控制输入约束和故障下的非线性系统的增强控制:基于神经网络的综合模糊滑动模式方法
Guangyi Yang1, Stelios Bekiros2, Qijia Yao3
1Information Center, Hunan Institute of Metrology and Test, Changsha 410014, China.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了非线性系统的新型控制方法,结合神经网络和模糊逻辑来解决执行器故障和局限性. 这种方法确保了有限时间稳定性和在现实应用中强大的性能.
科学领域:
- 控制系统工程 控制系统工程
- 在工程领域的人工智能.
- 非线性系统动态 非线性系统动态
背景情况:
- 现有的控制技术往往忽视了系统故障和物理限制,阻碍了现实世界的应用.
- 需要先进的控制策略,以适应实际系统中的执行器故障和约束.
研究的目的:
- 为非线性系统开发一种创新的控制方法,以稳定处理控制执行器故障和物理限制.
- 为了提高系统的适应性和减少聊天,使用具有模糊逻辑调节的智能观察员来提高系统的适应性.
主要方法:
- 一个基于神经网络的滑动模式控制算法,与模糊逻辑系统集成.
- 一个智能观察者结合了一个模糊逻辑引擎来管理执行器故障和限制.
- 有限时间稳定性分析以验证控制设计.
主要成果:
- 拟议的控制器有效地保持系统调节,尽管控制输入的限制和故障.
- 具有模糊逻辑的智能观察者减少了系统的喋喋不休,提高了适应性.
- 证明了闭环系统的有限时间收和稳定性.
结论:
- 开发的控制策略为面临执行器故障和局限性的非线性系统提供了强大的解决方案.
- 该方法确保了有限时间稳定性和自主和非自主系统的有效性能.
- 这项研究促进了控制系统在具有挑战性的现实场景中的实际实施.
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