Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling

• Main Authors: Yan, Mingxue, Han, Minghao, Law, Adrian Wing-Keung, Yin, Xunyuan
• Other Authors: School of Chemistry, Chemical Engineering and Biotechnology
• Format: Article
• Language: English
• Published: 2025
• Subjects: Engineering; Koopman operator; Moving horizon estimation
• Online Access: https://hdl.handle.net/10356/182689
• Institution: Nanyang Technological University

In this article, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural networks—the state lifting network and the noise...