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Comment:Made output of U and parameters consistent.
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SHA3-256: e9bb3a65498ab6ca4b229809db8a447d7ee8468fddedd50972889545d21107f2
User & Date: gawthrop@users.sourceforge.net on 2002-05-23 17:27:06
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Context
2002-05-24
11:04:10
Removed unnecessary message about *.log file not existing when -D option is used. check-in: 0ff1595929 user: geraint@users.sourceforge.net tags: origin/master, trunk
2002-05-23
17:27:06
Made output of U and parameters consistent. check-in: e9bb3a6549 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
17:26:07
Cosmetic tweaks to graphs check-in: 6964cc60f6 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
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Modified mttroot/mtt/lib/control/PPP/ppp_nlin_run.m from [df20d4cf0c] to [76fba90674].

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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));

  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;



  ## 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 = [];

  t_last = 0;
  UU = [];
  UU_l =[];
  UU_c =[];
  
  t_ppp = [];
  t_est = [];
  its_ppp = [];
  its_est = [];
  t_open = [];
  x_nexts = zeros(2*n_x,1);

  ## Initial U is zero
  [n_U,junk] = size(A_u);
  U = zeros(n_U,1);




  ## Create input basis functions
  u_star_tau = ppp_ustar(A_u,1,Tau',0,0,n_u-n_U);
	
  ## Reverse time to get "previous" U
  U_old = U;









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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 = [];



  t_last = 0;

  UU_l =[];
  UU_c =[];
  
  t_ppp = [];
  t_est = [];
  its_ppp = [];
  its_est = [];
  t_open = [];
  x_nexts = zeros(2*n_x,1);

  ## Initial U is zero
  [n_U,junk] = size(A_u);
  U = zeros(n_U,1);

  ## Initialise saved U
  UU = [];

  ## Create input basis functions
  u_star_tau = ppp_ustar(A_u,1,Tau',0,0,n_u-n_U);
	
  ## Reverse time to get "previous" U
  U_old = U;
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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);








    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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    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'];

	## Set up according to interval length
	if (T_ol>T_ol_0) ## Truncate data
	  simpar_est.last = T_ol_0;
	  y_est = y_ol(1:n_t+1,:);
	else
	  simpar_est.last = T_ol;








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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)




	## Set up according to interval length
	if (T_ol>T_ol_0) ## Truncate data
	  simpar_est.last = T_ol_0;
	  y_est = y_ol(1:n_t+1,:);
	else
	  simpar_est.last = T_ol;
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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'];
      t = [t; t_ol+t_last*ones(n_ol,1) ];
      t_last = t_last + T_ol;

    endif


    if (extras.simulate==1) # Do the actual simulation








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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];

      t = [t; t_ol+t_last*ones(n_ol,1) ];
      t_last = t_last + T_ol;

    endif


    if (extras.simulate==1) # Do the actual simulation
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