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https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-05 19:25:37 +08:00
63 lines
1.8 KiB
Matlab
63 lines
1.8 KiB
Matlab
function [sys,x0,str,ts] = Book532_Plant(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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sizes = simsizes;
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sizes.NumContStates = 2; %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 = 2; %设置系统输出的变量
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sizes.NumInputs = 1; %设置系统输入的变量
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sizes.DirFeedthrough = 0; %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 = [pi/60 0]; % 系统初始状态变量
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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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% 倒立摆状态方程
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ut = u(1);
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th = x(1); % 摆角
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dth = x(2); % 摆速
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% 参数的定义
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g = 9.8;
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mc = 1; %小车质量
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m = 0.1; %摆的质量
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l = 0.5;
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f_up = g*sin(th) - m*l*dth^2*cos(th)*sin(th)/(mc+m);
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f_down = l *(4/3 - m*(cos(th)^2)/(mc+m));
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f = f_up / f_down;
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g_up = cos(th)/(mc+m);
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g_down = l *(4/3 - m*(cos(th)^2)/(mc+m));
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g = g_up / g_down;
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some = f + g * ut;
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sys(1) = x(2);
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sys(2) = some;
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function sys=mdlOutputs(t,x,u) %产生（传递）系统输出
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sys(1) = x(1); %x1
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sys(2) = x(2); %x2
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