基于Matlab实现数字RBF神经网络控制（源码）.rar

%Discrete RBF control for two-link manipulators clear all; close all; T=0.001; %Sampling time xk=[0 0 0 0]; tol1_1=0; tol2_1=0; ei=0; node=5; c_M=[-1 -0.5 0 0.5 1; -1 -0.5 0 0.5 1]; c_C=[-1 -0.5 0 0.5 1; -1 -0.5 0 0.5 1; -1 -0.5 0 0.5 1; -1 -0.5 0 0.5 1]; c_G=[-1 -0.5 0 0.5 1; -1 -0.5 0 0.5 1]; b=10; W_M11_1=zeros(node,1);W_M12_1=zeros(node,1); W_M21_1=zeros(node,1);W_M22_1=zeros(node,1); W_C11_1=zeros(node,1);W_C12_1=zeros(node,1); W_C21_1=zeros(node,1);W_C22_1=zeros(node,1); W_G1_1=zeros(node,1);W_G2_1=zeros(node,1); Hur=[5 0;0 5]; for k=1:1:5000 if mod(k,200)==1 k end time(k) = k*T; qd1(k)=sin(2*pi*k*T); qd2(k)=sin(2*pi*k*T); dqd1(k)=2*pi*cos(2*pi*k*T); dqd2(k)=2*pi*cos(2*pi*k*T); ddqd1(k)=-(2*pi)^2*sin(2*pi*k*T); ddqd2(k)=-(2*pi)^2*sin(2*pi*k*T); para=[tol1_1 tol2_1]; %D/A tSpan=[0 T]; [t,xx]=ode45('chap9_3plant',tSpan,xk,[],para); %A/D speed xk = xx(length(xx),:); %A/D position q1(k)=xk(1); dq1(k)=xk(2); q2(k)=xk(3); dq2(k)=xk(4); q=[q1(k);q2(k)]; z=[q1(k);q2(k);dq1(k);dq2(k)]; e1(k)=qd1(k)-q1(k); de1(k)=dqd1(k)-dq1(k); e2(k)=qd2(k)-q2(k); de2(k)=dqd2(k)-dq2(k); e=[e1(k);e2(k)]; de=[de1(k);de2(k)]; r=de+Hur*e; dqd=[dqd1(k);dqd2(k)]; dqr=dqd+Hur*e; ddqd=[ddqd1(k);ddqd2(k)]; ddqr=ddqd+Hur*de; for j=1:1:node h_M11(j)=exp(-norm(q-c_M(:,j))^2/(b^2)); h_M21(j)=exp(-norm(q-c_M(:,j))^2/(b^2)); h_M12(j)=exp(-norm(q-c_M(:,j))^2/(b^2)); h_M22(j)=exp(-norm(q-c_M(:,j))^2/(b^2)); end for j=1:1:node h_C11(j)=exp(-norm(z-c_C(:,j))^2/(b^2)); h_C21(j)=exp(-norm(z-c_C(:,j))^2/(b^2)); h_C12(j)=exp(-norm(z-c_C(:,j))^2/(b^2)); h_C22(j)=exp(-norm(z-c_C(:,j))^2/(b^2)); end for j=1:1:node h_G1(j)=exp(-norm(q-c_G(:,j))^2/(b^2)); h_G2(j)=exp(-norm(q-c_G(:,j))^2/(b^2)); end T_M11=5*eye(node); T_M21=5*eye(node); T_M12=5*eye(node); T_M22=5*eye(node); T_C11=10*eye(node); T_C21=10*eye(node); T_C12=10*eye(node); T_C22=10*eye(node); T_G1=5*eye(node); T_G2=5*eye(node); for i=1:1:node W_M11(i,1)=W_M11_1(i,1)+T*T_M11(i,i)*h_M11(i)*ddqr(1)*r(1); W_M21(i,1)=W_M21_1(i,1)+T*T_M21(i,i)*h_M21(i)*ddqr(1)*r(2); W_M12(i,1)=W_M12_1(i,1)+T*T_M12(i,i)*h_M12(i)*ddqr(2)*r(1); W_M22(i,1)=W_M22_1(i,1)+T*T_M22(i,i)*h_M22(i)*ddqr(2)*r(2); W_C11(i,1)=W_C11_1(i,1)+T*T_C11(i,i)*h_C11(i)*dqr(1)*r(1); W_C21(i,1)=W_C21_1(i,1)+T*T_C21(i,i)*h_C21(i)*dqr(1)*r(2); W_C12(i,1)=W_C12_1(i,1)+T*T_C12(i,i)*h_C12(i)*ddqr(2)*r(1); W_C22(i,1)=W_C22_1(i,1)+T*T_C22(i,i)*h_C22(i)*ddqr(2)*r(2); W_G1=W_G1_1+T*T_G1(i,i)*h_G1(i)*r(1); W_G2=W_G2_1+T*T_G2(i,i)*h_G2(i)*r(2); end MSNN_g=[W_M11'*h_M11' W_M12'*h_M12'; W_M21'*h_M21' W_M22'*h_M22']; GSNN_g=[W_G1'*h_G1'; W_G2'*h_G2']; CDNN_g=[W_C11'*h_C11' W_C12'*h_C12'; W_C21'*h_C21' W_C22'*h_C22']; tol_m=MSNN_g*ddqr+CDNN_g*dqr+GSNN_g; Kp=[20 0;0 20]; Ki=[20 0;0 20]; Kr=[1.5 0;0 1.5]; Kp=[100 0;0 100]; Ki=[100 0;0 100]; Kr=[0.1 0;0 0.1]; ei=ei+e*T; tol=tol_m+Kp*r+Kr*sign(r)+Ki*ei; tol1(k)=tol(1); tol2(k)=tol(2); W_M11_1=W_M11; W_M21_1=W_M21; W_M12_1=W_M12; W_M22_1=W_M22; W_C11_1=W_C11; W_C21_1=W_C21; W_C12_1=W_C12; W_C22_1=W_C22; W_G1_1=W_G1; W_G2_1=W_G2; tol1_1=tol1(k); tol2_1=tol2(k); end figure(1); subplot(211); plot(time,qd1,'r',time,q1,'k:','linewidth',2); xlabel('time(s)'),ylabel('Position tracking of link 1'); legend('ideal position','position tracking'); subplot(212); plot(time,qd2,'r',time,q2,'k:','linewidth',2); xlabel('time(s)'),ylabel('Position tracking of link 2'); legend('ideal position','position tracking'); figure(2); subplot(211); plot(time,dqd1,'r',time,dq1,'k:','linewidth',2); xlabel('time(s)'),ylabel('Speed tracking of link 1'); legend('ideal speed','speed tracking'); subplot(212); plot(time,dqd2,'r',time,dq2,'k:','linewidth',2); xlabel('time(s)'),ylabel('Speed tracking of link 2'); legend('ideal speed','speed tracking'); figure(3); subplot(211); plot(time,tol1,'r','linewidth',2); xlabel('time(s)'),ylabel('Control input of link 1'); subplot(212); plot(time,tol2,'r','linewidth',2); xlabel('time(s)'),ylabel('Control input of link 2');