GA.rar_遗传算法代码

%Single Neural Adaptive Controller clear all; close all; x=[0,0,0]'; xite=0.40; w1_1=0.10; w2_1=0.10; w3_1=0.10; e_1=0; e_2=0; y_1=0;y_2=0; u_1=0;u_2=0; ts=0.001; for k=1:1:1000 time(k)=k*ts; r(k)=0.5*sign(sin(2*2*pi*k*ts)); y(k)=0.368*y_1+0.26*y_2+0.1*u_1+0.632*u_2; e(k)=r(k)-y(k); %Adjusting Weight Value by supervised Heb learning algorithm w1(k)=w1_1+xite*e(k)*u_1*x(1); w2(k)=w2_1+xite*e(k)*u_1*x(2); w3(k)=w3_1+xite*e(k)*u_1*x(3); K=0.12; x(1)=e(k)-e_1; x(2)=e(k); x(3)=e(k)-2*e_1+e_2; w=[w1(k),w2(k),w3(k)]; u(k)=u_1+K*w*x; %Control law e_2=e_1; e_1=e(k); u_2=u_1;u_1=u(k); y_2=y_1;y_1=y(k); w1_1=w1(k); w2_1=w2(k); w3_1=w3(k); end figure(1); plot(time,r,'b',time,y,'r'); xlabel('time(s)');ylabel('Position tracking'); figure(2); plot(time,e,'r'); xlabel('time(s)');ylabel('error'); figure(3); plot(time,w1,'r'); xlabel('time(s)');ylabel('w1'); figure(4); plot(time,w2,'r'); xlabel('time(s)');ylabel('w2'); figure(5); plot(time,w3,'r'); xlabel('time(s)');ylabel('w3');