通过实用的终端滑动模式和自适应动态编程,为多轴伺服系统提供近乎最佳的容错的受约束跟踪
Hanlin Dong1, Zhaoke Ning2, Zhiqiang Ma3
1School of Automation, Northwestern Polytechnical University, Xi'an, 710129, China.
ISA transactions
|December 5, 2023
概括
本研究介绍了机器人操纵器的新型控制策略,尽管存在不确定性和故障,但提高了跟踪精度. 该方法通过整合自适应动态编程和神经网络来实现精确的机器人控制,确保了强大的性能.
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:
- 机器人操纵器面临诸如参数不确定性,执行器故障和输入和等挑战.
- 现有的控制方法可能会与复杂的,现实世界的操作约束作斗争.
研究的目的:
- 为n链路机器人操纵器开发一个近乎最佳的跟踪控制.
- 为了有效地解决参数不确定性,时间配置文件故障和输入和约束.
主要方法:
- 设计一个实用的终端滑动模式 (PTSM) 分组器,具有线性项.
- 使用自适应动态编程 (ADP) 开发一个近乎最佳的滑动模式达到法.
- 整合一个辐射基函数神经网络 (RBFNN) 用于故障和和补偿.
主要成果:
- PTSM变频器确保控制状态的快速趋同到平衡状态.
- 基于ADP的覆盖法将系统动态限制在所需的区域.
- 由ADP的批评网络更新的RBFNN有效地弥补了执行器故障和输入和.
结论:
- 拟议的控制策略显著提高了机器人操纵器的跟踪性能和稳定性.
- 综合方法结合了PTSM,ADP和RBFNN,为复杂的机器人控制挑战提供了全面的解决方案.
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