基于强化学习的有限时间故障耐受性控制对操作器有执行器故障的操纵器
IEEE transactions on cybernetics
|April 24, 2025
概括
本研究介绍了一种用于操纵器系统的新型有限时间故障耐受控制器,它结合了非单元终端滑动模式 (NTSM) 和强化学习 (RL) 来增强强性和应对执行器故障的故障耐受性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 在工程领域的人工智能.
- 容错控制系统 容错控制系统
背景情况:
- 操纵器系统容易发生执行器故障,损害操作可靠性.
- 现有的耐故障控制方法通常需要精确的系统模型,并与不确定性作斗争.
- 整合先进的控制策略对于提高机器人系统的稳定性和安全性至关重要.
研究的目的:
- 为操纵系统开发一种新的有限时间故障耐受控制器.
- 用混合NTSM和RL方法解决执行器故障和参数不确定性.
- 为了提高机器人操纵器的强度和提高故障耐受性.
主要方法:
- 集成非单元终端滑动模式 (NTSM) 控制,以控制对不确定性的稳定性.
- 强化学习 (RL) 的应用与一个关键参与者网络来近似非线性动态.
- 制定适应性定律来补偿执行器故障和莱普诺夫稳定性分析.
主要成果:
- 拟议的控制器证明了闭环系统的半全球实用有限时间稳定性.
- 有效地减轻参数不确定性和执行器故障影响.
- 减少对精确模型准确性的依赖,从而增加了系统故障容忍度.
结论:
- 结合NTSM和RL策略,为操纵器控制提供了强大且耐故障的解决方案.
- 在Kinova Jaco 2平台上的模拟和实验验证证证了算法的有效性.
- 这种方法在出现故障时显著提高了机器人系统的可靠性和性能.
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