基于Hebb的单神经元自适应PID控制——问题求助

function [sys,x0,str,ts] = sj_PID1(t,x,u,flag)
switch flag,
  case 0,
    [sys,x0,str,ts]=mdlInitializeSizes;
  case 1,
    sys=mdlDerivatives(t,x,u);
  case 2,
    sys=mdlUpdate(t,x,u);
  case 3,
    sys=mdlOutputs(t,x,u);
  case 4,
    sys=mdlGetTimeOfNextVarHit(t,x,u);
  case 9,
    sys=mdlTerminate(t,x,u);
  otherwise
    DAStudio.error('Simulink:blocks:unhandledFlag', num2str(flag));

end
function [sys,x0,str,ts]=mdlInitializeSizes
sizes = simsizes;
sizes.NumContStates  = 0;
sizes.NumDiscStates  = 3;
sizes.NumOutputs     = 1;
sizes.NumInputs      = 1;
sizes.DirFeedthrough = 1;
sizes.NumSampleTimes = 1;  
sys = simsizes(sizes);
x0  = [0;0;0;];
str = [];
ts  = [-1 0];


function sys=mdlDerivatives(t,x,u)
sys = [];

function sys=mdlUpdate(t,x,u)
x(3)=x(2);
x(2)=x(1);
x(1)=u;
q1=x(1);
q2=x(1)-x(2);
q3=x(1)-2*x(2)+x(3);
sys =[q1;q2;q3];

function sys=mdlOutputs(t,x,u)
 persistent wkpl_1 wkil_1 wkdl_1 ul_1
 xiteP=700;
 xiteI=10;
 xiteD=60;
if t==0   
 wkpl_1=rand;
 wkil_1=rand;
 wkdl_1=rand;
 ul_1=0;
 end
% %有监督的Hebb学习算法的权值调整规则
 wkil=wkil_1+xiteI*x(1)*ul_1*x(1);%I
 wkpl=wkpl_1+xiteP*x(1)*ul_1*x(2);%P
 wkdl=wkdl_1+xiteD*x(1)*ul_1*x(3);%D
 waddl=abs(wkpl)+abs(wkil)+abs(wkdl);
 wlll=wkpl/waddl;
 wl22=wkil/waddl;
 wl33=wkdl/waddl;
 wl=[wlll,wl22,wl33];
 kl=7;
 ul=kl*wl*x;  %增量型
 wkpl_1=wkpl;
 wkdl_1=wkdl;
 wkil_1=wkil;
 ul_1=ul+ul_1;
sys=ul;

function sys=mdlGetTimeOfNextVarHit(t,x,u)
sampleTime = 0.01;%  Example, set the next hit to be one second later.
sys = t + sampleTime;

function sys=mdlTerminate(t,x,u)
sys = [];