Work

Towards gain tuning for numerical KKL observers

Year: 2022

Type: article

Abstract: This paper presents a first step towards tuning observers for general nonlinear systems. Relying on recent results around Kazantzis-Kravaris/Luenberger (KKL) observers, we propose an empirical criterion to guide the calibration of the observer, by trading off transient performance and sensitivity to measurement noise. We parametrize the gain matrix and evaluate this criterion over a family of observers for different parameter values. We then use neural networks to learn the mapping between the observer and the nonlinear system, and present a novel method to sample the state-space efficiently for nonlinear regression. We illustrate the merits of this approach in numerical simulations.

Source: RWTH Publications (RWTH Aachen)

DOI: 10.48550/arxiv.2204.00318

Authors: Buisson-Fenet, Mona, Bahr, Lukas, Morgenthaler, Valery, Di Meglio, Florent

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: RWTH Aachen University

OpenAlex ID: W4226395030

FWCI: 0.303

Cites:

Cited by: 3

Related to: 10