gusucode.com > MATLAB神经网络多个案例分析及详细源代码 > 源程序/案例6 PID神经元网络解耦控制算法_多变量系统控制/MPIDCS.m
web browser http://www.ilovematlab.cn/thread-62563-1-1.html %% 清空环境变量 clc clear %% 网络结构初始化 rate1=0.006;rate2=0.001; %学习率 k=0.3;K=3; y_1=zeros(3,1);y_2=y_1;y_3=y_2; %输出值 u_1=zeros(3,1);u_2=u_1;u_3=u_2; %控制率 h1i=zeros(3,1);h1i_1=h1i; %第一个控制量 h2i=zeros(3,1);h2i_1=h2i; %第二个控制量 h3i=zeros(3,1);h3i_1=h3i; %第三个空置量 x1i=zeros(3,1);x2i=x1i;x3i=x2i;x1i_1=x1i;x2i_1=x2i;x3i_1=x3i; %隐含层输出 ki=1.5;kp=1;kd=10; %权值初始化 k0=0.03; %第一层权值 w11=k0*rand(3,2); w12=k0*rand(3,2); w13=k0*rand(3,2); %第二层权值 w21=k0*rand(1,9); w22=k0*rand(1,9); w23=k0*rand(1,9); %值限定 ynmax=1;ynmin=-1; %系统输出值限定 xpmax=1;xpmin=-1; %P节点输出限定 qimax=1;qimin=-1; %I节点输出限定 qdmax=1;qdmin=-1; %D节点输出限定 uhmax=1;uhmin=-1; %输出结果限定 %% 网络迭代优化 for k=1:1:200 %% 控制量输出计算 %--------------------------------网络前向计算-------------------------- %系统输出 y1(k)=(0.4*y_1(1)+u_1(1)/(1+u_1(1)^2)+0.2*u_1(1)^3+0.5*u_1(2))+0.3*y_1(2); y2(k)=(0.2*y_1(2)+u_1(2)/(1+u_1(2)^2)+0.4*u_1(2)^3+0.2*u_1(1))+0.3*y_1(3); y3(k)=(0.3*y_1(3)+u_1(3)/(1+u_1(3)^2)+0.4*u_1(3)^3+0.4*u_1(2))+0.3*y_1(1); r1(k)=0.7;r2(k)=0.4;r3(k)=0.6; %控制目标 %系统输出限制 yn=[y1(k),y2(k),y3(k)]; yn(find(yn>ynmax))=ynmax; yn(find(yn<ynmin))=ynmin; %输入层输出 x1o=[r1(k);yn(1)];x2o=[r2(k);yn(2)];x3o=[r3(k);yn(3)]; %隐含层 x1i=w11*x1o; x2i=w12*x2o; x3i=w13*x3o; %比例神经元P计算 xp=[x1i(1),x2i(1),x3i(1)]; xp(find(xp>xpmax))=xpmax; xp(find(xp<xpmin))=xpmin; qp=kp*xp; h1i(1)=qp(1);h2i(1)=qp(2);h3i(1)=qp(3); %积分神经元I计算 xi=[x1i(2),x2i(2),x3i(2)]; qi=[0,0,0];qi_1=[h1i(2),h2i(2),h3i(2)]; qi=qi_1+xi; qi(find(qi>qimax))=qimax; qi(find(qi<qimin))=qimin; QI=ki*qi; h1i(2)=QI(1);h2i(2)=QI(2);h3i(2)=QI(3); %微分神经元D计算 xd=[x1i(3),x2i(3),x3i(3)]; qd=[0 0 0]; xd_1=[x1i_1(3),x2i_1(3),x3i_1(3)]; qd=kd*(xd-xd_1); qd(find(qd>qdmax))=qdmax; qd(find(qd<qdmin))=qdmin; h1i(3)=qd(1);h2i(3)=qd(2);h3i(3)=qd(3); %输出层计算 wo=[w21;w22;w23]; qo=[h1i',h2i',h3i'];qo=qo'; uh=wo*qo; uh(find(uh>uhmax))=uhmax; uh(find(uh<uhmin))=uhmin; u1(k)=uh(1);u2(k)=uh(2);u3(k)=uh(3); %控制律 %% 网络权值修正 %--------------------------------------网络反馈修正---------------------- %计算误差 error=[r1(k)-y1(k);r2(k)-y2(k);r3(k)-y3(k)]; error1(k)=error(1);error2(k)=error(2);error3(k)=error(3); J(k)=0.5*(error(1)^2+error(2)^2+error(3)^2); %调整大小 ypc=[y1(k)-y_1(1);y2(k)-y_1(2);y3(k)-y_1(3)]; uhc=[u_1(1)-u_2(1);u_1(2)-u_2(2);u_1(3)-u_2(3)]; %隐含层和输出层权值调整 %调整w21 Sig1=sign(ypc./(uhc(1)+0.00001)); dw21=sum(error.*Sig1)*qo'; w21=w21+rate2*dw21; %调整w22 Sig2=sign(ypc./(uh(2)+0.00001)); dw22=sum(error.*Sig2)*qo'; w22=w22+rate2*dw22; %调整w23 Sig3=sign(ypc./(uh(3)+0.00001)); dw23=sum(error.*Sig3)*qo'; w23=w23+rate2*dw23; %输入层和隐含层权值调整 delta2=zeros(3,3); wshi=[w21;w22;w23]; for t=1:1:3 delta2(1:3,t)=error(1:3).*sign(ypc(1:3)./(uhc(t)+0.00000001)); end for j=1:1:3 sgn(j)=sign((h1i(j)-h1i_1(j))/(x1i(j)-x1i_1(j)+0.00001)); end s1=sgn'*[r1(k),y1(k)]; wshi2_1=wshi(1:3,1:3); alter=zeros(3,1); dws1=zeros(3,2); for j=1:1:3 for p=1:1:3 alter(j)=alter(j)+delta2(p,:)*wshi2_1(:,j); end end for p=1:1:3 dws1(p,:)=alter(p)*s1(p,:); end w11=w11+rate1*dws1; %调整w12 for j=1:1:3 sgn(j)=sign((h2i(j)-h2i_1(j))/(x2i(j)-x2i_1(j)+0.0000001)); end s2=sgn'*[r2(k),y2(k)]; wshi2_2=wshi(:,4:6); alter2=zeros(3,1); dws2=zeros(3,2); for j=1:1:3 for p=1:1:3 alter2(j)=alter2(j)+delta2(p,:)*wshi2_2(:,j); end end for p=1:1:3 dws2(p,:)=alter2(p)*s2(p,:); end w12=w12+rate1*dws2; %调整w13 for j=1:1:3 sgn(j)=sign((h3i(j)-h3i_1(j))/(x3i(j)-x3i_1(j)+0.0000001)); end s3=sgn'*[r3(k),y3(k)]; wshi2_3=wshi(:,7:9); alter3=zeros(3,1); dws3=zeros(3,2); for j=1:1:3 for p=1:1:3 alter3(j)=(alter3(j)+delta2(p,:)*wshi2_3(:,j)); end end for p=1:1:3 dws3(p,:)=alter2(p)*s3(p,:); end w13=w13+rate1*dws3; %参数更新 u_3=u_2;u_2=u_1;u_1=uh; y_2=y_1;y_1=yn; h1i_1=h1i;h2i_1=h2i;h3i_1=h3i; x1i_1=x1i;x2i_1=x2i;x3i_1=x3i; end %% 结果分析 time=0.001*(1:k); figure(1) subplot(3,1,1) plot(time,r1,'r-',time,y1,'b-'); title('PID神经网络控制'); ylabel('被控量'); legend('控制目标','实际输出'); subplot(3,1,2) plot(time,r2,'r-',time,y2,'b-'); ylabel('被控量'); legend('控制目标','实际输出'); subplot(3,1,3) plot(time,r3,'r-',time,y3,'b-'); xlabel('时间(秒)');ylabel('被控量'); legend('控制目标','实际输出'); figure(2) plot(time,u1,'r-',time,u2,'g-',time,u3,'b'); title('PID神经网络提供给对象的控制输入'); xlabel('时间'),ylabel('被控量'); legend('u1','u2','u3');grid figure(3) plot(time,J,'r-'); axis([0,0.2,0,1]);grid title('网络学习目标函数J动态曲线'); xlabel('时间');ylabel('控制误差'); web browser http://www.ilovematlab.cn/thread-62563-1-1.html