Model Identification and Adaptive State Observation for a Class of Nonlinear Systems

• 成果类型: Article
• 署名作者: Bin, Michelangelo; Marconi, Lorenzo
• 署名单位: Imperial College London; University of Bologna
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3041238
• 发表日期: 2021
• 页码: 5621-5636
• 关键词: Adaptation models; observers; predictive models; Robustness; Nonlinear systems; uncertainty; data models; Adaptive observers; identification for control; high-gain observers; Least Squares; Wavelets

摘要

In this article, we consider the joint problems of state estimation and model identification for a class of continuous-time nonlinear systems in the output-feedback canonical form. An adaptive observer is proposed that combines an extended high-gain observer and a discrete-time identifier. The extended observer provides the identifier with a dataset permitting the identification of the system model and the identifier adapts the extended observer according to the new estimated model. The design of the identifier is approached as a system identification problem and sufficient conditions are presented that, if satisfied, allow different identification algorithms to be used for the adaptation phase. The cases of recursive least-squares and multiresolution black-box identification via wavelet-based identifiers are specifically addressed. Stability results are provided relating the asymptotic estimation error to the prediction capabilities of the identifier. Robustness with respect to additive disturbances affecting the system equations and measurements is also established in terms of an input-to-state stability property relative to the noiseless estimates.