Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline

verfasst von

Jan-Hendrik Ewering, Björn Volkmann, Simon Friedrich Gerhard Ehlers, Thomas Seel, Michael Bernhard Meindl

Abstract

Intelligent real-world systems critically depend on expressive information about their system state and changing operation conditions, e.g., due to variation in temperature, location, wear, or aging. To provide this information, online inference and learning attempts to perform state estimation and (partial) system identification simultaneously. Current works combine tailored estimation schemes with flexible learningbased models but suffer from convergence problems and computational complexity due to many degrees of freedom in the inference problem (i. e., parameters to determine). To resolve these issues, we propose a procedure for data-driven offline conditioning of a highly flexible Gaussian Process (GP) formulation such that online learning is restricted to a subspace, spanned by expressive basis functions. Due to the simplicity of the transformed problem, a standard particle filter can be employed for Bayesian inference. In contrast to most existing works, the proposed method enables online learning of target functions that are nested nonlinearly inside a first-principles model. Moreover, we provide a theoretical quantification of the error, introduced by restricting learning to a subspace. A Monte-Carlo simulation study with a nonlinear battery model shows that the proposed approach enables rapid convergence with significantly fewer particles compared to a baseline and a state-of-the-art method.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung
Robotik & autonome Systeme

Typ

Aufsatz in Konferenzband

Seiten

4157-4164

Anzahl der Seiten

8

Publikationsdatum

16.12.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Modellierung und Simulation, Steuerung und Optimierung

Elektronische Version(en)

https://doi.org/10.1109/CDC56724.2024.10886241 (Zugang: Geschlossen)
https://doi.org/10.48550/arXiv.2409.09331 (Zugang: Offen)