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Comment:Updated to use qp_mu (Adrian Wills/Will Heath)
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SHA3-256: 5ed5baf95700f7707eabcbf4e1559b442969182ae72295a5ee449564fe3ccf0f
User & Date: gawthrop@users.sourceforge.net on 2002-08-26 10:12:55
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Context
2002-08-27
10:48:29
Corrected documentation check-in: d0e482a408 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
2002-08-26
10:12:55
Updated to use qp_mu (Adrian Wills/Will Heath) check-in: 5ed5baf957 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
2002-08-23
09:13:58
Sensitivity version check-in: 8553f17d95 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
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Modified mttroot/mtt/lib/control/PPP/ppp_qp.m from [e94ddba2d2] to [519bc8a390]. 

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function [u,U,J] = ppp_qp (x,W,J_uu,J_ux,J_uw,Us0,Gamma,gamma)
function [u,U,iterations] = ppp_qp (x,W,J_uu,J_ux,J_uw,Us0,Gamma,gamma,mu)

  ## usage:  [u,U] = ppp_qp (x,W,J_uu,J_ux,J_uw,Gamma,gamma)
  ## INPUTS:
  ##      x: system state    
  ##      W: Setpoint vector
  ##      J_uu,J_ux,J_uw: Cost derivatives (see ppp_lin)
  ##      Us0: value of U* at tau=0 (see ppp_lin)
  ##      Gamma, gamma: U constrained by Gamma*U <= gamma 
  ##      mu  Parameter of qp_mu
  ## Outputs:
  ##      u: control signal
  ##      U: control weight vector
  ##
  ## Predictive pole-placement of linear systems using quadratic programming
  ## Use ppp_input_constraint and ppp_output_constraint to generate Gamma and gamma
  ## Use ppp_lin to generate J_uu,J_ux,J_uw
  ## Use ppp_cost to evaluate resultant cost function

  ## Copyright (C) 1999 by Peter J. Gawthrop
  ## 	$Id$	

  if nargin<9
    mu = 0
  endif


  ## Check the sizes
  n_x = length(x);

  [n_U,m_U] = size(J_uu);
  if n_U != m_U
    error("J_uu must be square");
  endif

  [n,m] = size(J_ux);
  if (n != n_U)||(m != n_x)
    error("J_ux should be %ix%i not %ix%i",n_U,n_x,n,m);
  endif


  if length(gamma)>0		# Constraints exist: do the QP algorithm 
    U = qp(J_uu,(J_ux*x - J_uw*W),Gamma,gamma); # QP solution for weights U
    #U = pd_lcp04(J_uu,(J_ux*x - J_uw*W),Gamma,gamma); # QP solution for weights U
  if length(gamma)>0		# Constraints exist: do the QP algorithm
    [U,iterations] = qp_mu(J_uu,(J_ux*x - J_uw*W),Gamma,gamma,mu); # QP solution for weights U	

    ##U = qp(J_uu,(J_ux*x - J_uw*W),Gamma,gamma); # QP solution for weights U
    ##U = pd_lcp04(J_uu,(J_ux*x - J_uw*W),Gamma,gamma); # QP solution for weights U
    u = Us0*U;			# Control signal
  else			# Do the unconstrained solution
    ## Compute the open-loop gains
    K_w = J_uu\J_uw;
    K_x = J_uu\J_ux;

    ## Closed-loop control
    U = K_w*W - K_x*x;		# Basis functions weights - U(t)
    u = Us0*U;			# Control u(t)
  endif

endfunction

Modified mttroot/mtt/lib/control/PPP/ppp_qp_sim.m from [58fd63cdfa] to [442bf78425]. 

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function [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,Delta_ol,movie)
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

  ## 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));
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  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
  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
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  [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)
    [uu U] = ppp_qp (x,W,J_uu,J_ux,J_uw,Us0,Gamma,gamma); # Compute U
    [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

  tock = time;
  Iterations = length(T_cl)
  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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