基于神经动态编程的事件触发式对具有多个故障的非线性系统的容错控制.
Haowei Lin1, Weifeng Su2, Runlin Huang1
1Hong Kong Baptist University, Hong Kong, 999077, China; Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087, China.
概括
本研究介绍了非线性系统的在线故障耐受性控制 (FTC) 方案,解决执行器和传感器故障. 这种新的方法利用神经网络进行状态和故障估计,从而实现事件触发控制,并减少计算负载.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能在控制中
- 非线性系统动态 非线性系统动态
背景情况:
- 现有的基于神经动态编程 (NDP) 的故障耐受性控制 (FTC) 方法主要解决执行器故障,忽视传感器故障.
- 在线实施NDP-FTC受到激发条件 (PE) 的严格持久性和初始可接受控制要求的阻碍.
研究的目的:
- 为不确定非线性系统开发在线FTC方案,能够处理多个执行器和传感器故障.
- 集成神经网络,同时进行状态重建和故障估计.
- 通过事件触发控制 (ETC) 策略来减少计算资源的消耗.
主要方法:
- 用两个神经网络 (NN) 的神经观察器用于状态和故障估计.
- 一个批评者NN间接地推导出一个事件触发控制 (ETC) 政策.
- 基于NDP的FTC策略结合了基于NDP的ETC与执行器故障补偿器.
- 一个额外的稳定术语和经验重复技术放松了PE和初始控制条件.
主要成果:
- 同时准确地重建系统状态并估计执行器/传感器故障.
- 通过事件触发的FTC策略,大大减少了计算资源的消耗.
- 使用利亚普诺夫直接方法证明了观察者错误,故障估计错误和闭环系统的统一终极边界性.
结论:
- 拟议的在线FTC计划有效地处理不确定的非线性系统中的多个故障.
- 整合NN和事件触发控制提供了一个计算效率高的解决方案.
- 该方法缓解了传统的约束,使其能够在线实施.
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