Hybrid Multi-Observer for Improving Estimation Performance

• 成果类型: Article
• 署名作者: Petri, Elena; Postoyan, Romain; Astolfi, Daniele; Nesic, Dragan; Andrieu, Vincent
• 署名单位: Eindhoven University of Technology; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; Universite Claude Bernard Lyon 1; Centre National de la Recherche Scientifique (CNRS); University of Melbourne
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3500792
• 发表日期: 2025
• 页码: 3256-3271
• 关键词: Observers; estimation; noise; Noise measurement; CONVERGENCE; Nonlinear systems; stability analysis; COSTS; Tuning; monitoring; Hybrid dynamical systems; nonlinear dynamical systems; observers; State estimation

摘要

Various methods are nowadays available to design observers for broad classes of systems, where the primary focus is on establishing the convergence of the estimated states. Nevertheless, the question of the tuning of the observer to achieve satisfactory estimation performance remains largely open. In this context, we present a general design framework for the online tuning of the observer gains. Our starting point is a robust nominal observer designed for a general nonlinear system, for which an input-to-state stability property can be established. Our goal is then to improve the performance of this nominal observer. We present for this purpose a new hybrid multi-observer scheme, whose flexibility can be exploited to enforce various desirable properties, e.g., fast convergence and good sensitivity to measurement noise. We prove that an input-to-state stability property also holds for the proposed scheme and, importantly, we ensure that the estimation performance in terms of a quadratic cost is (strictly) improved. We illustrate the efficiency of the approach in improving the performance of given nominal observers in two numerical examples (Van der Pol oscillator and lithium-ion battery model).