MATLAB基于BP神经网络PID控制程序

1、知识是人类进步的阶梯MATLAB于BP神经网络PID控制程序- > %BP based PID Control clear all;close all;xite=0.20; % 学习速率alfa=0.01; % 惯性因子IN=4;H=5;Out=3; %NN Structurewi=-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.

2、7660;%wi=0.50*rands(H,IN); %隐含层加权系数wi初始化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); %输出层加权系数wo初始化wo_1=wo;wo_2=wo;wo_3=wo;ts=20; %采样周期取值x=0,0,0; % 比例，积分，微分赋初值u_1=0;u_2=0;u_3=0;u_4=0;u_5=

3、0;y_1=0;y_2=0;y_3=0;Oh=zeros(H,1); %Output from NN middle layer 隐含层的输出 I=Oh; %Input to NN middle layer隐含层输入error_2=0;error_1=0;for k=1:1:500 % 仿真开始，共500步 time(k)=k*ts;知识是人类进步的阶梯rin(k)=1.0;建立被控对象传递函数？ 把传递函数离散化？ 离散化后提取分子、分母%Delay plantsys=tf(1.2,208 1,'inputdelay',80); %dsys=c2d(sys,ts,'zo

4、h');%num,den=tfdata(dsys,'v'); % yout(k)=-den(2)*y_1+num(2)*u_5;error(k)=rin(k)-yout(k);xi=rin(k),yout(k),error(k),1;%经典增量式数字PID的控制算式为：u(k) u(k 1) kp(e(k) e(k 1) kie(k) kd(e(k) 2e(k 1) e(k 2)BP神经网络PID的控制算式为：333u(k) u(k 1) Oi (e(k) e(k 1) O2 e(k) 6 (e(k) 2e(k 1) e(k 2)x(1)=error(k)-error_

5、1; %比例输出x(2)=error(k); %积分输出x(3)=error(k)-2*error_1+error_2; %微分输出epid=x(1);x(2);x(3);I=xi*wi'% 隐含层的输入，即：输入层输入*权值for j=1:1:HOh(j)=(exp(I(j)-exp(-I(j)/(exp(I(j)+exp(-I(j);%Middle Layer 在激活函数作用下隐含层的输出endK=wo*Oh; %Output Layer输出层的输入，即：隐含层的输出 *权值for l=1:1:OutK(l)=exp(K(l)/(exp(K(l)+exp(-K(l); %Getti

6、ng kp,ki,kd输出层的输出，即三个pid控制器的参数endkp(k)=K(1);ki(k)=K(2);kd(k)=K(3);Kpid=kp(k),ki(k),kd(k);du(k)=Kpid*epid;控制器饱和环节u(k)=u_1+du(k)；if u(k)>=10 % Restricting the output of controller知识是人类进步的阶梯u(k)=10; end if u(k)<=-10 u(k)=-10;end%以下为权值 wi、wo的在线调整，参考 刘金琨的先进 PID控制 dyu(k)=sign(yout(k)-y_1)/(u(k)-u_1+

7、0.0000001);%Output layer 输出层for j=1:1:OutdK(j)=2/(exp(K(j)+exp(-K(j)A2;endfor l=1:1:Outdelta3(l)=error(k)*dyu(k)*epid(l)*dK(l); endfor l=1:1:Outfor i=1:1:Hd_wo=xite*delta3(l)*Oh(i)+alfa*(wo_1-wo_2);endendwo=wo_1+d_wo+alfa*(wo_1-wo_2);%Hidden layerfor i=1:1:HdO(i)=4/(exp(I(i)+exp(-I(i)A2;endsegma=del

8、ta3*wo;for i=1:1:Hdelta2(i)=dO(i)*segma(i); endd_wi=xite*delta2'*xi;wi=wi_1+d_wi+alfa*(wi_1-wi_2);%Parameters Update 参数更新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%仿真结束，绘图figure(1)；plot

9、(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,