在联合连接的开关拓下,基于强化学习的灵活操纵器的边界优化控制
IEEE transactions on neural networks and learning systems
|September 23, 2025
概括
这项研究为灵活的操纵器引入了先进的容错控制,提高了追踪精度和稳定性,即使有通信问题. 新方法尽量减少错误和能源消耗,以实现强大的机器人系统性能.
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
- 应用数学 应用数学 应用数学
背景情况:
- 由于振动和外部干扰,灵活的操纵器在精确控制方面存在挑战.
- 现有的控制策略在具有间歇性通信的动态环境中往往缺乏稳定性.
- 容错对于复杂机器人系统的可靠运行至关重要.
研究的目的:
- 为灵活的操纵器开发极限优化的故障耐受性跟踪控制.
- 为了应对交换位图和异质线性领导者所带来的挑战.
- 为了最大限度地减少跟踪错误,振动偏移和控制能量.
主要方法:
- 分布式观察器的设计,用于在通信中断的情况下切换图表中的领导信息.
- 使用边界状态开发基于部分微分方程 (PDE) 的断层观察器 (FO).
- 为优化制定一种新的长期积分成本函数.
- 使用演员关键神经网络 (NN) 和强化学习 (RL) 接近边界最佳控制规律.
主要成果:
- 拟议的控制器确保灵活操纵器的错误状态具有统一的最终界限 (UUB).
- 通过数值模拟证明了有效性,验证了控制方法.
- 使用有限的边界状态信息,成功估计了未知的故障.
结论:
- 开发的边界优化故障耐受性控制对于复杂通信环境中的灵活操纵器是有效的.
- 集成基于PDE的故障观察器和基于RL的控制提供了一个强大的解决方案.
- 拟议的方法提高了跟踪性能,同时确保了系统稳定性和故障耐受性.
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