基于知识与数据融合驱动的可控制悬挂的强有力的耐故障控制研究
Honglin Zhu1, Weiping Ding2, Mingliang Yang1
1School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China.
Scientific reports
|December 20, 2023
概括
一种新的知识与数据融合策略增强了可控制悬挂系统的强大的故障耐受性控制. 这种方法有效地管理系统的不确定性和故障,通过合并的控制规律优化性能.
科学领域:
- 控制系统工程 控制系统工程
- 汽车工程 汽车工程
- 人工智能的人工智能
背景情况:
- 可控制的悬架系统需要强大的耐故障控制策略.
- 区分系统不确定性和故障是很有挑战性的.
- 现有的方法可能需要复杂的状态估计.
研究的目的:
- 为强大的故障耐受性控制提出知识数据融合控制战略.
- 为了解决没有明确状态估计的不确定性和故障.
- 为了优化系统性能,同时确保故障耐受性.
主要方法:
- 一种基于数据的方法,使用近距离政策优化 (PPO) 来实现"数据控制法".
- 一个以知识为导向的"天"控制法,用于性能优化,考虑到系统的非线性.
- 数字乘法数据和知识控制规律的融合.
主要成果:
- 成功开发了一个数据驱动的控制法来处理不确定性和故障.
- 在系统动态下设计了一个基于知识的控制规律,用于在系统动态下进行性能优化.
- 证明了控制规律的有效融合,以优化强大的故障耐受性.
- 通过模拟和实时实验在各种激发条件和故障场景下验证了该方法.
结论:
- 拟议的知识与数据融合战略为可控制的悬挂系统提供了有效的,强大的,耐故障的控制.
- 该方法成功地整合了数据驱动和知识驱动的方法,以提高性能和可靠性.
- 实验验证证证实了拟议的控制策略的可行性和有效性.
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