2024-06-11-peralez24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Deep model-free KKL observer: A switching approach	This paper presents a new model-free methodology to learn Kazantzis-Kravaris-Luenberger (KKL) observers for nonlinear systems. We address three major difficulties arising in observer design: the peaking phenomenon, the noise sensitivity and the trade-off between convergence speed and robustness. We formulate the learning objective as an optimization problem, strictly minimizing the error of the observer estimates, without the need of adding explicit constraints or regularization terms. We further improve the performance with a switching approach, efficiently transitioning between two observers, respectively designed for the transient phase and the asymptotic convergence. Numerical results on the Van der Pol system, the Rössler attractor and on a bioreactor illustrate the gain of the method regarding the literature, in term of performance and robustness. Code available online: https://github.com/jolindien-git/DeepKKL	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	peralez24a	0	Deep model-free {KKL} observer: {A} switching approach	929	940	929-940	929	false	Peralez, Johan and Nadri, Madiha	given family Johan Peralez given family Madiha Nadri	2024-06-11		Proceedings of the 6th Annual Learning for Dynamics & Control Conference	242	inproceedings	date-parts 2024 6 11	https://proceedings.mlr.press/v242/peralez24a/peralez24a.pdf