mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-05 19:25:37 +08:00
174 lines
4.0 KiB
Matlab
174 lines
4.0 KiB
Matlab
function [sys,x0,str,ts] = Book6332_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 一个 4*7*2结构 输入[e1 e2 de1 de2] 对应输出 [tau1 tau2]
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node = 7;
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c = 1 * [-1.5 -1.0 -0.5 0 0.5 1 1.5;
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-1.5 -1.0 -0.5 0 0.5 1 1.5;
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-1.5 -1.0 -0.5 0 0.5 1 1.5;
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-1.5 -1.0 -0.5 0 0.5 1 1.5]; % 高斯函数的中心点矢量 维度 IN * MID 4*7
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b = 10 * ones(node,1); % 高斯函数的基宽 维度node * 1 7*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 = 4; %设置系统输出的变量
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sizes.NumInputs = 8; %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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dqd1 = cos(t);
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dqd2 = cos(t);
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qd1 = u(1);
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qd2 = u(2);
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q1 = u(3);
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q2 = u(4);
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dq1 = u(5);
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dq2 = u(6);
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e1 = q1 - qd1; % e = qd - q
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e2 = q2 - qd2;
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de1 = dq1 - dqd1;
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de2 = dq2 - dqd2;
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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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xite = 1500;
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alpha = 20;
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gama = 0.05;
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input = [e; de];
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h = zeros(node , 1); %7*1矩阵
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for i =1:node
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h(i) = exp(-(norm(input - c(:,i))^2) / (b(i)^2)); % 7*1
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end
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W = [x(1) x(2) x(3) x(4) x(5) x(6) x(7);
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x(8) x(9) x(10) x(11) x(12) x(13) x(14)]'; % 7*2
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x2_1 = de + alpha * e;
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% 权值的自适应律
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dw = -xite * x2_1 * h'; %
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% dw = dw';
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for i = 1:node
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sys(i) = dw(1,i);
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sys(i+7) = dw(2,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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% dqd1 = cos(t);
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% dqd2 = cos(t);
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ddqd1 = -sin(t);
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ddqd2 = -sin(t);
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qd1 = u(1);
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qd2 = u(2);
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q1 = u(3);
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q2 = u(4);
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dq1 = u(5);
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dq2 = u(6);
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ddq1 = u(7);
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ddq2 = u(8);
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dqd1 = cos(t);
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dqd2 = cos(t);
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ddq = [ddq1; ddq2];
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ddqd = [ddqd1; ddqd2];
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dqd = [dqd1; dqd2];
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e1 = q1 - qd1; % e = q - qd
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e2 = q2 - qd2;
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de1 = dq1 - dqd1;
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de2 = dq2 - dqd2;
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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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xite = 1500;
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alpha = 20;
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gama = 0.05;
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m1 = 1;
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m2 = 1.5;
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r1 = 1;
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r2 = 0.8;
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M11 = (m1 + m2)*r1^2 + m2*r2^2 + 2*m2*r1*r2*cos(q2);
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M12 = m2*r2^2 + m2*r1*r2*cos(q2);
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M21 = M12;
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M22 = m2 * r2^2;
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V12 = m2*r1*sin(q2);
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G1 = (m1+m2)*r1*cos(q2) + m2*r2*cos(q1+q2);
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G2 = m2*r2*cos(q1+q2);
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M = [M11 M12;
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M21 M22];
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V = [-V12*dq2 -V12*(dq1+dq2);
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V12*q1 0];
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G = [G1; G2];
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D = [10*dq1 + 30*sign(dq1); 10*dq2 + 30*sign(dq2)];
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input = [e; de];
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h = zeros(node , 1); %7*1矩阵
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for i =1:node
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h(i) = exp(-(norm(input - c(:,i))^2) / (b(i)^2)); % 7*1
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end
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W = [x(1) x(2) x(3) x(4) x(5) x(6) x(7);
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x(8) x(9) x(10) x(11) x(12) x(13) x(14)]'; % 7*2
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% 网络输出
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fx = W' * h;
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omiga = M * alpha * de + V * alpha * e;
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x2_1 = de + alpha * e;
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% 反馈控制律
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ut = -omiga - 1/(2*gama^2) * x2_1 + fx - 1/2*x2_1;
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tau = ut + M * ddqd + V * dqd + G;
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sys(1) = tau(1);
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sys(2) = tau(2);
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sys(3) = fx(1);
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sys(4) = fx(2);
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