mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-05 11:09:47 +08:00
118 lines
3.2 KiB
Matlab
118 lines
3.2 KiB
Matlab
function [sys,x0,str,ts] = Book4342_Controller(t,x,u,flag)
|
||
% 以下程序是 基于RBF神经网络的直接鲁棒自适应控制
|
||
switch flag
|
||
case 0 %初始化
|
||
[sys,x0,str,ts]=mdlInitializeSizes;
|
||
case 1 %连续状态计算
|
||
sys=mdlDerivatives(t,x,u);
|
||
case {2,4,9} %离散状态计算，下一步仿真时刻，终止仿真设定
|
||
sys=[];
|
||
case 3 %输出信号计算
|
||
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
|
||
global c b node If lamda W
|
||
W = [0 0 0 0 0 0 0 0 0 0 0 0 0]' ; %MID * OUT 矩阵 13*1
|
||
node = 13;
|
||
If = 0.25;
|
||
lamda = 5;
|
||
sizes = simsizes;
|
||
sizes.NumContStates = node; %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 = 1; %设置系统输出的变量
|
||
sizes.NumInputs = 3; %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 = zeros(1,13); % 系统初始状态变量
|
||
% 神经网络采用5-9-1结构
|
||
c = [-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6;
|
||
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6;
|
||
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6;
|
||
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6;
|
||
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6]; % 高斯函数的中心点矢量 维度 IN * MID 5*13
|
||
b = 5; % 高斯函数的基宽 维度MID * 1 1*1 b的选择很重要 b越大 网路对输入的映射能力越大
|
||
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
|
||
|
||
|
||
function sys = mdlDerivatives(t,x,u) %该函数仅在连续系统中被调用，用于产生控制系统状态的导数
|
||
global c b node If lamda W
|
||
% 仿真中应根据网络输入值的有效映射范围来设计 c和b 从而保证有效的高斯映射 不合适的b或c均会导致结果不正确
|
||
IN = 5;
|
||
Mid = 13;
|
||
Out = 1;
|
||
yd = pi/6 * sin(t);
|
||
dyd = pi/6 * cos(t);
|
||
ddyd = -pi/6 * sin(t);
|
||
|
||
% e = -u(1); % e = x - xd; 实际-期望
|
||
% de = -u(2);
|
||
% x_1 = u(3);
|
||
% x_2 = u(4);
|
||
x1 = u(2);
|
||
x2 = u(3);
|
||
e = x1 - yd;
|
||
de = x2 - dyd;
|
||
s = lamda * e + de;
|
||
s_if = s/If;
|
||
|
||
v = -ddyd + lamda * de;
|
||
Input = [x1; x2; s; s_if ; v];
|
||
% Input = [x_1; x_2; s; s_if ; v];
|
||
h = zeros(Mid , 1); %13*1矩阵
|
||
for i =1:Mid
|
||
h(i) = exp(-(norm(Input - c(:,i))^2) / (2*b^2));
|
||
end
|
||
|
||
rou = 0.005;
|
||
Gama = 15 * eye(13);
|
||
W = [x(1); x(2); x(3); x(4); x(5); x(6); x(7); x(8); x(9); x(10); x(11); x(12); x(13)];
|
||
S = -Gama * (h*s + rou*W);
|
||
|
||
for i = 1:node
|
||
sys(i) = S(i);
|
||
end
|
||
|
||
|
||
function sys = mdlOutputs(t,x,u) %产生（传递）系统输出
|
||
global c b node If lamda W
|
||
yd = pi/6 * sin(t);
|
||
dyd = pi/6 * cos(t);
|
||
ddyd = -pi/6 * sin(t);
|
||
x_1 = u(2);
|
||
x_2 = u(3);
|
||
e = x_1 - yd; % e = x - xd; 实际-期望
|
||
de = x_2 - dyd;
|
||
s = lamda * e + de;
|
||
s_if = s/If;
|
||
|
||
v = -ddyd + lamda * de;
|
||
Input = [x_1; x_2; s; s_if ; v];
|
||
% Input = [x_1; x_2; s; s_if ; v];
|
||
h = zeros(node , 1); %13*1矩阵
|
||
for i =1:node
|
||
h(i) = exp(-(norm(Input - c(:,i))^2) / (2*b^2));
|
||
end
|
||
W = [x(1); x(2); x(3); x(4); x(5); x(6); x(7); x(8); x(9); x(10); x(11); x(12); x(13)];
|
||
belta = 1;
|
||
ut = 1/belta * W' * h;
|
||
sys(1) = ut;
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|