chap5_3f.m

关于神经网络算法在MATLAB中对典型二阶对象进行仿真

function[Kpidi,BsJ]=pid_gaf(Kpidi,BsJ)
global rin yout timef

ts=0.001;
sys=tf(500,[1,20,1],'inputdelay',500);

dsys=c2d(sys,ts,'z');
[num,den]=tfdata(dsys,'v');

rin=1.0;
u_1=0.0;u_2=0.0;
y_1=0.0;y_2=0.0;
x=[0,0,0]';
B=0;
error_1=0;
tu=1;
s=0;
P=100;

for  k=1:1:P
    timef(k)=k*ts;
    r(k)=rin;
    u(k)=Kpidi(1)*x(1)+Kpidi(2)*x(2)+Kpidi(3)* x(3);
    if u(k)>=10
        u(k)=10;
   
    end
    
    yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2;
    error(k)=r(k)-yout(k);
    
    u_2=u_1;u_1=u(k);
y_2=y_1;y_1=yout(k);

x(1)=error(k);
x(2)=(error(k)-error_1)/ts;
x(3)=x(3)+error(k)*ts;

error_2=error_1;
error_1=error(k);
if s==0
    if yout(k)>0.95&yout(k)<1.05
        tu=timef(k);
        s=1;
    end
end
end

for i=1:1:P
    Ji(i)=0.999*abs(error(i))+0.01*u(i)^2*0.1;
    B=B+Ji(i);
    if i>1
            B=B+100*abs(erry(i));
        end
    end
end
BsJ=B+0.2*tu*10;