mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-10-02 18:23:03 +08:00
105 lines
3.1 KiB
Matlab
105 lines
3.1 KiB
Matlab
function [sys,x0,str,ts] = Book321_controller(t,x,u,flag)
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% RBF神经网络自适应控制刘金琨例题3.2.1仿真
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% 基于RBF神经网络的模型参考自适应控制
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switch flag
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case 0 %初始化
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[sys,x0,str,ts]=mdlInitializeSizes;
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case {1,2,4,9} %离散状态计算，下一步仿真时刻，终止仿真设定
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sys=[];
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case 3 %输出信号计算
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sys=mdlOutputs(t,x,u);
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otherwise
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DAStudio.error('Simulink:blocks:unhandledFlag', num2str(flag));
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end
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function [sys,x0,str,ts]=mdlInitializeSizes %系统的初始化
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sizes = simsizes;
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sizes.NumContStates = 0; %设置系统连续状态的变量
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sizes.NumDiscStates = 0; %设置系统离散状态的变量
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sizes.NumOutputs = 1; %设置系统输出的变量
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sizes.NumInputs = 3; %设置系统输入的变量
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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
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sizes.NumSampleTimes = 0; % 模块采样周期的个数
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% 需要的样本时间，一般为1.
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% 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
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sys = simsizes(sizes);
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x0 = []; % 系统初始状态变量
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str = []; % 保留变量，保持为空
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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
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% 权值初值的选择
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% 神经网络PID控制器 2-7-1结构
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global W_new W_past C_new C_past B_new B_past yk_new yk_past ut_new ut_past some
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C_new = [-3 -2 -1 0 1 2 3;
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-3 -2 -1 0 1 2 3;
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-3 -2 -1 0 1 2 3]; %3*7 中心矢量
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C_past = C_new;
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B_new = [2 2 2 2 2 2 2]; %1*7 基宽度参数
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B_past = B_new;
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% W_new = rand(1,7); %权值取0-1的随机值
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W_new = [-0.0316 -0.0421 -0.0318 0.0068 0.0454 -0.0381 -0.0381];
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W_past = W_new;
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yk_new = 0;
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yk_past = yk_new;
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ut_new = 0;
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ut_past = ut_new;
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some = [0 0 0]';
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function sys = mdlOutputs(t,x,u) %产生（传递）系统输出
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global W_new W_past C_new C_past B_new B_past yk_new yk_past ut_new ut_past some
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% if t>0
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% 神经网络PID控制器 2-6-1结构
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alpha = 0.05; %惯性系数
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xite = 0.35; %学习效率
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IN = 3;
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Mid = 7;
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Out = 1;
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yd = u(1);
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ec = u(2);
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yk_new = u(3);
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h = zeros(Mid, 1);
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for j = 1:Mid
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h(j) = exp(-(norm(some - C_new(:,j))^2/(2* B_new(j)^2))); %7*1矩阵 径向基函数
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end
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% RBF的网络输出ym
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ut_new = W_new * h;
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dyu = sign((yk_new - yk_past)/(ut_new - ut_past));
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% 权值的调整 更新值
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deltaW = zeros(1,Mid);
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for i = 1:Mid
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deltaW(i) = xite * ec * dyu * h(i);
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end
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W_new = W_new + deltaW + alpha*(W_new - W_past);
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% 基宽带参数b的修正
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% deltab = zeros(1,Mid);
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% for i = 1:Mid
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% deltab(i) = xite * dyu * W_new(i) * h(i) * (norm(some - C_new(:,i))^2 / B_new(i)^3);
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% B_new(i) = B_new(i) + deltab(i) + alpha*(B_new(i) - B_past(i));
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% end
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% 中心矢量c的修正
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% deltac = zeros(IN, Mid);
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% for i = 1:IN
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% for j = 1:Mid
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% deltac(i,j) = xite * dyu * W_new(j) * h(j) * ((some(i) - C_new(i,j)) / B_new(j)^2);
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% C_new(i,j) = C_new(i,j) + deltac(i,j) + alpha*(C_new(i,j) - C_past(i,j));
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% end
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% end
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sys(1) = ut_new;
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some = [u(1); u(2); u(3)];
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W_past = W_new;
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B_past = B_new;
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C_past = C_new;
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ut_past = ut_new;
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yk_past = yk_new;
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% else
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% sys(1) = ut_new;
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% end
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