基于Matlab实现神经网络滑模控制（源码）.rar

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 xite cij bj h c sizes = simsizes; sizes.NumContStates = 10; sizes.NumDiscStates = 0; sizes.NumOutputs = 3; sizes.NumInputs = 5; sizes.DirFeedthrough = 1; sizes.NumSampleTimes = 0; sys = simsizes(sizes); x0 = 0.1*ones(10,1); str = []; ts = []; cij=[-1 -0.5 0 0.5 1; -1 -0.5 0 0.5 1]; bj=5; h=[0,0,0,0,0]; c=5; xite=0.01; function sys=mdlDerivatives(t,x,u) global xite cij bj h c thd=u(1); dthd=0.1*cos(t); ddthd=-0.1*sin(t); x1=u(2); x2=u(3); e=thd-x1; de=dthd-x2; s=c*e+de; xi=[x1;x2]; for j=1:1:5 h(j)=exp(-norm(xi-cij(:,j))^2/(2*bj^2)); end for i=1:1:5 wf(i,1)=x(i); end for i=1:1:5 wg(i,1)=x(i+5); end fxn=wf'*h'; gxn=wg'*h'+0.01; ut=1/gxn*(-fxn+ddthd+xite*sign(s)+c*de); gama1=10;gama2=1.0; S1=-gama1*s*h; S2=-gama2*s*h*ut; for i=1:1:5 sys(i)=S1(i); end for j=6:1:10 sys(j)=S2(j-5); end function sys=mdlOutputs(t,x,u) global xite cij bj h c thd=u(1); dthd=0.1*cos(t); ddthd=-0.1*sin(t); x1=u(2); x2=u(3); e=thd-x1; de=dthd-x2; s=c*e+de; for i=1:1:5 wf(i,1)=x(i); end for i=1:1:5 wg(i,1)=x(i+5); end xi=[x1;x2]; for j=1:1:5 h(j)=exp(-norm(xi-cij(:,j))^2/(2*bj^2)); end fxn=wf'*h'; gxn=wg'*h'+0.01; ut=1/gxn*(-fxn+ddthd+xite*sign(s)+c*de); sys(1)=ut; sys(2)=fxn; sys(3)=gxn;