truck_a.m

FISMAT accommodates different arithmetic operators, fuzzification and defuzzification algorithm, imp

echo off
% truck_a.m
%
% This script runs in conjunction with the model file truck_s and the script
% truck_d.
% This script runs the simulation from workspace and analysis the results.
%
% FSTB - Fuzzy Systems Toolbox for MATLAB
% Copyright (c) 1993-1996 by Olaf Wolkenhauer
% Control Systems Centre at UMIST
% Manchester M60 1QD, UK
%
% 28-May-1994
 
echo off
tstart=0;
tfinal=150;
tol=1e-3;
minstep=0.01;
maxstep=1;
clc
dec=input('Sequence of runs ? y/n [n]: ','s');
if isempty(dec), dec='n'; end;
if dec=='y',
  dec2=input('Saving data in files ? (overwrites previous files) y/n [n]: ','s');
  ip=[20 20;30 20;45 20;50 20;55 20;70 20;80 20]; % initial positions (x,y).
  % number of initial positions:
  nu_of_ip=length(ip);    
  an=[-60 40 140 190 240];       % change of angle phi for each initial position.
  nu_of_an=length(an);
  dec3=input('Using initial positions and angles from script file ? y/n [n]: ','s');
  if isempty(dec3), dec3='n'; end;
  if dec3=='n',
    nu_of_ip=input('Number of initial positions ? : '); disp('');
    disp('Type in a vector with angles, for which each initial ');
    an=input('position is starting. Example: [-60 140 220] : ');
    nu_of_an=length(an);
    ip=zeros(nu_of_ip,2);
    for i=1:nu_of_ip,
      ip(i,1)=input('initial x-position : ');
      ip(i,2)=input('initial y-position : ');
    end;
  end;
  % Run script for preparing the controller:
  truck_d
  echo off
  lcount=zeros(nu_of_ip*nu_of_an,1); % counter for the number of samples per run
  sco=0;                  % simulation counter.
  if dec2=='y',
    [infid,message]=fopen('truck.inf','w');
    if infid==-1, disp(['Error: ',message]); end;
    fprintf(infid,'%g\n',tol);
    fprintf(infid,'%g\n',tfinal);
    fprintf(infid,'%g\n',minstep);
    fprintf(infid,'%g\n',maxstep);
    [datfid,message]=fopen('truck.dat','w');
    if datfid==-1, disp(['Error: ',message]); end;
  end;
  clf
  for i=1:nu_of_ip,
    IXPOS=ip(i,1);
    IYPOS=ip(i,2);
    for j=1:nu_of_an,
      IPHI=an(j);
      x0=[];
      [t,x,y]=rk45('truck_s',[tstart,tfinal],x0,[tol,minstep,maxstep]);
      % Analysis of the results: y(1) = x-position y(2) = y-position
      % y(3) = phi y(4) = theta
      if dec2=='y',
        fprintf(datfid,'%g %g %g %g %g\n',[t';y(:,1)';y(:,2)';y(:,3)';y(:,4)']);
        spl=length(y);
        sco=sco+1;
        lcount(sco)=spl;
        fprintf(infid,'%g  %g  %g  %g\n',IXPOS,IYPOS,IPHI,spl);
      end;
      spl=length(y);
      % docking error: 
      dockerr=sqrt((90-y(spl,3))^2+(50-y(spl,1))^2+(100-y(spl,2))^2);
      trajlen=(t(spl)/Ts)*MOVDIS;
      shortdi=sqrt((IXPOS-50)^2+(IYPOS-100)^2);
      % trajectory error = 
      % length of truck trajectory/distance(initial position,desired final pos.)
      trajerr=trajlen/shortdi; 
      hold on
      plot(y(:,1),y(:,2)); title('loading dock'); axis([0 100 0 100]);
      xlabel(['docking error: ',num2str(dockerr),'     trajectory error: ',num2str(trajerr)]);
      drawnow
      echo on
      disp(['ip ',num2str(i),'  angle ',num2str(j)]);
      echo off
    end; % of loop for different angle with the same initial position.
  end;   % of loop for initial positions.
  hold off
  if dec2=='y',
    fprintf(infid,'%g  %g\n',sco,sum(lcount));
    status=fclose('all');
    if status==-1, disp('Error closing all files. '); end;
    disp('The data file is: truck.dat ');
    disp('Information to these files are in truck.inf')
  end;
  clear x0 nu_of_ip nu_of_an message status datfid trajlen dockerr shortdi
  clear sco spl i j ip an infid lcount t x y dec2 dec3 trajerr
end;                                       % of simulation sequence.

dec=input('Single run ? y/n [n]: ','s');
if isempty(dec)
  dec='n';
end;
if dec=='y',
  % Run script for preparing the controller:
  truck_d
  echo off
  disp(['current initial position: x=',num2str(IXPOS),'  y=',num2str(IYPOS),...
  '  phi=',num2str(IPHI)]);
  IXPOS=input(' x-position : ');
  IYPOS=input(' y-position : ');
  IPHI=input(' phi : ');
  x0=[];
  [t,x,y]=rk45('truck_s',[tstart,tfinal],x0,[tol,minstep,maxstep]);
  % Analysis of the results: y(1) = x-position y(2) = y-position
  % y(3) = phi y(4) = theta
  spl=length(y);
  % docking error: 
  dockerr=sqrt((90-y(spl,3))^2+(50-y(spl,1))^2+(100-y(spl,2))^2);
  trajlen=(t(spl)/Ts)*MOVDIS;
  shortdi=sqrt((IXPOS-50)^2+(IYPOS-100)^2);
  % trajectory error = 
  % length of truck trajectory/distance(initial position,desired final pos.)
  trajerr=trajlen/shortdi;
  clf,plot(y(:,1),y(:,2)); title('loading dock'); axis([0 100 0 100]);
  xlabel(['docking error: ',num2str(dockerr),'     trajectory error: ',num2str(trajerr)]);
  dec=input('Time plot ? y/n [n] : ','s');
  if isempty(dec),
    dec=='n';
  end;
  if dec=='y',
    figure
    subplot(3,1,1),plot(t,y(:,1)),title('controller input x position:'),grid on
    subplot(3,1,2),plot(t,y(:,2)),title('controller input angle phi:'),grid on
    subplot(3,1,3),plot(t,y(:,4)),title('steering angle signal theta:'),grid on
    xlabel(['traveltime : ',num2str(t(spl))]);
  end;
  clear trajerr trajlen shortdi dockerr spl x0
end;               % of single simulation run.

dec=input('Loading sample data in workspace (from files) ? y/n [n]: ','s');
if isempty(dec)
  dec='n';
end;
if dec=='y',
  % The file containing all sample vectors is called: truck.dat .
  [datfid,message]=fopen('truck.dat');
  if datfid==-1, disp(['Error: ',message]); end;
  samples=fscanf(datfid,'%g %g %g %g %g',[5 inf]);
  samples=samples';
  status=fclose(datfid);
  if status==-1,
    [message]=ferror(datfid);
    disp(['Error: ',message]);
  end;
  disp('The sample vectors are now stored in "samples" .');
  clear message datfid status
end; % of loading sample data from files to workspace.
disp(' ');
disp(['There are ',num2str(length(samples)), ' sample vectors.']);
dec=input('Plotting the input data distribution ? y/n [n]: ','s');
if isempty(dec), dec='n'; end;
if dec=='y',
  figure
  axis([-100 250 0 100]); plot(samples(:,4),samples(:,2),'.');
  title('Input data distribution'); xlabel('phi'); ylabel('x');
  figure
  axis([-100 250 0 100 -30 30]); 
  plot3(samples(:,4),samples(:,2),samples(:,5),'.'); grid on
  title('Input data distribution'); xlabel('phi'); ylabel('x'); zlabel('theta');
end;
echo off
dec=input('Product space clustering ? y/n [n]: ','s');
if isempty(dec), dec='n'; end;
if dec=='y',
  nos=input(['Number of training pattern ? [',num2str(length(samples)),'] : ']);
  if isempty(nos), nos=length(samples); end;
  p=input('Number of synaptic vectors ? [245] : ');
  if isempty(p), p=245; end;
  N=input('Number of nearest synaptic vectors or "winners" ? [35] : ');
  if isempty(N), N=35; end;
  wii=input('wii (positive) ? [1] : ');
  if isempty(wii), wii=1; end;
  wij=input('wij (negative) ? [-1] : ');
  if isempty(wij), wij=-1; end;
  c=input('c (c>0) ? [1] : ');
  if isempty(c), c=1; end;
  vectors=[samples(:,2) samples(:,4) samples(:,5)];
  xsec=[0 32.5; 32.5 47.5; 47.5 52.5; 52.5 67.5; 67.5 100];
  phisec=[-90 0; 0 66.5; 66.5 86; 86 94; 94 113.5; 113.5 182.5; 182.5 270];
  thetasec=[-30 -20; -20 -7.5; -7.5 -2.5; -2.5 2.5; 2.5 7.5; 7.5 20; 20 30];
  dec=input('random mix of sample pattern ? y/n [y] : ','s');
  if isempty(dec), dec='y'; end;
  if dec=='y',
    rands=randperm(nos);
  end;
  [FAMbank,msum,y2,m2x,m2p,M,mdens]=truckpsc(xsec,phisec,thetasec,vectors(1:nos,:),N,p,wii,wij,c,rands);
  FAMbank,msum
  figure
  subplot(2,2,1),plot(y2); ylabel('y2');xlabel('training time');
  subplot(2,2,2),plot(m2x); ylabel('M(2,1) = x');
  subplot(2,2,4),plot(m2p); ylabel('M(2,2) = phi'); xlabel('training time');
  subplot(2,2,3),axis([0 1 0 1]);set(gca,'Visible','off'); 
  text(0,0,['wii = ',num2str(wii)]);
  text(0,0.25,['wij = ',num2str(wij)]);
  text(0,.5,['pattern = ',num2str(nos)]);
  text(0,0.75,'ct = 0.1(1-t/pattern)');
  
  mdens=mdens(:).*100./p;
  figure
  subplot(2,2,1); bar(mdens);
  xlabel('cell number'); ylabel('% classification');
  title('synaptic vector histogram');
  
  subplot(2,2,2); 
  axis([xsec(1,1) xsec(length(xsec),2) phisec(1,1) phisec(length(phisec),2)]);
  hold on
  plot(vectors(1:nos,1),vectors(1:nos,2),'.');
  title('pattern distribution'); ylabel('phi');
    
  subplot(2,2,3); axis([0 1 0 1]); set(gca,'Visible','off'); 
  text(0,0.5,['c = ',num2str(c)]);
  text(0,0.75,['p = ',num2str(p)]);
  text(0,0.25,[num2str(N),'  "winner"']);

  subplot(2,2,4); 
  axis([xsec(1,1) xsec(length(xsec),2) phisec(1,1) phisec(length(phisec),2)]);
  set(gca,'XTick',[xsec(:,1);xsec(length(xsec),2)]); 
  set(gca,'XGrid','on');
  set(gca,'YTick',[phisec(:,1);phisec(length(phisec),2)]); 
  set(gca,'YGrid','on');
  hold on; xlabel('x'); ylabel('phi');
  title('Synaptic vectors M'); xlabel('x');ylabel('phi');
  plot(M(1,:),M(2,:),'.'); 
  clear xsec phisec thetasec vectors mdens nos N wii wij c p y2 m2x m2p M msum
end;

clear dec tstart tfinal tol minstep maxstep 
echo on