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Comment:Replaced Euler integration by exact solution via ppp_ystar.
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SHA3-256: 40caf743c8552fc7c1b44459d2389b73187960600c32ff1ed0b1f9cddcaadc85
User & Date: gawthrop@users.sourceforge.net on 2002-08-27 12:33:40
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Context
2002-08-27
16:09:01
Added port_name to error message when multiple bonds are near a port. check-in: c7b42b1371 user: geraint@users.sourceforge.net tags: origin/master, trunk
12:33:40
Replaced Euler integration by exact solution via ppp_ystar. check-in: 40caf743c8 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
10:48:29
Corrected documentation check-in: f60511b828 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
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Modified mttroot/mtt/lib/control/PPP/ppp_qp_sim.m from [442bf78425] to [3484a100c1]. 

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function [T,y,u,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


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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
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  ## 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-dt;	# Create the open-loop time vector
  else
    T_ol = 0;
    Delta_ol = dt;
  endif
  
  T_cl = 0:Delta_ol:t(length(t))-Delta_ol; # Closed-loop time vector
  n_Tcl = length(T_cl);

  

  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 = [];








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  ## 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 = [];
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    ## 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)
    [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];

    ## Simulation loop
    i_ol = 0;
    for t_ol=T_ol		# Inner loop at dt

      ## Compute ol control
      i_ol = i_ol+1;
      range = (i_ol-1)*n_U + 1:i_ol*n_U; # Extract current U*
      ut = Ustar_ol(:,range)*U;	# Compute OL control (U* U)

      ## Simulate the system
      i = i+1;
      X = [X x];		# Save state
      u = [u ut];		# Save input
      Iterations = [Iterations iterations]; # Save iteration count
      x = Ad*x + Bd*ut;	# System

#       if movie			# Plot the moving horizon
# 	tau = T(1:n_T-i);	# Tau with moving horizon
# 	tauT = T(i+1:n_T);	# Tau with moving horizon + real time
# 	[ys,us,xs,xu,AA] = ppp_ystar (A,B,C,D,x,A_u,U,tau); # OL response
# 	plot(tauT,ys, tauT(1), ys(1), "*")
#       endif


    endfor
  endfor
  
  ## Save the last values
  X = [X x];		# Save state
  u = [u ut];		# Save input
  Iterations = [Iterations iterations]; # Save iteration count









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    ## 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
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MTT: Model Transformation Tools
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