基于Q学习的故障估计和对MIMO系统的故障耐受代学习控制.
Rui Wang1, Zhihe Zhuang1, Hongfeng Tao1
1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.
ISA transactions
|August 12, 2023
概括
本研究介绍了一种基于Q学习的故障估计和故障耐受性控制方法,用于代学习控制系统. 该方法适应不断变化的执行器故障,在重复性任务中提高控制性能.
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:
- 代学习控制 (ILC) 对执行器故障敏感,特别是随时间和试验而变化的未知故障.
- 这些故障严重挑战了ILC系统在重复性任务中的控制性能和可靠性.
研究的目的:
- 为面临执行器故障的ILC系统开发一个强大的故障估计 (FE) 和故障耐受性控制 (FTC) 方案.
- 通过智能故障管理,提高ILC系统的适应性和性能.
主要方法:
- 适应性故障估计 (FE) 采用了Q学习算法,使得估计器能够持续调整以适应不断变化的故障动态.
- 一个规范最佳的代学习控制 (NOILC) 框架被用于容错控制 (FTC).
- 根据Q学习算法提供的FE结果,FTC控制器进行动态调整,以抵消故障影响.
主要成果:
- 拟议的基于Q学习的FE和FTC方案有效地适应时间变化和试验变化的执行器故障.
- 整合FE的Q学习显著提高了NOILC框架的稳定性和性能.
- 在移动机器人平台上的模拟演示了拟议的故障管理策略的实际有效性和可靠性.
结论:
- 开发的基于Q学习的FE和FTC方案为解决ILC系统中执行器故障挑战提供了强大的解决方案.
- 这种方法提高了执行重复任务的控制系统的弹性和运行稳定性,特别是在动态故障场景中.
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