Joint state and dynamics estimation with high-gain observers and Gaussian process models

Proceedings of the ... American Control Conference/Proceedings of the American Control Conference, pp. 4027–4032

Abstract

With the rising complexity of dynamical systems generating ever more data, learning dynamics models appears as a promising alternative to physics-based modeling. However, the data available from physical platforms may be noisy and not cover all state variables. Hence, it is necessary to jointly perform state and dynamics estimation. In this paper, we propose interconnecting a high-gain observer and a dynamics learning framework, specifically a Gaussian process state-space model. The observer provides state estimates, which serve as the data for training the dynamics model. The updated model, in turn, is used to improve the observer. Joint convergence of the observer and the dynamics model is proved for high enough gain, up to the measurement and process perturbations. Simultaneous dynamics learning and state estimation are demonstrated on simulations of a mass-spring-mass system.

Authors 4

1. Mona Buisson-Fenet Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany

2. Valéry Morgenthaler

   Ansys (France)

   Affiliation as printed

   ANSYS Research Team, ANSYS France, Villeurbanne, France

3. Sebastian Trimpe Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany

4. Florent Di Meglio

   Université Paris Sciences et Lettres · École Nationale Supérieure des Mines de Paris · Centre Automatique et Systèmes

   Affiliation as printed

   Centre Automatique et Systèmes, Mines ParisTech, PSL University, Paris, France