对具有周期性跳跃的混合系统进行数据驱动的混合开关增益估计
Xuan Jia1, Junfeng Zhang1, Tarek Raïssi2
1School of Information and Communication Engineering, Hainan University, Haikou 570228, China.
ISA transactions
|December 18, 2025
概括
这项研究引入了数据驱动的方法,用于在未知的混合系统中进行状态估计,具有周期性跳跃,绕过了精确模型的需求. 该方法通过使用测量数据,在行走机器人模拟中证明了全球指数稳定性的保证.
科学领域:
- 控制系统工程 控制系统工程
- 系统理论 系统理论
- 机器人技术 机器人技术 机器人技术
背景情况:
- 准确的状态估计对于控制混合系统至关重要.
- 基于模型的观察者需要精确的系统模型,这些模型通常是不可用的,特别是对于具有周期性跳跃的系统.
- 现有的方法与未知的混合系统和周期动力学作斗争.
研究的目的:
- 为具有周期性跳跃的未知混合系统开发数据驱动的估计方法.
- 消除对明确系统模型识别的需要.
- 确保对估计稳定的理论保证.
主要方法:
- 使用S-lemma直接从杂的输入/测量数据构建估计定律.
- 应用行为系统理论来导出解释数据的系统.
- 在稳定性标准中使用基于数据的线性矩阵不等式.
主要成果:
- 介绍了两类数据驱动估计方法.
- 设计了一个数据驱动的混合估计方案,删除了模型识别步骤.
- 在温和条件下,全球指数稳定性在理论上是有保证的.
结论:
- 提出的数据驱动方法有效地解决了对具有周期性跳跃的未知混合系统的状态估计.
- 该方法已成功应用于数据驱动的混合交换增益估计框架.
- 用行走机器人的模拟验证了数据驱动估计技术的有效性.
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