基于RBF网络逼近的自适应控制

function [sys,x0,str,ts] = spacemodel(t,x,u,flag) switch flag, case 0, [sys,x0,str,ts]=mdlInitializeSizes; case 1, sys=mdlDerivatives(t,x,u); case 3, sys=mdlOutputs(t,x,u); case {2,4,9} sys=[]; otherwise error(['Unhandled flag = ',num2str(flag)]); end function [sys,x0,str,ts]=mdlInitializeSizes global b c lama sizes = simsizes; sizes.NumContStates = 5; sizes.NumDiscStates = 0; sizes.NumOutputs = 2; sizes.NumInputs = 4; sizes.DirFeedthrough = 1; sizes.NumSampleTimes = 1; sys = simsizes(sizes); x0 = 0.1*ones(1,5); str = []; ts = [0 0]; c=0.5*[-2 -1 0 1 2; -2 -1 0 1 2]; b=3.0; lama=10; function sys=mdlDerivatives(t,x,u) global b c lama xd=sin(t); dxd=cos(t); x1=u(2); x2=u(3); e=x1-xd; de=x2-dxd; s=lama*e+de; W=[x(1) x(2) x(3) x(4) x(5)]'; xi=[x1;x2]; h=zeros(5,1); for j=1:1:5 h(j)=exp(-norm(xi-c(:,j))^2/(2*b^2)); end gama=1500; for i=1:1:5 sys(i)=gama*s*h(i); end function sys=mdlOutputs(t,x,u) global b c lama xd=sin(t); dxd=cos(t); ddxd=-sin(t); x1=u(2); x2=u(3); e=x1-xd; de=x2-dxd; s=lama*e+de; W=[x(1) x(2) x(3) x(4) x(5)]; xi=[x1;x2]; h=zeros(5,1);c for j=1:1:5 h(j)=exp(-norm(xi-c(:,j))^2/(2*b^2)); end fn=W*h; xite=1.50; %fn=10*x1+x2; %Precise f ut=-lama*de+ddxd-fn-xite*sign(s); sys(1)=ut; sys(2)=fn;