imccon.m

基于神经网络的控制工具箱

% ---------------------------------    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)