使用强化学习对异质非线性MAS的同步进行规定的性能容错控制
IEEE transactions on cybernetics
|April 1, 2024
概括
这项研究引入了一种新的多代理系统 (MAS) 的故障耐受性控制方法,该方法可以确保尽管执行器故障和未知的系统动态,但仍能确保同步. 该方法使用强化学习来实现强大的性能和固定时间领先状态估计.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性系统是非线性系统.
背景情况:
- 多代理系统 (MAS) 由于执行器故障和有限的信息交换,在同步方面面临挑战.
- 在不确定性下,在MAS中实现规定的性能需要强大的控制策略.
研究的目的:
- 开发一种用于异质非线性MAS与未知执行器故障的新型规定的性能同步控制.
- 用分布式辅助感知系统来解决领导者信息可访问性问题.
- 设计一个数据驱动的故障耐受控制器,独立于系统动态.
主要方法:
- 一个分布式辅助感知系统,用于固定时间的领导者状态估计.
- 一个规定的性能故障耐受性控制 (FTC) 战略.
- 一个强化学习 (RL) 算法用于基于数据的控制器设计,使用政策之外的数据.
主要成果:
- 拟议的方法保证了领先状态估计错误的固定时间趋同.
- 在异质非线性MAS中实现用户定义的性能规范和故障容忍.
- 通过模拟来证明控制器的有效性,验证稳定性和规定的同步.
结论:
- 开发的数据驱动的FTC方法确保了MAS在故障时的强有力的同步与规定的性能.
- 该方法独立于系统动态,提供实用优势.
- 使用利亚普诺夫稳定定理和模拟验证了稳定性和性能.
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