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simpar_model = simpar; # Initial internal model simulation params
simpar_pred = simpar; # Initial prediction simulation params
T_ol_0 = simpar.last; # The initial specified interval
n_t = round(simpar.last/simpar.dt); # Corresponding length
x_0 = eval(sprintf("%s_state(par);", system_name));
x_0_model = x_0;
[n_x,n_y,n_u] = eval(sprintf("%s_def;", system_name));
[n_par,m_par] = size(i_par);
if nargin<8
Q = ones(n_y,1);
endif
## Sensitivity system details -- defines moving horizon simulation
simpars = eval(sprintf("%s_simpar;", s_system_name));
pars = eval(sprintf("%s_numpar;", s_system_name));
x_0s = eval(sprintf("%s_state(pars);", s_system_name));
x_0_models = x_0s;
p = []; # Initialise saved parameters
## Times
## -- within opt horizon
n_Tau = round(simpars.last/simpars.dt);
dtau = simpars.dt;
Tau = [0:n_Tau-1]'*dtau;
[n_tau,n_w] = size(w_s);
tau = Tau(n_Tau-n_tau+1:n_Tau);
w = w_s(n_tau,:); # Final value of setpoint
## Main simulation loop
y = [];
x = [];
u = [];
t = [];
p = [];
UU = [];
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A_u_sim = A_u;
simpar_sim = simpar;
T_total = simpar.last;
endif
for i = 0:N_ol # Main loop
printf("%i",i);
UU = [UU; U']; # Save control U
if n_par>0
par_est = pars(i_par(:,1));
p = [p; par_est']; # Save up the estimated parameters
endif
if (extras.simulate==1)
[y_ol,u_ol] = ppp_RT_sim(U); # Simulate
else
[y_ol,u_ol] = ppp_RT(U); # Real thing
endif
t_start = time; # Initialise time
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t_open = [t_open;T_ol];
## Generate input to actual system
u_star_t = ppp_ustar(A_u,1,t_ol',0,0,n_u-n_U);
## Tune parameters/states
if (estimating_parameters==1)
## Save up the estimated parameters
par_est = pars(i_par(:,1));
p = [p; par_est'];
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pars(i_ppp(:,1)) = U; # Put final value of U into the parameter vector
## Save up data
y_ol = y_ol(1:n_ol,:);
y = [y; y_ol];
u = [u; u_ol];
UU = [UU; U'];
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