mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-05 11:09:47 +08:00
174 lines
3.8 KiB
Matlab
174 lines
3.8 KiB
Matlab
function [sys,x0,str,ts] = Book6242_Controller(t,x,u,flag)
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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 c b node
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% 神经网络采用4 - 7 - 2结构
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node = 7;
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c = 1 * [-1.5 -1 -0.5 0 0.5 1 1.5;
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-1.5 -1 -0.5 0 0.5 1 1.5]; % 高斯函数的中心点矢量 维度 IN * MID 2*7
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b = 10 * ones(node,1); % 高斯函数的基宽 维度node * 1 5*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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sizes = simsizes;
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sizes.NumContStates = node*2; %设置系统连续状态的变量 W V
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sizes.NumDiscStates = 0; %设置系统离散状态的变量
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sizes.NumOutputs = 6; %设置系统输出的变量
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sizes.NumInputs = 6; %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 * ones(node*2,1); % 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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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 node
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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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% 角度跟踪指令
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qd1 = 0.1*sin(t);
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qd2 = 0.1*sin(t);
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dqd1 = 0.1*cos(t);
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dqd2 = 0.1*cos(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 = qd1 - q1; % e = qd - q
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e2 = qd2 - q2;
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de1 = dqd1 - dq1;
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de2 = dqd2 - dq2;
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% 参数的定义
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kv = [10 0; 0 10];
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ita = [15 0;0 15];
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xite1 = 5;
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xite2 = 5;
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% 滑模函数
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s1 = de1 + xite1*e1;
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s2 = de2 + xite2*e2;
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s = [s1;s2];
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Input1 = [e1;de1];
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hw = zeros(node, 1); %7*1矩阵
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for j = 1:node
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hw(j) = exp(-(norm(Input1 - c(:,j))^2) / (b(j)^2));
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end
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Input2 = [e2;de2];
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hv = zeros(node, 1); %7*1矩阵
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for j = 1:node
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hv(j) = exp(-(norm(Input2 - c(:,j))^2) / (b(j)^2));
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end
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W = x(1:node); % node*1
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V = x(node+1:2*node);
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% W权值的更新
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dw1 = ita(1)*hw*s(1);
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dw2 = ita(4)*hv*s(2);
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for i = 1:node
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sys(i) = dw1(i);
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sys(i+node) = dw2(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 c b node
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% 角度跟踪指令
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qd1 = 0.1*sin(t);
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qd2 = 0.1*sin(t);
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dqd1 = 0.1*cos(t);
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dqd2 = 0.1*cos(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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K1 = u(5);
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K2 = u(6);
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e1 = qd1 - q1; % e = qd - q
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e2 = qd2 - q2;
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de1 = dqd1 - dq1;
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de2 = dqd2 - dq2;
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% 参数的定义
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kv = [20 0; 0 20];
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ita = [15 0;0 15];
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xite1 = 5;
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xite2 = 5;
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epc = 2;
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bd = 2.1;
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% 滑模函数
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s1 = de1 + xite1*e1;
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s2 = de2 + xite2*e2;
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d1 = norm(s1)/sqrt(xite1^2+1);
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d2 = norm(s2)/sqrt(xite2^2+1);
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% 滑模函数
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s1 = de1 + xite1*e1;
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s2 = de2 + xite2*e2;
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s = [s1;s2];
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Input1 = [e1;de1];
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hw = zeros(node, 1); %7*1矩阵
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for j = 1:node
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hw(j) = exp(-(norm(Input1 - c(:,j))^2) / (b(j)^2));
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end
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Input2 = [e2;de2];
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hv = zeros(node, 1); %7*1矩阵
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for j = 1:node
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hv(j) = exp(-(norm(Input2 - c(:,j))^2) / (b(j)^2));
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end
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W = x(1:node); % node*1
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V = x(node+1:2*node);
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% 神经网络的输出
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fx1 = W' * hw;
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fx2 = V' * hv;
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v = -(epc+bd)*sign(s);
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temp1 = (abs(K1)+1)*sign(s1);
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temp2 = (abs(K2)+1)*sign(s2);
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v_new = -[temp1; temp2];
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tau = [fx1;fx2]+kv*s-v;
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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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sys(5) = s1;
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sys(6) = s2;
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