ga_ex631.m

书籍代码:遗传演算法原理与应用_活用MATLAB(Source Code)

% GA_ex631.m file
% Find scaling factors of a Fuzzy control system by using GA

% PenChen Chou, 8-4-2001

%**********************************************************/
%       User can modify the following in blocks
%**********************************************************/
global MIN_offset MUL_factor LOCUS x_data y_data 
global Emin MDL Se Sde Su ex631f FT

ex631f=readfis('ex631f');
MDL='motor_control'; eval(MDL); FT=0.5; 
MIN_offset=9e3; LOCUS=0; x_data=[]; y_data=[];
MUL_factor=5;
Emin=MIN_offset;
obj_fcn = 'GA_f631';	% Objective function
UB=45*ones(1,3); LB=0.01*ones(1,3);
range = [LB;UB];
IC=[];elite=1;
gen_no=30;  popuSize=30;
bit_n=40; xover_rate=1; mutate_rate=0.05;
%************************************************************
% calling GA 
tic
% function [popu, popu_real, fcn_value, upper, average,...
%   lower, BEST_popu, popuSize, gen_no, para]=GA_genetic(obj_fcn,...
%   range, IC, elite, gen_no, popuSize, bit_n, xover_rate,...
%   mutate_rate);
[popu, popu_real, fcn_value, upper, average, lower, ...
    BEST_popu, popuSize, gen_no, para] = GA_genetic(obj_fcn,...
    range, IC, elite, gen_no, popuSize, bit_n, xover_rate,...
    mutate_rate);
t=toc/60;

fprintf('==>  Computation time is (%.2f) minutes.\n',t);            
            
% Show the result using parameters found by GA
Se=para(1);
Sde=para(2);
Su=para(3);
[t, X, Y]=sim(MDL,[0:0.01:0.5]);
in=Y(:,2); x1=Y(:,3); u=Y(:,4); y=[in x1];
figure(2);subplot(211);plot(t,y);
title('GA based Motor speed command and output responses(RPM.)');
xlabel(['Time(s), Se=' num2str(Se) ',Sde=' num2str(Sde) ',Su=' num2str(Su)]);
figure(2);subplot(212);plot(t,[u]);
title('Control effort');