mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-08-18 01:17:18 +08:00
102 lines
2.6 KiB
Matlab
102 lines
2.6 KiB
Matlab
function [sys,x0,str,ts] = Book423_Controller(t,x,u,flag)
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switch flag
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case 0
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[sys,x0,str,ts]=mdlInitializeSizes;
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case 1
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sys=mdlDerivatives(t,x,u);
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case {2,4,9}
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sys=[];
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case 3
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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 = 2; %设置系统输出的变量
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sizes.NumInputs = 2; %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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global W m_0
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% 神经网络采用2-5-1结构 IN = 2 MID = 5 OUT = 1
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% 初始权值
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W = [0 ; 0 ; 0 ; 0 ; 0]'; %MID * OUT矩阵 1*5
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m_0 = 120;
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function sys = mdlOutputs(t,x,u) %产生（传递）系统输出
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global W m_0
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% 神经网络采用2-5-1结构
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b = 100; % 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
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c = [-2 -1 0 1 2;
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-2 -1 0 1 2]; % 高斯函数的中心点矢量 维度 IN * MID 2*5
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% 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
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IN = 2;
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Mid = 5;
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Out = 1;
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Q = [500 0; 0 500];
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kd = 50;
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kp = 30;
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gama = 1200; %gama为正常数
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xite = 0.0001;
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m = 100;
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e = u(1);
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de = u(2);
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Input = [u(1); u(2)];
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h = zeros(Mid , 1); %5*1矩阵
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for i =1:Mid
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h(i) = exp(-(norm(Input - c(:,i))^2) / (2*b^2));
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end
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% fx的估计
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fx_refer = W * h;
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K = [kp ;kd];
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E = [e ; de];
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yd = sin(t);
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dyd = cos(t);
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ddyd = -sin(t);
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% 控制率ut
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ut = 1/(m_0)*(-fx_refer + ddyd + K' * E);
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sys(1) = ut;
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sys(2) = fx_refer;
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% 自适应律的设计
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fai = [0 1; -kp -kd];
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P = lyap(fai', Q); %P为对称正定矩阵且满足Lyapunov方程
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B = [0; 1];
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dw = zeros(1, 5);
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for i = 1:5
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dw(i) = -gama * E' * P * B * h(i); % 1*1 * 1*2 * 2*2 *2*1 * 1*1
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end
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dt = 0.001; % 仿真步长
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W = W + dw * dt; % W的自适应律
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% m的估计律
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some = E' * P * B * ut;
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if some > 0
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dm = 1/xite*some;
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elseif some <= 0 && m_0 > m
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dm = 1/xite*some;
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else
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dm = 1/xite;
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end
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m_0 = m_0 + dm * dt; % m的自适应律
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