www.pudn.com > nnctrl_v5.zip > imccon.m, change:1997-10-28,size:8706b


% ---------------------------------    IMCCON     -------------------------------- 
% 
%  Program for simulating internal model control of nonlinear processes. 
% 
%  All design parameters must be defined in the file 'imcinit.m' 
% 
%  Programmed by Magnus Norgaard IAU, Technical University of Denmark. 
%  LastEditDate: Oct. 28, 1997 
 
%---------------------------------------------------------------------------------- 
%-------------------         >>>  INITIALIZATIONS  <<<        --------------------- 
%---------------------------------------------------------------------------------- 
 
%>>>>>>>>>>>>>>>>>>>>>>      READ VARIABLES FROM FILE       <<<<<<<<<<<<<<<<<<<<<<< 
clear plot_a plot_b 
global ugl 
imcinit 
eval(['load ' nninv]);                  % Load inverse neural model 
 
 
% >>>>>>>>>>>>>>>>>>>>>>>>   DETERMINE REGRESSOR STRUCTURE   <<<<<<<<<<<<<<<<<<<<<<    
na = NN(1);                             % # of past y's to be used in TDL 
nb = NN(2);                             % # of past u's to be used in TDL 
nk = NN(3);                             % Time delay in system 
nab         = na+sum(nb);               % Number of inputs to each net 
outputs  = 1;                           % # of outputs 
inputs   = nab-1;                       % # of inputs 
phi = zeros(inputs,1);                  % Initialize regressor vector 
 
 
% >>>>>>>>>>>>>>>>>   DETERMINE STRUCTURE OF FORWARD MODEL    <<<<<<<<<<<<<<<<<<< 
eval(['load ' nnforw]);                 % Load forward neural model of system 
hiddenf   = length(NetDeff(1,:));       % Number of hidden neurons 
L_hiddenf = find(NetDeff(1,:)=='L')';   % Location of linear hidden neurons 
H_hiddenf = find(NetDeff(1,:)=='H')';   % Location of tanh hidden neurons 
L_outputf = find(NetDeff(2,:)=='L')';   % Location of linear output neurons 
H_outputf = find(NetDeff(2,:)=='H')';   % Location of tanh output neurons 
y1f       = [zeros(hiddenf,1);1];       % Hidden layer outputs 
yhat      = zeros(outputs,1);           % Network output 
 
 
% >>>>>>>>>>>>>>>>>>   DETERMINE STRUCTURE OF INVERSE MODEL    <<<<<<<<<<<<<<<<<< 
hiddeni    = length(NetDefi(1,:));      % Number of hidden neurons 
L_hiddeni  = find(NetDefi(1,:)=='L')';  % Location of linear hidden neurons 
H_hiddeni  = find(NetDefi(1,:)=='H')';  % Location of tanh hidden neurons 
L_outputi  = find(NetDefi(2,:)=='L')';  % Location of linear output neurons 
H_outputi  = find(NetDefi(2,:)=='H')';  % Location of tanh output neurons 
y1i        = [zeros(hiddeni,1);1];      % Hidden layer outputs 
 
 
%>>>>>>>>>>>>>>>>>>>>>>>        INITIALIZE VARIABLES        <<<<<<<<<<<<<<<<<<<<<< 
% Determine length of polynomials 
nam = length(Am); 
nbm = length(Bm); 
 
% Initialization of past signals 
maxlength = 5;                          % MIGHT BE NECESSARY TO INCREASE maxlen 
ref_old   = zeros(maxlength,1);         % FOR HIGH ORDER SYSTEMS 
y_old     = zeros(maxlength,1); 
ym_old    = zeros(maxlength,1); 
u_old     = zeros(maxlength,1); 
uc_old    = zeros(maxlength,1); 
e_old     = zeros(maxlength,1); 
 
% Initialization of PID parameters 
if strcmp(regty,'pid'), 
  B1 = K*(1+Ts*Wi/2); 
  A1 = Ts*Wi; 
  B2 = (2*Td+Ts)/(2*alf*Td+Ts); 
  A2 = 2*Ts/(2*alf*Td+Ts); 
  I1 = 0; 
  I2 = 0; 
  uimin = -10; uimax = 10; 
end   
 
% Miscellanous initializations 
t = -Ts; 
u = 0; 
y = 0; 
 
% Initialization of Simulink system 
if strcmp(simul,'simulink') 
  simoptions = simset('Solver',integrator,'MaxRows',0); % Set integrator opt. 
  eval(['[sizes,x0] = ' sim_model '([],[],[],0);']);    % Get initial states 
end 
 
% Initialization of data vectors 
ref_data    = zeros(samples,1); 
u_data      = zeros(samples,1); 
y_data      = zeros(samples,1); 
yhat_data   = zeros(samples,1); 
ym_data     = zeros(samples,1); 
t_data      = zeros(samples,1); 
fighandle=progress; 
 
% A predefined vector contains the reference 
if ~(strcmp(refty,'siggener')|strcmp(refty,'none')), 
  eval(['ref_data = ' refty ';']); 
  ref_data=ref_data(:); 
  i=length(ref_data); 
  if i>=samples, 
    ref_data=ref_data(1:samples); 
  else 
    ref_data=[ref_data;ref_data(i)*ones(samples-i,1)]; 
  end 
end 
 
%------------------------------------------------------------------------------ 
%-------------------         >>>   MAIN LOOP   <<<           ------------------ 
%------------------------------------------------------------------------------ 
for i=1:samples, 
  t = t + Ts; 
   
%>>>>>>>>>>>>>>>>>>>>>     GENERATE REFERENCE SIGNAL      <<<<<<<<<<<<<<<<<<<<< 
  if strcmp(refty,'siggener') 
    ref = siggener(t,sq_amp,sq_freq,sin_amp,sin_freq,dc,sqrt(Nvar)); 
  else                  % Predefined reference 
    ref = ref_data(i); 
  end 
 
 
%>>>>>>>>>>>   COMPUTE OUTPUT FROM "DESIRED" MODEL ONE STEP AHEAD  <<<<<<<<<<<< 
  ym = sum(- Am(2:nam)*ym_old(1:nam-1)) + Bm(1:nbm)*ref_old(1:nbm); 
   
 
%>>>>>>>>>>>>>>>>>>>  READ OUTPUT FROM THE PHYSICAL SYSTEM   <<<<<<<<<<<<<<<<<< 
  if strcmp(simul,'simulink') 
    utmp=[t-Ts,u_old(1);t,u_old(1)]; 
    simoptions.InitialState=x0; 
    [time,x0,y] = sim(sim_model,[t-Ts t],simoptions,utmp); 
    x0 = x0(size(x0,1),:)'; 
    y  = y(size(y,1),:)'; 
  elseif strcmp(simul,'matlab') 
    ugl = u_old(1); 
    [time,x] = ode45(mat_model,[t-Ts t],x0); 
    x0 = x(length(time),:)'; 
    eval(['y = ' model_out '(x0);']); 
  end 
 
 
%>>>>>>>>>>>>>>>>>>>>>   NEURAL NET PREDICTION OF OUTPUT   <<<<<<<<<<<<<<<<<<<< 
  phi = [y_old(1:na);u_old(1:nb);1]; 
  h1f = W1f*phi; 
  y1f(H_hiddenf)  = pmntanh(h1f(H_hiddenf)); 
  y1f(L_hiddenf)  = h1f(L_hiddenf);     
  h2f = W2f*y1f; 
  yhat(H_outputf) = pmntanh(h2f(H_outputf)); 
  yhat(L_outputf) = h2f(L_outputf); 
 
  if strcmp(simul,'nnet') 
   y = yhat; 
  end 
 
 
%>>>>>>>>>>>>>>>>>>>>>>     DETERMINE CONTROL SIGNAL      <<<<<<<<<<<<<<<<<<<<<< 
  e = ref - y + yhat; 
 
  % Internal model control 
  if strcmp(regty,'imc'), 
    u1 = sum(Bm*[e;e_old(1:nbm-1)]); 
    u2 = sum(Am(2:nam)*uc_old(1:nam-1)); 
    uc = ( u1 - u2 ) / Am(1); 
 
    phi = [uc;y;y_old(1:na-1);u_old(1:nb-1);1]; 
    h1i = W1i*phi;   
    y1i(H_hiddeni) = pmntanh(h1i(H_hiddeni)); 
    y1i(L_hiddeni) = h1i(L_hiddeni);     
    h2i = W2i*y1i; 
    u(H_outputi)   = pmntanh(h2i(H_outputi)); 
    u(L_outputi)   = h2i(L_outputi); 
     
  % PID controller 
    elseif strcmp(regty,'pid'), 
    e  = ref - y; 
    ui = B1*e + I1; 
    um = ui; 
    if ui<uimin, um=uimin; end 
    if ui>uimax, um=uimax; end 
    u = (um-I2)*B2 + I2; 
    I1 = I1 + (K*e - (ui - um))*A1; 
    I2 = I2 + (um - I2)*A2; 
   
  % No controller 
  else 
     u = ref; 
  end 
  
   
 %>>>>>>>>>>>>>>>>       COPY DATA INTO THE DATA VECTORS        <<<<<<<<<<<<<<< 
  ref_data(i)     = ref; 
  u_data(i)       = u; 
  y_data(i)       = y; 
  yhat_data(i)    = yhat; 
  ym_data(i)      = ym; 
  t_data(i)       = t; 
 
 
%>>>>>>>>>>>>>>>>>>>>>>>         TIME OPDATES          <<<<<<<<<<<<<<<<<<<<<<<< 
  e_old    = shift(e_old,e); 
  y_old    = shift(y_old,y); 
  u_old    = shift(u_old,u); 
  uc_old   = shift(uc_old,uc); 
  ref_old  = shift(ref_old,ref); 
  ym_old   = shift(ym_old,ym); 
 
 
%>>>>>>>>>>>>>>>       PRINT %-AGE OF SIMULATION COMPLETED       <<<<<<<<<<<<<< 
  progress(fighandle,floor(100*i/samples)); 
end 
%------------------------------------------------------------------------------ 
%----------------         >>>   END OF MAIN LOOP   <<<        ----------------- 
%------------------------------------------------------------------------------ 
 
%>>>>>>>>>>>>>>>>>>>>>>            DRAW PLOTS           <<<<<<<<<<<<<<<<<<<<<<< 
figure(gcf);clf 
set(gcf,'DefaultTextInterpreter','none'); 
% Plot A 
  if(exist('plot_a')==1), 
   [a_plots,dummy]=size(plot_a);        % Number of plots in plot A 
   plmat = zeros(samples,a_plots);      % Collect vectors in plmat 
   for nn = 1:a_plots,  
     plmat(:,nn) = eval(plot_a(nn,:));    
   end 
   subplot(2,1,1); 
   plot([0:samples-1],plmat);           % Plot plmat 
   xlabel('Samples'); 
   set(gca,'Xlim',[0 samples-1]);       % Set x-axis 
   if regty(1)=='i', 
     title('Internal model control'); 
   elseif regty(1)=='p', 
     title('Constant gain PID controller'); 
   else 
     title('Open-loop simulation'); 
   end 
   grid on 
   legend(plot_a); 
  end 
   
 % Plot B 
  if(exist('plot_b')==1), 
   [b_plots,dummy]=size(plot_b);        % Number of plots in plot B 
   plmat = zeros(samples,b_plots);      % Collect vectors in plmat 
   for nn = 1:b_plots,  
     plmat(:,nn) = eval(plot_b(nn,:));    
   end 
   subplot(2,1,2); 
   plot([0:samples-1],plmat);           % Plot plmat 
   xlabel('Samples');  
   set(gca,'Xlim',[0 samples-1]);       % Set x-axis 
   grid on 
   legend(plot_b); 
  end 
set(gcf,'DefaultTextInterpreter','tex'); 
subplot(111)