摘要
为了提高双轮移动机器人运动轨迹追踪精度,采用改进粒子群算法优化BP神经网络PID控制器,并对控制效果进行仿真验证。创建双轮移动机器人模型简图,给出运动轨迹误差方程式。在传统PID控制基础上增加BP神经网络结构,引用粒子群算法并对其进行改进,采用改进粒子群算法优化BP神经网络PID控制调整参数,给出双轮移动机器人PID控制参数优化流程。采用数学软件MATLAB对双轮移动机器人轨迹追踪误差进行仿真验证,并与传统PID控制追踪误差进行对比。仿真曲线显示:在理想环境中,双轮移动机器人采用两种控制方法都能较好地实现轨迹追踪,追踪误差较小;在干扰波形环境中,传统PID控制双轮移动机器人追踪误差较大,而改进PID控制双轮移动机器人追踪误差较小。采用改进粒子群算法优化BP神经网络PID控制器,可以提高移动机器人运动轨迹追踪精度。
In order to improve the tracking accuracy of two-wheeled mobile robot,the improved particle swarm optimization algorithm is used to optimize the BP neural network PID controller,and the control effect is verified by simulation.The model of two wheeled mobile robot is created and the error equation of motion trajectory is given.The structure of BP neural network is added to the traditional PID control,and the particle swarm algorithm is used to improve it.The improved particle swarm algorithm is used to optimize the PID control parameters of BP neural network,and the PID control parameters optimization process of two-wheeled mobile robot is given.The trajectory tracking error of two-wheeled mobile robot is simulated and verified by MATLAB,and compared with the traditional PID control tracking error.Simulation curves show that in ideal environment,two-wheeled mobile crobots can achieve better trajectory tracking with less tracking error.In disturbance waveform environment,the tracking error of traditional PID control two-wheeled mobile robots is larger,while which of the improved PID control two-wheeled mobile robots is smaller.The improved particle swarm optimization algorithm is used to optimize BP neural network PID controller,which can improve the tracking accuracy of mobile robot.
作者
李蕾
刘建鹏
LI Lei;LIU Jian-peng(School of Mechanical Engineering,Anhui Sanlian University,Hefei,Anhui 230601,China;Meteorological detection system division,Anhui four creates an electronic Limited by Share Ltd,Hefei,Anhui230000,China)
出处
《井冈山大学学报(自然科学版)》
2019年第1期68-72,共5页
Journal of Jinggangshan University (Natural Science)
关键词
PID控制
BP神经网络结构
改进粒子群算法
追踪误差
PID control
BP neural network structure
improved particle swarm optimization
tracking error