混合神经网络-FOPID控制器的蝙蝠优化,用于强大的机器人操纵器控制
Bashra Kadhim Oleiwi1, Mohamed Jasim1, Ahmad Taher Azar2,3
1Department of Control and System Engineering, University of Technology, Baghdad, Iraq.
Frontiers in robotics and AI
|May 19, 2025
概括
本研究介绍了机器人操纵器的三种新型混合控制结构,将分数顺序PID控制与神经网络相结合. 类似神经网络的分数顺序比例-积分加导数控制器 (NN-FOPIPD) 在跟踪控制中表现出卓越的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 机器人操纵器控制由于未知的负载和干扰而面临准确性和稳定性的挑战.
- 现有的控制方法在多输入,多输出系统中与信号聊天作斗争.
研究的目的:
- 为三环刚性机器人操纵器 (3-LRRM) 提出和评估三种混合控制结构.
- 为了解决机器人操纵器控制中的准确性,稳定性和信号聊天的问题.
- 为了比较新型NN-FOPID混合控制器的性能.
主要方法:
- 开发了三个混合控制方案:NN-FOPIPD,NN + FOPID和ELNN-FOPID.
- 应用蝙蝠优化算法 (BOA) 调整控制器参数,最大限度地减少整数时方位错误 (ITSE).
- 使用MATLAB进行模拟分析,以评估控制器的性能,对不确定性和干扰的稳定性.
主要成果:
- 在拟议的混合控制方案中,NN-FOPIPD控制器表现最好.
- 所有拟议的控制器都显示出对系统参数不确定性,外部干扰和初始位置变化的稳定性.
- 该研究成功地缓解了3-LRRM系统中的信号聊天问题.
结论:
- 结合神经网络和分数顺序PID控制器的混合控制结构在机器人操纵器控制中提供了显著的改进.
- 该NN-FOPIPD结构是非常有效的精确和稳定的轨迹跟踪的刚性链路机器人操纵器.
- 提出的方法为面对不可预测条件的现实世界机器人应用提供了强大的解决方案.
相关概念视频
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01:26PD Controller: Design
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01:24PI Controller: Design
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01:19PID Controller
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01:24One-Degree-of-Freedom System
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01:07Time-Domain Interpretation of PD Control
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