数据驱动的NODE基于多级采样数据状态反控制
Long Zhao1, Shihua Li1, Rongjie Liu2
1School of Automation Southeast University, Nanjing, China.
ISA transactions
|November 10, 2023
概括
本研究引入了一种新的方法,用于稳定多速率采样数据系统,使用神经普通微分方程 (NODE). 它提供了选择多个样本周期的标准,以确保系统稳定性和优化控制器参数.
科学领域:
- 控制系统工程 控制系统工程
- 非线性系统动态 非线性系统动态
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多级采样数据系统提高了控制系统的性能和适应性,特别是混合信号类型.
- 这些系统的现有控制器可能过于复杂,并且缺乏关于采样间隔选择的明确指导.
- 在非线性多速率系统中确保稳定性和优化性能仍然是重大挑战.
研究的目的:
- 为多速率采样数据系统开发一个强大的稳定性标准.
- 为了优化控制器参数以提高系统性能.
- 解决非线性多速率系统中现有控制器的局限性.
主要方法:
- 一种新的方法,结合了系统稳定性和选择多个样本周期的实际考虑.
- 使用神经常规微分方程 (NODE) 在线性实用控制器中进行最佳参数选择.
- 设计和实施线性稳定器,使用三种采样级别的多速率采样数据.
主要成果:
- 建立了一个选择多个样本周期的标准,以保证系统稳定性.
- 使用NODE确定了最佳控制器参数,根据预定义的索引最大限度地提高性能.
- 稳定性和控制器设计在三个不同的采样水平上成功分析.
结论:
- 拟议的战略有效地保证了非线性多速率采样数据系统中的系统稳定性.
- 该方法提供了控制器参数选择的最佳方法,提高了系统性能.
- 通过模拟和现实世界单链机器人系统应用来证明有效性.
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