基于BP神经网络的倒立摆智能控制研究

Research on intelligent control of inverted pendulum based on BP neural network

本文首先介绍了一种典型3层BP神经网络模型及其基本原理,然后在惯性坐标系内应用经典力学理论建立了直线一级倒立摆的动力学模型,得到以小车加速度作为输入的系统状态空间方程,在MATLAB中利用lqr函数求得系统最优反馈增益矩阵K并实现了稳定控制。在实时控制过程中不断采集数据样本并保存,用.m文件编写BP神经网络训练程序,对数据样本进行训练并自动生成BP神经网络控制器模块,最后在Simulink中搭建仿真模型进行仿真验证,通过分析仿真结果并进行实物验证,证明了所设计的BP神经网络控制器的合理性与有效性。

This paper first introduces a typical three-layer BP neural network model and its basic principle, then establishes the dynamic model of linear inverted pendulum in inertial coordinate system by applying classical mechanics theory and obtains the system state space equation with the acceleration of the car as the input. In MATLAB, the lqr function is used to obtain the optimal feedback gain matrix K and realize the stability control. In the process of real-time control, data samples are collected and saved continuously, and BP neural network training program is written with. M file to train data samples and generate BP neural network controller module automatically. Finally, a simulation model is built in Simulink for simulation verification. Through the analysis of simulation results and physical verification, the rationality and effectiveness of the designed BP neural network controller are proved.