具有马尔科夫跳跃目标的LTI系统的无模型强大的跟踪控制:数据信息化方法
Zhipeng Wang1, Shi Li1, Shicheng Huo2
1School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China.
ISA transactions
|November 15, 2025
概括
这项研究开发了用于未知系统的数据驱动控制器,具有马尔科夫跳转引用,确保可靠的输出跟踪而不需要持续激发. 控制器是使用数据信息性推导的,并在无人驾驶车辆模型上进行验证.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
- 随机系统 随机系统 随机系统
背景情况:
- 强大的输出跟踪对于未知的线性时间不变系统至关重要.
- 马尔科夫跳跃系统产生的参考信号引入了复杂性.
- 传统方法通常需要持续激发系统识别,这并不总是可行的.
研究的目的:
- 为未知线性时间不变系统开发数据驱动的强大的输出跟踪控制器.
- 通过利用数据信息性来规避持续兴奋的需要.
- 根据观察到的数据,建立传输零,可控性和随机稳定的条件.
主要方法:
- 基于数据信息性的控制器设计,用于一系列系统.
- 对传输零值,可控性和随机稳定的充分/必要条件的分析.
- 导出数据信息性条件,以实现可靠的输出跟踪.
- 使用基于数据的线性矩阵不等式用于随机稳定性的控制器增益导出.
主要成果:
- 对于与观察到的数据相对应的系统,开发了共同的控制器.
- 数据信息性条件是为了实现可靠的输出跟踪而导出的.
- 控制器收益成功地根据随机稳定性标准推导出.
- 建议的控制策略在无人驾驶车辆模型上得到验证.
结论:
- 数据驱动的方法有效地实现了对未知系统的可靠输出跟踪,并具有马尔科夫跳转引用.
- 该方法绕过了持续刺激的需要,提供了实际的优势.
- 模拟结果证实了开发的控制策略的有效性和稳定性.
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