nn_pid.m

基于神经网络的PID控制不是用神经网络来整定PID的参数

%BP based PID Control
clear all;
close all;

xite=0.25;
alfa=0.05;
S=0;
S=input('请选择输入波形（阶跃为1，正弦为2，斜坡为3）：');   %Signal type
while((S~=1)&(S~=2)&(S~=3))
    Message='S is invalible!'
    S=input('请选择输入波形（阶跃为1，正弦为2，斜坡为3）：');
end
IN=4;H=5;Out=3;   %NN Structure
if ((S==1)|(S==3))   %Step Signal
    wi=[-0.6394    -0.2696    -0.3756    -0.7023;
        -0.8603    -0.2013    -0.5024    -0.2596;
        -1.0749     0.5543    -1.6820    -0.5437;
        -0.3625    -0.0724    -0.6463    -0.2859;
         0.1425     0.0279    -0.5406    -0.7660];
     %wi=0.50*rands(H,IN);
    wi_1=wi;wi_2=wi;wi_3=wi;
    wo=[ 0.7576     0.2616     0.5820    -0.1416    -0.1325;
        -0.1146     0.2949     0.8352     0.2205     0.4508;
         0.7201     0.4566     0.7672     0.4962     0.3632];
    %wo=0.50*rands(Out,H);
    wo_1=wo;wo_2=wo;wo_3=wo;
end

if S==2   %Sine Signal
    wi=[-0.2846     0.2193    -0.5097    -1.0668;
        -0.7484    -0.1210    -0.4708     0.0988;
        -0.7176     0.8297    -1.6000     0.2049;
        -0.0858     0.1925    -0.6346     0.0347;
         0.4358     0.2369    -0.4564    -0.1324];
    %wi=0.50*rands(H,IN)
    wi_1=wi;wi_2=wi;wi_3=wi;
    wo=[ 1.0438     0.5478     0.8682     0.1446     0.1537;
         0.1716     0.5811     1.1214     0.5067     0.7370;
         1.0063     0.7428     1.0534     0.7824     0.6494];
    %wo=0.50*rands(Out,H)
    wo_1=wo;wo_2=wo;wo_3=wo;
end

x=[0,0,0];
u_1=0;u_2=0;u_3=0;u_4=0;u_5=0;
y_1=0;y_2=0;y_3=0;

Oh=zeros(H,1);   %Output from NN middle layer
I=Oh;     %Input to NN middle layer
error_2=0;
error_1=0;

ts=0.001;
for k=1:1:10000
    time(k)=k*ts;
    
    if S==1
        rin(k)=1;
    elseif S==2
        rin(k)=sin(1*2*pi*k*ts);
    else
        rin(k)=k*ts;
    end
    
    %Unlinear model
    %a(k)=1.2*(1-0.8*exp(-0.1*k));
    yout(k)= y_1/(1+y_1^2)+u_1;
    
    error(k)=rin(k)-yout(k);
    
    xi=[rin(k),yout(k),error(k),1];
    
    x(1)=error(k)-error_1;
    x(2)=error(k);
    x(3)=error(k)-2*error_1+error_2;
    
    epid=[x(1);x(2);x(3)];
    I=xi*wi';
    for j=1:1:H
        Oh(j)=(exp(I(j)-exp(-I(j))))/(exp(I(j))+exp(-I(j)));   %Middle Layer
    end
    K=wo*Oh;    %Output Layer
    for l=1:1:Out
        K(1)=exp(K(l))/(exp(K(l))+exp(-K(l)));   %Getting kp,ki,kd
    end
    kp(k)=K(1);ki(k)=K(2);kd(k)=K(3);
    Kpid=[kp(k),ki(k),kd(k)];
    
    du(k)=Kpid*epid;
    u(k)=u_1+du(k);
    if u(k)>=10   %Restricting the output of controller
        u(k)=10;
    end
    if u(k)<=-10
        u(k)=-10;
    end
    
    dyu(k)=sign((yout(k)-y_1)/(u(k)-u_1+0.0000001));
    
    %Output layer
    for j=1:1:Out
        dK(j)=2/(exp(K(j))+exp(-K(j)))^2;
    end
    for l=1:1:Out
        delta3(1)=error(k)*dyu(k)*epid(l)*dK(l);
    end
    
    for l=1:1:Out
        for i=1:1:H
            d_wo=xite*delta3(1)*Oh(i)+alfa*(wo_1-wo_2);
        end
    end
    wo=wo_1+d_wo+alfa*(wo_1-wo_2);
    %Hidden layer
    for i=1:1:H
        dO(i)=4/(exp(I(i))+exp(-I(i)))^2;
    end
    segma=delta3*wo;
    for i=1:1:H
        delta2(i)=dO(i)*segma(i);
    end
    
    d_wi=xite*delta2'*xi;
    wi=wi_1+d_wi+alfa*(wi_1-wi_2);
    %Parameters Update
    u_5=u_4;u_4=u_3;u_3=u_2;u_2=u_1;u_1=u(k);
    y_2=y_1;y_1=yout(k);
    
    wo_3=wo_2;
    wo_2=wo_1;
    wo_1=wo;
    
    wi_3=wi_2;
    wi_2=wi_1;
    wi_1=wi;
    
    error_2=error_1;
    error_1=error(k);
end
figure(1);
plot(time,rin,'r',time,yout,'b');
xlabel('time(s)');ylabel('rin,yout');
figure(2);
plot(time,error,'r');
xlabel('time(s)');ylabel('error');
figure(3);
plot(time,u,'r');
xlabel('time(s)');ylabel('u');
figure(4);
subplot(311);
plot(time,kp,'r');
xlabel('time(s)');ylabel('kp');
subplot(312);
plot(time,ki,'g');
xlabel('time(s)');ylabel('ki');
subplot(313);
plot(time,kd,'b');
xlabel('time(s)');ylabel('kd');