Work

Deep Learning-based Luenberger observer design for discrete-time nonlinear systems

Year: 2021

Type: conference-paper

Abstract: In this paper we address the problem of observer design for nonlinear discrete-time systems. Combining the theory of so-called Kazantzis–Kravaris-Luenberger (KKL) observers and Deep Learning, we aim to identify the mapping which transforms a nonlinear dynamics to a stable linear system modulo an output injection and design an asymptotic discrete-time observer. The proposed approach leverages the power of Machine Learning to provide an algorithm based on an unsupervised learning of the mapping, which allows to properly explore the state space.The approach is illustrated on two examples of the autonomous case and two of the non-autonomous one. These examples have been taken from the literature and judiciously chosen to compare the proposed approach with existing results.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc45484.2021.9683485

Authors: Johan Peralez, Madiha Nadri

Institutions: Lyon 1 Université, Centre National de la Recherche Scientifique, Laboratoire d'Automatique, de Génie des Procédés et de Génie Pharmaceutique, Institut National des Sciences Appliquées de Lyon

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Data and Prior, Machine Learning and Control