Details
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 63rd Conference on Decision and Control, CDC 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4157-4164 |
| Number of pages | 8 |
| ISBN (electronic) | 9798350316339 |
| ISBN (print) | 979-8-3503-1634-6 |
| Publication status | Published - 16 Dec 2024 |
| Event | 63rd IEEE Conference on Decision and Control, CDC 2024 - Milan, Italy Duration: 16 Dec 2024 → 19 Dec 2024 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| ISSN (Print) | 0743-1546 |
| ISSN (electronic) | 2576-2370 |
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.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Modelling and Simulation
- Mathematics(all)
- Control and Optimization
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2024 IEEE 63rd Conference on Decision and Control, CDC 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 4157-4164 (Proceedings of the IEEE Conference on Decision and Control).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline
AU - Ewering, Jan-Hendrik
AU - Volkmann, Björn
AU - Ehlers, Simon Friedrich Gerhard
AU - Seel, Thomas
AU - Meindl, Michael Bernhard
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/12/16
Y1 - 2024/12/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=86000505657&partnerID=8YFLogxK
U2 - 10.1109/CDC56724.2024.10886241
DO - 10.1109/CDC56724.2024.10886241
M3 - Conference contribution
AN - SCOPUS:86000505657
SN - 979-8-3503-1634-6
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4157
EP - 4164
BT - 2024 IEEE 63rd Conference on Decision and Control, CDC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 63rd IEEE Conference on Decision and Control, CDC 2024
Y2 - 16 December 2024 through 19 December 2024
ER -