mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-05 19:25:37 +08:00
158 lines
3.6 KiB
Matlab
158 lines
3.6 KiB
Matlab
function [sys,x0,str,ts] = Book532_Controller(t,x,u,flag)
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% 以下程序是 基于RBF神经网络的直接鲁棒自适应控制
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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 %连续状态计算
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sys=mdlDerivatives(t,x,u);
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case {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 = 10; %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 = 4; %设置系统输出的变量
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sizes.NumInputs = 4; %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 = 0.1 * ones(10,1); % 系统初始状态变量 代表W和V向量
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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 c b
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% 神经网络采用2-5-1结构
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c = 2*[-1 -0.5 -0 0.5 1;
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-1 -0.5 -0 0.5 1]; % 高斯函数的中心点矢量 维度 IN * MID 2*5
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b = 10; % 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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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
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global c b
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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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yd = 0.1 * sin(t);
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dyd = 0.1 * cos(t);
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ddyd = -0.1 * sin(t);
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c1 = 5;
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gama1 = 10;
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gama2 = 10;
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xite = 0.01;
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x_1 = u(1);
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x_2 = u(2);
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e = u(3);
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de = u(4);
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s = c1 * e + de;
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Input = [x_1; x_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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W = [x(1); x(2); x(3); x(4); x(5)];
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fx = W' * h;
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S1 = -gama1 * s * h;
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for i = 1:5
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sys(i) = S1(i);
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end
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th = u(1); % 摆角
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dth = u(2); % 摆速
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% 参数的定义
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g = 9.8;
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mc = 1; %小车质量
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m = 0.1; %摆的质量
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l = 0.5;
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f_up = g*sin(th) - m*l*dth^2*cos(th)*sin(th)/(mc+m);
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f_down = l *(4/3 - m*(cos(th)^2)/(mc+m));
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f = f_up / f_down;
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V = [x(6); x(7); x(8); x(9); x(10)];
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gx = V' * h ;
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ut = 1 / gx * (-f + ddyd + c1 * de + xite * sign(s));
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S2 = -gama2 * s * h * ut;
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for i = 6:10
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sys(i) = S2(i - 5);
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end
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function sys = mdlOutputs(t,x,u) %产生（传递）系统输出
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global c b
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IN = 2;
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Mid = 5;
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Out = 1;
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yd = 0.1 * sin(t);
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dyd = 0.1 * cos(t);
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ddyd = -0.1 * sin(t);
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c1 = 15;
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x_1 = u(1);
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x_2 = u(2);
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e = u(3);
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de = u(4);
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s = c1 * e + de;
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Input = [x_1; x_2];
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h = zeros(Mid , 1); %13*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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W = [x(1); x(2); x(3); x(4); x(5)];
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fx = W' * h;
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V = [x(6); x(7); x(8); x(9); x(10)];
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gx = V' * h ;
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th = x(1); % 摆角
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dth = x(2); % 摆速
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% 参数的定义
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g = 9.8;
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mc = 1; %小车质量
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m = 0.1; %摆的质量
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l = 0.5;
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f_up = g*sin(th) - m*l*dth^2*cos(th)*sin(th)/(mc+m);
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f_down = l *(4/3 - m*(cos(th)^2)/(mc+m));
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f = f_up / f_down;
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g_up = cos(th)/(mc+m);
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g_down = l *(4/3 - m*(cos(th)^2)/(mc+m));
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g = g_up / g_down;
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% if t<=1.5
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% xite = 1.0;
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% else
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% xite = 0.1;
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% end
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xite = 1;
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ut = 1 / gx * (-f + ddyd + c1 * de + xite * sign(s));
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sys(1) = ut;
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sys(2) = fx;
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sys(3) = gx;
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sys(4) = s;
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