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Comment:Time-varying set point W
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SHA3-256: 9d59aab3cbda99b98d0fc54cfa12970dd1c7ae01a7fcdab76576ffd9a58e14ea
User & Date: gawthrop@users.sourceforge.net on 2002-09-11 14:19:28
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Context
2002-09-11
14:21:22
large limits set to inf or -inf check-in: 0187760d62 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
14:19:28
Time-varying set point W check-in: 9d59aab3cb user: gawthrop@users.sourceforge.net tags: origin/master, trunk
14:17:36
Modified for new qp_mu algorithms check-in: a6445e6499 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
Changes

Modified mttroot/mtt/lib/control/PPP/ppp_qp_sim.m from [3b08bbc64d] to [9bb563078b].

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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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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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  [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");







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  [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 



  [Gamma_u, gamma_u] = ppp_input_constraints(A_u,Tau_u,Min_u,Max_u);




  ## Output constraints
  [Gamma_y,gamma_y] = ppp_output_constraints(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");
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  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);







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  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_last = 2*t(length(t));
  T_cl = 0:Delta_ol:t_last-Delta_ol; # Closed-loop time vector
  T = 0:dt:t_last;		# Overall time vector
 
  ## Lengths thereof
  n_Tcl = length(T_cl);
  n_ol = length(T_ol);
  n_T = length(T);

  ## Expand W with constant last value or truncate
  [n_W,m_W] = size(W);

  if m_W>n_T
    W = W(:,1:n_T);
  else
    W = [W W(:,m_W)*ones(1,n_T-m_W+1)];
  endif

  ## Compute U*
  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;

  ## disp("Simulating ...");
  for t=T_cl			# Outer loop at Delta_ol

    ##disp(sprintf("Time %g", t));
    ## Output constraints
    [Gamma_y,gamma_y] = ppp_output_constraints  (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];
    
    ## Current Setpoint value
    w = W(:,floor(t/dt)+1);
    
    ## 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);
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  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










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  u = [u ut];		# Save input
  Iterations = [Iterations iterations]; # Save iteration count

  tock = time;
  Elapsed_Time = tock-tick;
  y = C*X + D*u;		# System output



endfunction




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