MATLAB实现神经网络之单神经元自适应控制

系统对象
y(k) = 0.368y(k-1)+0.26y(k-2)+0.1u(k-1)+0.632u(k-2)

系统输入
rin(k)=0.5sgn(sin(4πt))

clc
clear

%定义输入矩阵
x = [0,0,0]'; 

%定义学习速率
xite1 = 0.4;  
xite2 = 0.3; 
xite3 = 0.5;

%初始化权重
wk1_1 = 0.1; 
wk2_1 = 0.1;
wk3_1 = 0.1;

%初始化误差
erro_1 = 0;
erro_2 = 0;

%初始化被控对象模型
y_1 = 0;y_2 = 0;y_3 = 0; 
u_1 = 0;u_2 = 0;u_3 = 0;

%定义时间步长
ts = 0.001; 

%迭代学习
for k=1:1:1000 %循环迭代次数
    time(k) = k*ts; %时间为K*ts总时间也就是1
    yd(k) = 0.5*sign(sin(4*pi*time(k))); %目标期望值
    y(k) = 0.368*y_1+0.26*y_2+0.1*u_1+0.632*u_2; %被控对象动态模型模型
    erro(k) = yd(k)-y(k); %获取误差
    
    %更新权重
    wk1(k) = wk1_1+xite1*erro(k)*u_1*x(1); 
    wk2(k) = wk2_1+xite2*erro(k)*u_1*x(2);
    wk3(k) = wk3_1+xite3*erro(k)*u_1*x(3);
    
    %定义比例系数
    K = 0.12; 
    
    %给输入矩阵赋值
    x(1) = erro(k)-erro_1; 
    x(2) = erro(k);
    x(3) = erro(k)-2*erro_1+erro_2;
    
    %将权重求和
    wadd(k) = abs(wk1(k))+abs(wk2(k))+abs(wk3(k)); 
    w11(k) = wk1(k)/wadd(k); %得w11
    w22(k) = wk2(k)/wadd(k); %w22
    w33(k) = wk3(k)/wadd(k); %w33
    w = [w11(k),w22(k),w33(k)];
    
    %增量
    u(k) = u_1 + K*w*x; 
    erro_2 = erro_1; %e(k-2)
    erro_1 = erro(k);%e(k-1)
    
    u_3 = u_2;u_2 = u_1;u_1 = u(k);
    y_3 = y_2;y_2 = y_1;y_1 = y(k);
    
    wk1_1 = wk1(k); %w1(k-1)
    wk3_1 = wk3(k); 
    wk2_1 = wk2(k);
end

%---------------结果可视化-------------------%

%显示被控模型曲线和设定曲线
figure(1);
plot(time,yd,'r',time,y,'k','linewidth',2);
xlabel('time(s)');ylabel('yd,y'); 
legend('ideal position','position tracking');
title('被控模型曲线和设定曲线')

%显示误差
figure(2);
plot(time,erro,'r','linewidth',2);
xlabel('time(s)');ylabel('erro(t)');
title('误差曲线')

 %显示控制u（t）输出曲线
figure(3);
plot(time,u,'r','linewidth',2);
xlabel('time(s)');ylabel('u(t)');
title('控制量u（t）输出曲线')

%显示w参数调整曲线
figure(4);
plot(time,w11,'g',time,w22,'r',time,w33,'b','linewidth',2);
xlabel('time(s)');ylabel('w(t)');
legend('w11(t)','w22(t)','w33(t)');
title('w11,w22,w33参数调整曲线')