function [T,y,u,X,Iterations] = ppp_qp_sim (A,B,C,D,A_u,A_w,t,Q, Tau_u,Min_u,Max_u,Order_u, Tau_y,Min_y,Max_y,Order_y, W,x_0,Delta_ol,mu,movie)
## usage: [T,y,u,J] = ppp_qp_sim (A,B,C,D,A_u,A_w,t,Q, Tau_u,Min_u,Max_u,Order_u, Tau_y,Min_y,Max_y,Order_y, W,x_0,movie)
## Needs documentation - see ppp_ex11 for example of use.
## OUTPUTS
## T: Time vector
## y,u,J output, input and cost
## Copyright (C) 1999 by Peter J. Gawthrop
## $Id$
if nargin<19 # No intermittent control
Delta_ol = 0;
endif
if nargin<20 # No movie
mu = 0;
endif
if nargin<21 # No movie
movie = 0;
endif
## Check some sizes
[n_x,n_u,n_y] = abcddim(A,B,C,D);
[n_x0,m_x0] = size(x_0);
if (n_x0 != n_x)||(m_x0 != 1)
error(sprintf("Initial state x_0 must be %ix1 not %ix%i",n_x,n_x0,m_x0));
endif
## Input constraints (assume same on all inputs)
Gamma_u=[];
gamma_u=[];
for i=1:n_u
[Gamma_i,gamma_i] = ppp_input_constraint (A_u,Tau_u,Min_u,Max_u,Order_u,i,n_u);
Gamma_u = [Gamma_u; Gamma_i];
gamma_u = [gamma_u; gamma_i];
endfor
## Output constraints
[Gamma_y,gamma_y] = ppp_output_constraint (A,B,C,D,x_0,A_u,Tau_y,Min_y,Max_y,Order_y);
## Composite constraints - t=0
Gamma = [Gamma_u; Gamma_y];
gamma = [gamma_u; gamma_y];
## Design the controller
## disp("Designing controller");
[k_x,k_w,K_x,K_w,Us0,J_uu,J_ux,J_uw,J_xx,J_xw,J_ww] = ppp_lin (A,B,C,D,A_u,A_w,t,Q);
## Set up various time vectors
dt = t(2)-t(1); # Time increment
## Make sure Delta_ol is multiple of dt
Delta_ol = floor(Delta_ol/dt)*dt;
if Delta_ol>0 # Intermittent control
T_ol = 0:dt:Delta_ol; # Create the open-loop time vector
else
T_ol = [0,dt];
Delta_ol = dt;
endif
T_cl = 0:Delta_ol:t(length(t))-Delta_ol; # Closed-loop time vector
n_Tcl = length(T_cl);
n_ol = length(T_ol);
Ustar_ol = ppp_ustar(A_u,n_u,T_ol); # U* in the open-loop interval
[n,m] = size(Ustar_ol);
n_U = m/length(T_ol); # Determine size of each Ustar
# ## Discrete-time system
# csys = ss2sys(A,B,C,D);
# dsys = c2d(csys,dt);
# [Ad, Bd] = sys2ss(dsys)
x = x_0; # Initialise state
## Initialise the saved variable arrays
X = [];
u = [];
Iterations = [];
du = [];
J = [];
tick= time;
i = 0;
## disp("Simulating ...");
for t=T_cl # Outer loop at Delta_ol
##disp(sprintf("Time %g", t));
## Output constraints
[Gamma_y,gamma_y] = ppp_output_constraint (A,B,C,D,x,A_u,Tau_y,Min_y,Max_y,Order_y);
## Composite constraints
Gamma = [Gamma_u; Gamma_y];
gamma = [gamma_u; gamma_y];
## Compute U(t) via QP optimisation
[uu, U, iterations] = ppp_qp (x,W,J_uu,J_ux,J_uw,Us0,Gamma,gamma,mu); # Compute U
## Compute the cost (not necessary but maybe interesting)
# [J_t] = ppp_cost (U,x,W,J_uu,J_ux,J_uw,J_xx,J_xw,J_ww); # cost
# J = [J J_t];
## OL Simulation (exact)
[ys,us,xs] = ppp_ystar (A,B,C,D,x,A_u,U,T_ol);
## Save values (discarding final ones)
X = [X xs(:,1:n_ol-1)]; # save state
u = [u us(:,1:n_ol-1)]; # save input
Iterations = [Iterations iterations*ones(1,n_ol-1)];
## Final values
x = xs(:,n_ol); # Final state
ut = us(:,n_ol); # Final control
endfor
## Save the last values
X = [X x]; # Save state
u = [u ut]; # Save input
Iterations = [Iterations iterations]; # Save iteration count
tock = time;
Elapsed_Time = tock-tick;
y = C*X + D*u; # System output
T = 0:dt:t+Delta_ol; # Overall time vector
endfunction