BPPID.rar_BPPID_PID 参数整定_PID 整定_bppid效果好差_神经PID

clear all;close all; %%%%%%%%%%%%%%%%%%%%%系统参数赋初值%%%%%%%%%%%%%%%%%%%%%x xite=0.01;%学习速率 alfa=0.15;%惯性系数 IN=4;H=5;Out=3; %wi=0.50*rands(H,IN);%神经网络权值初始化 wi=[-0.6534 -0.2842 -0.3906 -0.7250; -0.8085 -0.1476 -0.4470 -0.1870; -1.0428 0.5876 -1.6474 -0.4955; -0.2832 0.0095 -0.5620 -0.1779; 0.4151 0.3087 -0.2521 -0.4145]; wi_1=wi; wi_2=wi; wi_3=wi; %wo=0.50*rands(Out,H); wo=[0.7588 0.2628 0.5832 -0.1404 -0.1313; -0.1134 0.2961 0.8364 0.2217 0.4520; 0.7213 0.4578 0.7684 0.4974 0.3644]; wo_1=wo;wo_2=wo;wo_3=wo; x=[0,0,0]; u_1=0.0;u_2=0.0;u_3=0.0;u_4=0;u_5=0;%PID输出赋初值，为方便后面的离散化 y_1=0.0;y_2=0.0;y_3=0.0;y_4=0;y_5=0;%输出给0值 error_1=0;error_2=0;Oh=zeros(H,1); I=Oh; %隐含层输入赋初值 %%%%%%%%%%%%%%%%%%%%%%%%计算%%%%%%%%%%%%%%%%%%%%%%%%%% ts=0.96; for k=1:1:200 time(k)=k*ts; rin(k)=1.0; num=[1];%Unlinearmodel den=[10 1]; sys=tf(num,den,'inputdelay',0.5);%传递函数G(s)离散化 dsys=c2d(sys,ts,'z'); [num,den]=tfdata(dsys,'v'); yout(k)=-den(2)*y_1+num(2)*u_2+num(1)*u_1;%输出y离散化 error(k)=rin(k)-yout(k); xi=[error(k),error_1,error_2,1];%输入层给值 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';%隐含层节点输入 for j=1:1:H Oh(j)=(exp(I(j))-exp(-I(j)))/(exp(I(j))+exp(-I(j))); end%隐含层活化函数tanhx变成了欧拉形式 K=wo*Oh; %输出层节点输入 for l=1:1:Out K(l)=exp(K(l))/(exp(K(l))+exp(-K(l))); %输出层活化函数变成了欧拉氏 求解 end kp(k)=K(1);ki(k)=K(2);kd(k)=K(3);%得到kp,ki,kd参数值 Kpid=[kp(k),ki(k),kd(k)]; du(k)=Kpid*epid;%计算U(k) u(k)=u_1+du(k); if u(k)>=10%Restrictingtheoutputofcontroller u(k)=10; end if u(k)<=-10 u(k)=-10; end %%%%%%%%%%%%%%%%%%%%%反向修正%%%%%%%%%%%%%%%%%%%%%%%%%%输出层 dyu(k)=sign((yout(k)-y_1)/(u(k)-u_1+0.0000001)); for j=1:1:Out dK(j)=2/(exp(K(j))+exp(-K(j)))^2; end for l=1:1:Out delta3(l)=error(k)*dyu(k)*epid(l)*dK(l);%dJ/dWo end for l=1:1:Out for i=1:1:H d_wo=xite*delta3(l)*Oh(i)+alfa*(wo_1-wo_2); end end wo=wo_1+d_wo+alfa*(wo_1-wo_2); %隐含层 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); %数据更新，循环修正 u_5=u_4;u_4=u_3;u_3=u_2;u_2=u_1;u_1=u(k); y_5=y_4;y_4=y_3;y_3=y_2;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 wi%找到较为合适的权值，缩小调整范围 wo %%%%%%%%%%%%%%%%%%%%%%%%绘图%%%%%%%%%%%%%%%%%%%%%%%%%%%% figure(1); plot(time,rin,'r',time,yout,'b'); xlabel('time(s)');ylabel('rin,yout');%被控对象y figure(2); plot(time,error,'r'); xlabel('time(s)');ylabel('error');%误差 figure(3); plot(time,u,'r'); xlabel('time(s)');ylabel('u');%pid输出u figure(4); subplot(311); plot(time,kp,'r'); xlabel('time(s)');ylabel('kp');%ｋｐ，ｋｉ，ｋｄ值 subplot(312); plot(time,ki,'g'); xlabel('time(s)');ylabel('ki'); subplot(313); plot(time,kd,'b'); xlabel('time(s)');ylabel('kd');