基于卡尔曼波器和卢恩伯格观察者的同时状态和参数估计方法:一个教程和评论
Amal Chebbi1, Matthew A Franchek1, Karolos Grigoriadis1
1Mechanical Engineering Department, Cullen College of Engineering, University of Houston, Houston, TX 77204, USA.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本综述比较了卡尔曼波器和卢恩伯格观察器在控制系统中同时进行状态和参数估计. 它详细介绍了它们的理论基础,算法和用于增强系统建模和自适应控制的应用.
科学领域:
- 控制系统工程 控制系统工程
- 动态系统建模 动态系统建模
- 估计理论 估计理论
背景情况:
- 同时状态和参数估计对于控制系统设计和动态建模至关重要.
- 这种功能提供实时系统洞察力,有助于发现潜在的机制,并使自适应控制.
研究的目的:
- 审查和比较分析两个主要类的状态和参数估计方法:卡尔曼过器和卢恩伯格观察器.
- 专注于理论基础,算法进步和应用领域.
主要方法:
- 对不确定线性和非线性系统的卡尔曼波器变体 (EKF,UKF,CKF,EnKF) 的调查.
- 对确定性设置的卢恩伯格观察器结构 (高增益,滑动模式,自适应) 的审查.
主要成果:
- 详细检查卡尔曼波器和卢恩伯格观察者的理论基础和算法发展.
- 进行比较分析,强调每个方法的优点,局限性和实际相关性.
结论:
- 卡尔曼波器和卢恩伯格观测器对于状态和参数估计都至关重要,对于不同的系统不确定性和决定性场景,每个都有不同的强度.
- 该审查提供了一个全面的指南,用于在各种工程应用中选择合适的估计方法.
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