aug(A_w,laguerre_matrix(1,10)); # Specify basis functions: constant & exp(-5t) tau = [0.9:0.01:1]; # Optimisation interval t_ol = [0:0.01:0.2]; # Open-loop interval N = 5; # Number of open-loop intervals in simulation w = 1; # Setpoint ## Linear system [A,B,C,D] = rcPPP_sm; Q = 1; w = 1; ppp_lin_plot (A,B(:,1),C(1,:),D(1,1),A_u,A_w,tau,Q,w,x_0); psfig("rcPPP_lin"); ## Simulate non-linear PPP (on this linear system) extras.U_initial = "zero"; extras.U_next = "continuation"; extras.criterion = 1e-5; extras.max_iterations = 10; extras.alpha = 0.1; extras.verbose = 0; ## -- with no optimisation using linear PPP with continuation extras.U_initial = "linear"; extras.U_next = "continuation"; extras.criterion = 1e-5; extras.max_iterations = 0; [y_c,x,u_c,t,U,U_c,U_l] = ppp_nlin_sim (system_name,A_u,tau,t_ol,N,w,extras); ## -- with no optimisation using linear PPP at each step extras.U_initial = "linear"; extras.U_next = "linear"; extras.criterion = 1e-5; extras.max_iterations = 0; [y_l,x,u_l,t,U,U_c,U_l] = ppp_nlin_sim (system_name,A_u,tau,t_ol,N,w,extras); ## -- with no optimisation using linear PPP with continuation extras.U_initial = "zero"; extras.U_next = "continuation"; extras.criterion = 1e-5; extras.max_iterations = 100; [y,x,u,t,U,U_c,U_l] = ppp_nlin_sim (system_name,A_u,tau,t_ol,N,w,extras); ## Plots title(""); ## U, U_c and U_l I = 1:N; IU1 = [I' U(1,:)']; IU1_c = [I' U_c(1,:)']; IU1_l = [I' U_l(1,:)']; gset grid; xlabel "Interval" gplot IU1 title "U_1", IU1_c title "U_c1", IU1_l title "U_l1" psfig("rcPPP_U1"); IU2 = [I' U(2,:)']; IU2_c = [I' U_c(2,:)']; IU2_l = [I' U_l(2,:)']; gset grid; xlabel "Interval " gplot IU2 title "U_2", IU2_c title "U_c2", IU2_l title "U_l2" psfig("rcPPP_U2"); ## y & u gset grid; xlabel "Time (sec)" ty = [t' y'] ; tu = [t' u']; gplot ty title "Output", tu title "Input" psfig("rcPPP_yu"); title(""); gset grid; xlabel "Time (sec)" ty_c = [t' y_c'] ; ty_l = [t' y_l'] ; ty = [t' y'] ; tu = [t' u']; gplot ty_c title "Continuation", ty_l title "Linear", ty title "Optimisation" psfig("rcPPP_ylco");