mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-05 19:25:37 +08:00
147 lines
3.4 KiB
Matlab
147 lines
3.4 KiB
Matlab
function [sys,x0,str,ts] = Book6141_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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global c b node
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% 神经网络采用2-5-1结构
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node = 5;
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c = 0.5 * [-2 -1 -0 1 2;
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-2 -1 -0 1 2]; % 高斯函数的中心点矢量 维度 IN * MID 2*5
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b = 2 * ones(5,1); % 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; %设置系统连续状态的变量 W
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sizes.NumDiscStates = 0; %设置系统离散状态的变量
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sizes.NumOutputs = 3; %设置系统输出的变量
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sizes.NumInputs = 3; %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(node,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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IN = 2;
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node = 5;
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Out = 1;
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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); % e = q - qd
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dq = u(3);
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e = q - qd;
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de = dq - dqd;
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% 参数的定义
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M = 10;
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gama = 1200;
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alph = 3;
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kp = alph^2;
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kv = 2 * alph;
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Q = [50 0; 0 50];
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A = [0 1; -kp -kv];
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P = lyap(A' , Q);
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B = [0; 1/M];
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k1 = 0.001;
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Input = [e; de];
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h = zeros(node , 1); %5*1矩阵
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for i =1:node
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h(i) = exp(-(norm(Input - c(:,i))^2) / (2*b(i)^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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method = 1;
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if method == 1 % 自适应方法一
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dw = gama * h * Input' * P * B;
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for i = 1:node
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sys(i) = dw(i);
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end
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else % 自适应方法二
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dw = gama * h * Input' * P * B + k1 * gama * norm(Input) * W;
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for i = 1:node
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sys(i) = dw(i);
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end
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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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IN = 2;
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node = 5;
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Out = 1;
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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); % e = q - qd
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dq = u(3);
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e = q - qd;
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de = dq - dqd;
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% 参数的定义
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M = 10;
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gama = 1200;
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alph = 3;
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kp = alph^2;
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kv = 2 * alph;
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Q = [50 0; 0 50];
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A = [0 1; -kp -kv];
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P = lyap(A' , Q);
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B = [0; 1/M];
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k1 = 0.001;
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Input = [e; de];
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h = zeros(node , 1); %5*1矩阵
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for i =1:node
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h(i) = exp(-(norm(Input - c(:,i))^2) / (2*b(i)^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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% 神经网络的输出
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fx = W' * h;
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d = -15 * dq - 30 *sign(dq);
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some = 1;
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if some == 1
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ut = M * (ddqd - kv*de - kp*e) - fx; % RBF逼近未知干扰f
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elseif some == 2
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ut = M * (ddqd - kv*de - kp*e) - d;
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else
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ut = M * (ddqd - kv*de - kp*e);
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end
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sys(1) = ut;
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sys(2) = fx;
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sys(3) = d;
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