An LMI-Based Robust Nonlinear Adaptive Observer for Disturbed Regression Models

• 成果类型: Article
• 署名作者: Rios, Hector; de Loza, Alejandra Ferreira; Efimov, Denis; Franco, Roberto
• 署名单位: Universite de Lille; Centre National de la Recherche Scientifique (CNRS); Inria
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3342890
• 发表日期: 2024
• 页码: 4035-4041
• 关键词: Heuristic algorithms; Perturbation methods; observers; CONVERGENCE; Adaptation models; Linear matrix inequalities; Approximation algorithms; Adaptive control; parameter identification; regression models

摘要

This article deals with the problem of time-varying parameter identification in dynamical regression models affected by disturbances. The disturbances comprise time-dependent external perturbations and nonlinear unmodeled dynamics. With this aim in mind, we propose a robust nonlinear adaptive observer. The algorithm ensures the asymptotic convergence of the parameter identification error to an acceptably small region around the origin in the presence of disturbances. The synthesis of the adaptive observer is given in terms of linear matrix inequalities, providing a constructive design method. An academic example and a low inertia power system illustrate the robustness and the applicability of the proposed adaptive observer for the time-varying parameter identification problem.