Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems

Autor:	Niazi, Muhammad Umar B., Cao, John, Sun, Xudong, Das, Amritam, Johansson, Karl Henrik
Rok vydání:	2022
Předmět:	Mathematics - Optimization and Control Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Mathematics - Dynamical Systems
Druh dokumentu:	Working Paper
Popis:	Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection. The observer then estimates the system's state in the original coordinates by inverting the transformation map. However, finding a suitable injective transformation whose inverse can be derived remains a primary challenge for general nonlinear systems. We propose a novel approach that uses supervised physics-informed neural networks to approximate both the transformation and its inverse. Our method exhibits superior generalization capabilities to contemporary methods and demonstrates robustness to both neural network's approximation errors and system uncertainties. Comment: Proceedings of the 2023 American Control Conference (ACC)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.01476 Zobrazit plný text záznamu View this record from Arxiv