Performance Optimization via Sequential Processing for Nonlinear State Estimation of Noisy Systems

• 成果类型: Article
• 署名作者: Battilotti, Stefano
• 署名单位: Sapienza University Rome
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3095461
• 发表日期: 2022
• 页码: 2957-2972
• 关键词: Observers; CONVERGENCE; sensitivity; Noise measurement; Nonlinear systems; uncertainty; Measurement uncertainty; Noisy systems; nonlinear dynamics; observers

摘要

We propose a framework for designing observers for noisy nonlinear systems with global convergence properties and performing robustness and noise sensitivity. This framework comes out from the combination of a state norm estimator with a chain of filters, adaptively tuned by the state norm estimator. The state estimate is sequentially processed through the chain of filters. Each filter contributes to improving, by a certain amount, the estimation error performances of the previous filter in terms of noise sensitivity, and this amount is quantitatively evaluated using a comparison criterion, which considers the ratio of the asymptotic error norm bounds of two consecutive filters in the chain. A recursive algorithm is given for implementing the chain of filters and guaranteeing a sequential error performance optimization process. Simulations show the effectiveness of these chains of filters.