Details
| Original language | English |
|---|---|
| Number of pages | 16 |
| Publication status | E-pub ahead of print - 7 Feb 2024 |
Abstract
Keywords
- eess.SY, cs.SY
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2024.
Research output: Working paper/Preprint › Preprint
}
TY - UNPB
T1 - Gaussian Process-Based Nonlinear Moving Horizon Estimation
AU - Wolff, Tobias M.
AU - Lopez, Victor G.
AU - Müller, Matthias A.
N1 - Funding Information: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 948679).
PY - 2024/2/7
Y1 - 2024/2/7
N2 - In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. In the proposed scheme, we take advantage of the properties of Gaussian processes. On the one hand, we approximate the system dynamics by the posterior means of the learned Gaussian processes (GPs). On the other hand, we exploit the posterior variances of the Gaussian processes to design the weighting matrices in the MHE cost function and account for the uncertainty in the learned system dynamics. The data collection and the tuning of the hyperparameters are done offline. We prove robust stability of the GP-based MHE scheme using a Lyapunov-based proof technique. Furthermore, as additional contribution, we analyze under which conditions incremental input/output-to-state stability (a nonlinear detectability notion) is preserved when approximating the system dynamics using, e.g., machine learning techniques. Finally, we illustrate the performance of the GP-based MHE scheme in a simulation case study and show how the chosen weighting matrices can lead to an improved performance compared to standard cost functions.
AB - In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. In the proposed scheme, we take advantage of the properties of Gaussian processes. On the one hand, we approximate the system dynamics by the posterior means of the learned Gaussian processes (GPs). On the other hand, we exploit the posterior variances of the Gaussian processes to design the weighting matrices in the MHE cost function and account for the uncertainty in the learned system dynamics. The data collection and the tuning of the hyperparameters are done offline. We prove robust stability of the GP-based MHE scheme using a Lyapunov-based proof technique. Furthermore, as additional contribution, we analyze under which conditions incremental input/output-to-state stability (a nonlinear detectability notion) is preserved when approximating the system dynamics using, e.g., machine learning techniques. Finally, we illustrate the performance of the GP-based MHE scheme in a simulation case study and show how the chosen weighting matrices can lead to an improved performance compared to standard cost functions.
KW - eess.SY
KW - cs.SY
U2 - 10.48550/arXiv.2402.04665
DO - 10.48550/arXiv.2402.04665
M3 - Preprint
BT - Gaussian Process-Based Nonlinear Moving Horizon Estimation
ER -