对非线性系统的基于数据的代学习控制,这些系统的持续时间依赖于代
Yuxin Wu1, Deyuan Meng2, Jian Sun3
1National Key Laboratory of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, PR China.
ISA transactions
|December 28, 2025
概括
本研究介绍了基于数据的代学习控制 (ILC) 对不同持续时间的非线性系统. 一个新的ILC更新法通过利用系统数据确保了完美的跟踪.
科学领域:
- 控制工程 控制工程 控制工程
- 非线性系统动态 非线性系统动态
- 机器学习用于控制控制.
背景情况:
- 代学习控制 (ILC) 对于重复性任务至关重要.
- 局部利普希茨非线性系统由于复杂的动态存在挑战.
- 代依赖的持续时间使传统的ILC方法复杂化.
研究的目的:
- 开发基于数据的ILC策略,用于局部利普希茨非线性系统,其持续时间依赖于代.
- 设计一个ILC更新法,有效地利用收集的输入输出数据.
- 建立在这些系统中实现完美跟踪的条件.
主要方法:
- 一个测试框架,用于收集代特定的输入输出数据.
- 一个ILC更新法律,整合修改的输出来抵消持续时间的变化.
- 基于持久完全学习属性的分析.
主要成果:
- 为非线性系统提出了一项基于数据的ILC更新法.
- 该方法有效地弥补了依赖代的持续时间.
- 根据输出数据,根据输出数据推导出完美跟踪的必要和充分条件.
结论:
- 开发的基于数据的ILC适用于具有不规则动态的局部利普希茨非线性系统.
- 该方法为具有时间变化的运行长度的系统提供了可靠的解决方案.
- 在拟议的数据依赖条件下,可以实现完美的跟踪.
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