mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-06 03:31:48 +08:00
91 lines
2.3 KiB
Matlab
91 lines
2.3 KiB
Matlab
function [sys,x0,str,ts] = Book4132_Controller(t,x,u,flag)
|
||
switch flag
|
||
case 0
|
||
[sys,x0,str,ts]=mdlInitializeSizes;
|
||
case 1
|
||
sys=mdlDerivatives(t,x,u);
|
||
case {2,4,9}
|
||
sys=[];
|
||
case 3
|
||
sys=mdlOutputs(t,x,u);
|
||
otherwise
|
||
DAStudio.error('Simulink:blocks:unhandledFlag', num2str(flag));
|
||
end
|
||
|
||
function [sys,x0,str,ts]=mdlInitializeSizes %系统的初始化
|
||
sizes = simsizes;
|
||
sizes.NumContStates = 0; %设置系统连续状态的变量
|
||
sizes.NumDiscStates = 0; %设置系统离散状态的变量
|
||
sizes.NumOutputs = 2; %设置系统输出的变量
|
||
sizes.NumInputs = 2; %设置系统输入的变量
|
||
sizes.DirFeedthrough = 1; %如果在输出方程中显含输入变量u，则应该将本参数设置为1
|
||
sizes.NumSampleTimes = 0; % 模块采样周期的个数
|
||
% 需要的样本时间，一般为1.
|
||
% 猜测为如果为n，则下一时刻的状态需要知道前n个状态的系统状态
|
||
sys = simsizes(sizes);
|
||
x0 = []; % 系统初始状态变量
|
||
str = []; % 保留变量，保持为空
|
||
ts = []; % 采样时间[t1 t2] t1为采样周期，如果取t1=-1则将继承输入信号的采样周期；参数t2为偏移量，一般取为0
|
||
global W
|
||
% 神经网络采用2-5-1结构 IN = 2 MID = 5 OUT = 1
|
||
% 初始权值
|
||
W = [0 ; 0 ; 0 ; 0 ; 0]'; %MID * OUT矩阵 1*5
|
||
|
||
function sys = mdlOutputs(t,x,u) %产生（传递）系统输出
|
||
global W
|
||
% 神经网络采用2-5-1结构
|
||
IN = 2;
|
||
Mid = 5;
|
||
Out = 1;
|
||
|
||
Q = [500 0; 0 500];
|
||
kd = 50;
|
||
kp = 30;
|
||
gama = 1200; %gama为正常数
|
||
b = 0.2; % 高斯函数的基宽 维度MID * 1 1*1
|
||
c = [-2 -1 0 1 2;
|
||
-2 -1 0 1 2]; % 高斯函数的中心点矢量 维度 IN * MID 2*5
|
||
|
||
e = u(1);
|
||
de = u(2);
|
||
|
||
some = [u(1); u(2)];
|
||
h = zeros(Mid , 1); %5*1矩阵
|
||
for i =1:Mid
|
||
h(i) = exp(-(norm(some - c(:,i))^2) / (2*b^2));
|
||
end
|
||
fx_refer = W * h;
|
||
% 参数的定义
|
||
th = sin(t);
|
||
mc = 1; %小车质量
|
||
m = 0.1; %摆的质量
|
||
l = 0.5;
|
||
g_up = cos(th)/(mc+m);
|
||
g_down = l *(4/3 - m*(cos(th)^2)/(mc+m));
|
||
gx = g_up / g_down;
|
||
ddyd = -0.1 * sin(t); %跟踪信号的二阶导数
|
||
K = [kp ;kd];
|
||
E = [e ; de];
|
||
|
||
% 控制率ut
|
||
ut = 1/(gx)*(-fx_refer + ddyd + K' * E);
|
||
|
||
sys(1) = ut;
|
||
sys(2) = fx_refer;
|
||
|
||
% 自适应律的设计
|
||
fai = [0 1; -kp -kd];
|
||
P = lyap(fai', Q); %P为对称正定矩阵且满足Lyapunov方程
|
||
B = [0; 1];
|
||
dw = zeros(1, 5);
|
||
for i = 1:5
|
||
dw(i) = -gama * E' * P * B * h(i); % 1*1 * 1*2 * 2*2 *2*1 * 1*1
|
||
end
|
||
dt = 0.001; % 仿真步长
|
||
W = W + dw * dt;
|
||
|
||
|
||
|
||
|
||
|