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function [y,u,t,y_e,t_e] = ppp_lin_run (Name,Simulate,ControlType,w,p_c,p_o)
function [y,u,t,y_e,t_e,e_e] = ppp_lin_run (Name,Simulate,ControlType,w,x_0,p_c,p_o)
## usage: [y,u,t,y_e,t_e] = ppp_lin_run (Name,Simulate,ControlType,w,p_c,p_o);
## usage: [y,u,t,y_e,t_e,e_e] = ppp_lin_run (Name,Simulate,ControlType,w,x_0,p_c,p_o);
##
##
## Linear closed-loop PPP of lego system (and simulation)
##
## Name: Name of system (in mtt terms)
## Simulate = 0: real thing
## Simulate = 1: simulate
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endif
if nargin<3
ControlType = 2;
endif
if nargin<4
w = 1;
w = ones(n_y,1);;
endif
if nargin<5
p_c.N = 10;
x_0 = zeros(n_x,1);
endif
if nargin<6
p_o.sigma = 0.001;
p_c.N = 5;
endif
if nargin<7
# if !struct_contains(p_c,"N")
# p_c.N = 10; # Number of small samples per large sample
# endif
p_c.delta_ol = 1.0; # OL sample interval
p_c.delta_ol = 0.5; # OL sample interval
endif
if !struct_contains(p_c,"T")
p_c.T = 5.0; # Last time point.
endif
if !struct_contains(p_c,"A_w")
p_c.A_w = 0;
if !struct_contains(p_c,"Method")
p_c.Method = "lq";
endif
if !struct_contains(p_c,"A_u")
p_c.N_u = 3;
a_u = 2.0;
p_c.A_u = ppp_aug(p_c.A_w,laguerre_matrix(p_c.N_u-1,a_u));
endif
if !struct_contains(p_c,"Method")
if !struct_contains(p_o,"x_0")
p_c.Method = "lq";
p_c.Q = eye(n_y);
p_c.R = (0.1^2)*eye(n_u);
p_c.N_u = n_x;
[p_c.N_u,M_u] = size(p_c.A_u);
if (p_c.N_u<>M_u)
error("A_u must be square");
endif
x_0 = zeros(n_x,1);
x_est = x_0;
##x(2) = 0.2;
# x(2) = y_0(1);
# x(4) = y_0(2);
else # PPP control
I = ceil(p_c.T/p_c.delta_ol); # Number of large samples
else
I = ceil(p_c.T/p_c.delta_ol) # Number of large samples
if strcmp(p_c.Method, "original")
tau = [10:0.1:11]*(2/a_u); # Time horizons
[k_x,k_w,K_x,K_w] = ppp_lin(A,B,C,D,p_c.A_u,p_c.A_w,tau); # Design
elseif strcmp(p_c.Method, "lq")
elseif strcmp(p_c.Method, "lq") # LQ design
tau = [0:0.001:1.0]*5; # Time horizons
[k_x,k_w,K_x,K_w,Us0,J_uu,J_ux,J_uw,J_xx,J_xw,J_ww,y_u,p_c.A_u] \
= ppp_lin_quad (A,B,C,D,tau,p_c.Q,p_c.R);
else
error(sprintf("Method %s not recognised", p_c.Method));
endif
U = K_w*w; # Initial control U
if (ControlType==2)
G = eye(n_x); # State noise gain
if (ControlType==2) #
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
Sigma = p_o.sigma*eye(n_y) # Measurement noise variance
L = dlqe(Ad,G,C,sigma_x,Sigma)
else
L = zeros(n_x,n_y);
endif
obs_poles = eig(Ad-L*C)
obs_poles = eig(Ad-L*C);
## Short sample interval
dt = p_c.delta_ol/p_c.N;
## Write the include file for the real-time function
disp("Writing Ustar.h");
ppp_ustar2h(ppp_ustar (p_c.A_u, n_u, [0:dt:p_c.delta_ol], 0,0));
## Control loop
y = [];
u = [];
t = [];
y_e = [];
t_e = [];
e_e = [];
tick = time;
for i=1:I
i
if Simulate
t_sim = [0:p_c.N]*dt;
[yi,ui,xsi] = ppp_ystar (A,B,C,D,x,p_c.A_u,U,t_sim);
x = xsi(:,p_c.N+1);
y_now = yi(:,p_c.N+1);
else # The real thing
to_rt(U'); # Send U
data = from_rt(p_c.N); # Receive data
[yi,ui] = convert_data(data);
[yi,ui] = convert_data(data);
y_now = yi(:,p_c.N); # Current output
endif
## Zero-gain (OL) observer with state resetting
[x_est y_est] = ppp_int_obs (x_est,y_now,U,A,B,C,D,p_c.A_u,p_c.delta_ol,L);
# ## Reset states
# x_est(2) = y_now(1); # Position
# x_est(4) = y_now(2)/g_s; # Angle
U = K_w*w- K_x*x_est;
U = K_w*w - K_x*x_est
## Save
ti = [(i-1)*p_c.N:i*p_c.N-1]*dt;
t = [t;ti'];
y = [y;yi(:,1:p_c.N)'];
u = [u;ui(:,1:p_c.N)'];
y_e = [y_e; y_est'];
t_e = [t_e; (i*p_c.N)*dt];
e_e = [e_e; e_est];
endfor
sample_interval = (time-tick)/(I*p_c.N)
## Put data on file (so can use for identification)
filename = sprintf("%s_ident_data.dat",Name);
eval(sprintf("save -ascii %s t y u",filename));
## Plot
gset nokey
title("");
boxed=0;
monochrome=1;
grid;
xlabel("t");
ylabel("y");
figure(1);plot(t,y, t_e,y_e,"+");
# figfig("OL_y","eps",boxed,monochrome);
# figfig("OL_u","eps",boxed,monochrome);
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