## Control = 0:  step test
  ## Control = 1:  PPP open-loop
  ## Control = 2:  PPP closed-loop
  ## w is the (constant) setpoint
  ## par_control and par_observer are structures containing parameters
  ## for the observer and controller

  disp("Special version");

  ##Defaults
  if nargin<1			# Default name to dir name
    names = split(pwd,"/");
    [n_name,m_name] = size(names);
    Name = deblank(names(n_name,:));
  endif

	  p_c.A_u = ppp_aug(0,p_c.A_u);
	endif
      endif
    else
      error(sprintf("Method %s not recognised", p_c.Method));
    endif
  endif

  if !struct_contains(p_c,"tau") # Time horizon
    if strcmp(p_c.Method,"lq")
      p_c.tau = [0:0.1:1]*2;
    elseif strcmp(p_c.Method,"original");
      p_c.tau = [10:0.1:11];
    else
      error(sprintf("Method %s not recognised", p_c.Method));

  endif
  
  ## Initialise
  x_est = p_o.x_0;

  ## Initialise simulation state
  x = x_0;
  y_i = C*x_0

  if ControlType==0		# Step input
    I = 1;			# 1 large sample
    p_c.delta_ol = p_c.T;	# I
    K_w = zeros(p_c.n_U,n_y);
    K_w(1,1) = 1;
    K_w(2,1) = -1;

  
  ## Initial control U
  U = zeros(p_c.n_U,1)	;

  ## Short sample interval
  dt = p_c.delta_ol/p_c.N;


  ## Observer design
  G = eye(n_x);		# State noise gain 
  sigma_x = eye(n_x);		# State noise variance
  Sigma = p_o.sigma*eye(n_y)	# Measurement noise variance
  
  if strcmp(p_o.method, "intermittent")
    Ad = expm(A*p_c.delta_ol);		# Discrete-time transition matrix

  tick = time;
  i=0;

  for j=1:p_c.Iterations
    for k=1:I
      tim=time;			# Timing
      i++;

      if Simulate		# Exact simulation 
	X = x;			# Current (simulated) state







      else			# The real thing
	if strcmp(p_o.method, "remote")
	  [t_i,y_i,u_i,X] = ppp_put_get_X(U); # Remote-state interface
	else
	  [t_i,y_i,u_i] = ppp_put_get(U); # Generic interface to real-time
	endif
      endif

	  Ui = A_ud'*Ui;
	  y_e = [y_e; y_new'];
	  e_e = [e_e; e_est'];
	endfor
      elseif strcmp(p_o.method, "remote")
	## predict from remote state (with zero L)	
	if (ControlType==2)	# Closed-loop
    	  [x_est y_est y_new e_est] = ppp_int_obs \
    	      (X,y_i,U,A,B,C,D,p_c.A_u,p_c.delta_ol,zeros(n_x,1));
	##  x_est = X; y_est=y_i; y_new=y_i; e_est=0;
	else			# Open-loop
	  [x_est y_est y_new e_est] = ppp_int_obs \
	      (x_est,y_i,U,A,B,C,D,p_c.A_u,p_c.delta_ol,zeros(n_x,1));
	endif
      endif
      
      ## Simulation (based on U_i)
      if Simulate
	t_sim = [1:p_c.N]*dt;	# Simulation time points
	[yi,ui,xsi] = ppp_ystar(A,B,C,D,x,p_c.A_u,U,t_sim); # Simulate
	x = xsi(:,p_c.N);	# NEXT state
	ti  = [(i-1)*p_c.N:i*p_c.N-1]*dt; 
	y_i = yi(1);	# Current output
	t_i = ti(1);
      endif

      ##Control
      if ( length(p_c.Tau_u)==0&&length(p_c.Tau_y)==0 )
	U = K_w*w - K_x*x_est;
      else
	## Input constraints 
	[Gamma_u, gamma_u] = \
	    ppp_input_constraints(p_c.A_u,p_c.Tau_u,p_c.Min_u,p_c.Max_u);

	Gamma = [Gamma_u; Gamma_y];
	gamma = [gamma_u; gamma_y];
	
	[u_qp,U,n_active] = ppp_qp \
	    (x_est,w,J_uu,J_ux,J_uw,Us0,Gamma,gamma,1e-6,1);
      endif

      ## Allow for the delay
      ##U = expm(p_c.delta_ol*p_c.A_u)*U;

      ## Save data
      if Simulate
	t = [t;ti'];
	y = [y;yi'];
	X_est = [X_est;x_est'];
	y_c = [y_c;(C_c*xsi)'];
	u = [u;ui'];