mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-05 19:25:37 +08:00
266 lines
7.0 KiB
Matlab
266 lines
7.0 KiB
Matlab
function [sys,x0,str,ts] = Fixed_time_NNcontroller(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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global c1 b1 c2 b2 node gama1 gama2 xite belta lg
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% 神经网络采用2-7-1结构
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node = 7;
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c1 = 0.5*[-2 -1.5 -1 0 1 1.5 2;
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-2 -1.5 -1 0 1 1.5 2;
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-2 -1.5 -1 0 1 1.5 2;
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-2 -1.5 -1 0 1 1.5 2]; % 高斯函数的中心点矢量 维度 IN * MID 2*7
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b1 = 20; % 高斯函数的基宽 维度MID * 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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gama1 = 10;
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lg = 2;
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xite = 20; % 控制器的参数
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c2 = 1.8*[-2 -1.5 -1 0 1 1.5 2;
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-2 -1.5 -1 0 1 1.5 2;
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-2 -1.5 -1 0 1 1.5 2;
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-2 -1.5 -1 0 1 1.5 2]; % 高斯函数的中心点矢量 维度 IN * MID 2*7
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b2 = 4; % 高斯函数的基宽 维度MID * 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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gama2 = 0.01;
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belta = 2;
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sizes = simsizes;
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sizes.NumContStates = node*6; %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 = 7; %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 = 1; % 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(node*4,1); 2*ones(node*1,1);3*ones(node*1,1)]; % 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 = [0 0]; % 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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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 c1 b1 c2 b2 node gama1 gama2 xite belta lg
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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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yd1 = 0.5*sin(pi*t);
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dyd1 = 0.5*pi*cos(pi*t);
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ddyd1 = -0.5*pi*pi*sin(pi*t);
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yd2 = 0.5*sin(pi*t);
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dyd2 = 0.5*pi*cos(pi*t);
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ddyd2 = -0.5*pi*pi*sin(pi*t);
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q1 = u(1);
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q2 = u(2);
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dq1 = u(3);
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dq2 = u(4);
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e1 = yd1 - q1;
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e2 = yd2 - q2;
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de1 = dyd1 - dq1;
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de2 = dyd2 - dq2;
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e = [e1;e2];
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de = [de1;de2];
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% 参数的定义
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q = 3;
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p = 5;
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% 滑模面
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s1 = e1 + 1/belta*abs(de1)^(p/q)*sign(de1);
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s2 = e2 + 1/belta*abs(de2)^(p/q)*sign(de2);
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s = [s1; s2];
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coe1 = p/(belta*q)*de1^(p-q)^(1/q); % 必大于0
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coe2 = p/(belta*q)*de2^(p-q)^(1/q); % 必大于0
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Input = [q1;q2;dq1;dq2];
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% --------------------------------------------- W权值的更新
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hf1 = zeros(node , 1); %7*1矩阵
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hf2 = zeros(node , 1); %7*1矩阵
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for i =1:node
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hf1(i) = exp(-(norm(Input - c1(:,i))^2) / (2*b1^2));
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hf2(i) = exp(-(norm(Input - c1(:,i))^2) / (2*b1^2));
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end
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W1 = x(1:node); % 7*1矩阵
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W2 = x(node+1:2*node); % 7*1矩阵
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fx1 = W1' * hf1;
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fx2 = W2' * hf2;
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fx = [fx1; fx2]; % 2*1
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dw_fx1 = -gama1 * s1 * coe1 * hf1; % 7*1矩阵
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dw_fx2 = -gama1 * s2 * coe2 * hf2; % 7*1矩阵
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for i = 1:node
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sys(i) = dw_fx1(i);
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sys(i+node) = dw_fx2(i);
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end
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% ------------------------------------------------- V权值的更新
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hg11 = zeros(node , 1); %7*1矩阵
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hg12 = zeros(node , 1); %7*1矩阵
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hg21 = zeros(node , 1); %7*1矩阵
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hg22 = zeros(node , 1); %7*1矩阵
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for i =1:node
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hg11(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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hg12(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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hg21(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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hg22(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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end
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V11 = x(node*2+1:node*3);
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V12 = x(node*3+1:node*4);
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V21 = x(node*4+1:node*5);
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V22 = x(node*5+1:node*6);
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gx11 = V11' * hg11;
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gx12 = V12' * hg12;
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gx21 = V21' * hg21;
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gx22 = V22' * hg22;
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gx = [gx11 gx12; gx21 gx22];
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% 控制器的设计
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p1 = 2.9;
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p2 = 0.76;
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p3 = 0.87;
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p4 = 3.04;
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M = [p1+p2+2*p3*cos(q2) p2+p3*cos(q2);
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p2+p3*cos(q2) p2];
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% 控制器的设计
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g = inv(M);
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temp1 = -belta*q/p* (abs(de1)^(2-p/q)*sign(de1)) + fx(1) - (lg+xite)*sign(s1) - ddyd1;
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temp2 = -belta*q/p* (abs(de2)^(2-p/q)*sign(de2)) + fx(2) - (lg+xite)*sign(s2) - ddyd2;
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temp = [temp1; temp2];
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tau = -inv(gx) * temp;
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dw_g11 = -gama2 * s1 * coe1 * hg11 * tau(1);
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dw_g12 = -gama2 * s1 * coe1 * hg12 * tau(1);
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dw_g21 = -gama2 * s2 * coe2 * hg21 * tau(2);
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dw_g22 = -gama2 * s2 * coe2 * hg22 * tau(2);
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for i = 1 : node
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sys(i+node*2) = dw_g11(i);
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sys(i+node*3) = dw_g12(i);
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sys(i+node*4) = dw_g21(i);
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sys(i+node*5) = dw_g22(i);
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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 c1 b1 c2 b2 node gama1 gama2 xite belta lg
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% 角度跟踪指令
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yd1 = 0.5*sin(pi*t);
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dyd1 = 0.5*pi*cos(pi*t);
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ddyd1 = -0.5*pi*pi*sin(pi*t);
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yd2 = 0.5*sin(pi*t);
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dyd2 = 0.5*pi*cos(pi*t);
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ddyd2 = -0.5*pi*pi*sin(pi*t);
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q1 = u(1);
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q2 = u(2);
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dq1 = u(3);
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dq2 = u(4);
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e1 = yd1 - q1;
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e2 = yd2 - q2;
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de1 = dyd1 - dq1;
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de2 = dyd2 - dq2;
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e = [e1;e2];
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de = [de1;de2];
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ddyd = [ddyd1; ddyd2];
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% 参数的定义
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q = 3;
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p = 5;
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% 滑模面
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s1 = e1 + 1/belta*abs(de1)^(p/q)*sign(de1);
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s2 = e2 + 1/belta*abs(de2)^(p/q)*sign(de2);
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s = [s1; s2];
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Input = [q1;q2;dq1;dq2];
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% ------------------------------------------- W权值
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hf1 = zeros(node , 1); %7*1矩阵
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hf2 = zeros(node , 1); %7*1矩阵
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for i =1:node
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hf1(i) = exp(-(norm(Input - c1(:,i))^2) / (2*b1^2));
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hf2(i) = exp(-(norm(Input - c1(:,i))^2) / (2*b1^2));
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end
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W1 = x(1:node); % 7*1矩阵
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W2 = x(node+1:2*node); % 7*1矩阵
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coe1 = p/(belta*q)*de1^(p-q)^(1/q); % 必大于0
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coe2 = p/(belta*q)*de2^(p-q)^(1/q); % 必大于0
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fx1 = W1' * hf1;
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fx2 = W2' * hf2;
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fx = [fx1; fx2]; % 2*1
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% ------------------------------------------- V权值
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hg11 = zeros(node , 1); %7*1矩阵
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hg12 = zeros(node , 1); %7*1矩阵
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hg21 = zeros(node , 1); %7*1矩阵
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hg22 = zeros(node , 1); %7*1矩阵
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for i =1:node
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hg11(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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hg12(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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hg21(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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hg22(i) = exp(-(norm(Input - c2(:,i))^2) / (2*b2^2));
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end
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V11 = x(node*2+1:node*3);
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V12 = x(node*3+1:node*4);
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V21 = x(node*4+1:node*5);
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V22 = x(node*5+1:node*6);
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gx11 = V11' * hg11;
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gx12 = V12' * hg12;
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gx21 = V21' * hg21;
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gx22 = V22' * hg22;
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gx = [gx11 gx12; gx21 gx22];
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p1 = 2.9;
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p2 = 0.76;
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p3 = 0.87;
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p4 = 3.04;
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M = [p1+p2+2*p3*cos(q2) p2+p3*cos(q2);
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p2+p3*cos(q2) p2];
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% 控制器的设计
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g = inv(M);
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temp1 = -belta*q/p* (abs(de1)^(2-p/q)*sign(de1)) + fx(1) - (lg+xite)*sign(s1) - ddyd1;
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temp2 = -belta*q/p* (abs(de2)^(2-p/q)*sign(de2)) + fx(2) - (lg+xite)*sign(s2) - ddyd2;
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temp = [temp1; temp2];
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tau = - inv(gx) * temp;
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sys(1) = tau(1);
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sys(2) = tau(2);
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sys(3) = fx1;
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sys(4) = fx2;
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