| 李蕾,刘建鹏.采用改进神经网络PID控制的移动机器人轨迹追踪控制研究[J].井冈山大学自然版,2019,40(1):68-72 |
| 采用改进神经网络PID控制的移动机器人轨迹追踪控制研究 |
| TRAJECTORY TRACKING CONTROL OF MOBILE ROBOT BASED ON IMPROVED NEURAL NETWORK PID CONTROL |
| 投稿时间：2018-09-29 修订日期：2018-10-19 |
| DOI：10.3969/j.issn.1674-8085.2019.01.014 |
| 中文关键词: PID控制 BP神经网络结构 改进粒子群算法 追踪误差 |
| 英文关键词: PID control BP neural network structure improved particle swarm optimization tracking error |
| 基金项目: |
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| 摘要点击次数: 9847 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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