mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-08-17 17:11:39 +08:00
192 lines
4.5 KiB
Matlab
192 lines
4.5 KiB
Matlab
function [sys,x0,str,ts] = Book7241_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 c1 b1 node c2 b2 c3 b3 s_new s_past inte_s
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% 神经网络采用2 - 7 - 1结构 采用3个2*7*1 神经网络
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node = 7;
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c1 = 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]; % 高斯函数的中心点矢量 维度 IN * MID 2*7
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b1 = 20 * 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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c2 = 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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b2 = 20 * ones(node,1);
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c3 = 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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b3 = 20 * ones(node,1);
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s_new = 0;
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s_past = s_new;
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inte_s = 0;
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sizes = simsizes;
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sizes.NumContStates = node*3; %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 = 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 * ones(node*3,1); % 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 c1 b1 node c2 b2 c3 b3 s_new s_past inte_s
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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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% qd = sin(t);
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dqd = cos(t);
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ddqd = -sin(t);
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qd = u(1);
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q = u(2);
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dq = u(3);
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ddq = u(4);
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e = qd - q; % e = qd - q
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de = dqd - dq;
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dde = ddqd - ddq;
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% 参数的定义
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kr = 0.1;
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kp = 15;
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kt = 15;
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xite = 5.0;
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gamam = 100;
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gamac = 100;
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gamag = 100;
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s_new = xite * e + de;
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dqr = xite * e + dqd;
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ddqr = xite * de + ddqd;
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% 神经网络的输入
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input = [q; dq];
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h1 = zeros(node , 1); %7*1矩阵
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h2 = zeros(node , 1); %7*1矩阵
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h3 = zeros(node , 1); %7*1矩阵
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for i =1:node
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h1(i) = exp(-(norm(input - c1(:,i))^2) / (b1(i)^2)); % 7*1
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end
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for i =1:node
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h2(i) = exp(-(norm(input - c2(:,i))^2) / (b2(i)^2)); % 7*1
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end
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for i =1:node
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h3(i) = exp(-(norm(input - c3(:,i))^2) / (b3(i)^2)); % 7*1
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end
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W1 = x(1:node); % 7*1
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W2 = x(node+1: node*2);
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W3 = x(node*2+1: node*3);
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% 权值的自适应律
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dw1 = gamam * h1 * ddqr * s_new;
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dw2 = gamac * h2 * dqr * s_new;
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dw3 = gamag * h3 * s_new;
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for i = 1:node
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sys(i) = dw1(i);
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sys(i+7) = dw2(i);
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sys(i+14) = dw3(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 node c2 b2 c3 b3 s_new s_past inte_s
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% 角度跟踪指令
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% dqd1 = 0.1*cos(t);
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% dqd2 = 0.1*cos(t);
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% qd = sin(t);
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dqd = cos(t);
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ddqd = -sin(t);
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qd = u(1);
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q = u(2);
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dq = u(3);
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ddq = u(4);
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e = qd - q; % e = qd - q
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de = dqd - dq;
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dde = ddqd - ddq;
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% 参数的定义
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kr = 0.1;
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kp = 15;
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ki = 15;
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xite = 5.0;
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gamam = 100;
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gamac = 100;
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gamag = 100;
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s_new = xite * e + de;
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dqr = xite * e + dqd;
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ddqr = xite * de + ddqd;
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% 神经网络的输入
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input = [q; dq];
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h1 = zeros(node , 1); %7*1矩阵
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h2 = zeros(node , 1); %7*1矩阵
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h3 = zeros(node , 1); %7*1矩阵
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for i =1:node
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h1(i) = exp(-(norm(input - c1(:,i))^2) / (b1(i)^2)); % 7*1
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end
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for i =1:node
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h2(i) = exp(-(norm(input - c2(:,i))^2) / (b2(i)^2)); % 7*1
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end
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for i =1:node
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h3(i) = exp(-(norm(input - c3(:,i))^2) / (b3(i)^2)); % 7*1
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end
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W1 = x(1:node); % 7*1
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W2 = x(node+1: node*2);
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W3 = x(node*2+1: node*3);
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% 神经网络的输出
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fx1 = W1' * h1;
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fx2 = W2' * h2;
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fx3 = W3' * h3;
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M_refer = fx1;
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C_refer = fx2;
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G_refer = fx3;
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% 名义模型控制律
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taum = M_refer*ddqr + C_refer*dqr + G_refer;
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% 鲁棒项
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taur = kr*sign(s_new);
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dt = 0.001;
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inte_s = inte_s + (s_past + s_new)*dt/2;
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tau = taum + kp*s_new + ki*inte_s + taur;
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sys(1) = tau;
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sys(2) = fx1;
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sys(3) = fx2;
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sys(4) = fx3;
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s_past = s_new;
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