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Schiller, Julian D. ; Grüne, Lars ; Müller, Matthias A.:
Performance guarantees for optimization-based state estimation using turnpike properties.
Bayreuth ; Hannover
,
2025
. - 16 S.
DOI: https://doi.org/10.48550/arXiv.2501.18385
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| Projekttitel: |
Offizieller Projekttitel Projekt-ID Stochastic Optimal Control and MPC – Dissipativity, Risk and Performance GR 1569/25-1, BA 7477/3-1, project no. 499435839 Robuste Stabilität und Suboptimalität bei der Zustandsschätzung mit bewegtem Horizont---Von konzeptionellen zu praktisch relevanten Garantien project no. 426459964 |
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| Projektfinanzierung: |
Deutsche Forschungsgemeinschaft |
Abstract
In this paper, we develop novel accuracy and performance guarantees for optimal state estimation of general nonlinear systems (in particular, moving horizon estimation, MHE). Our results rely on a turnpike property of the optimal state estimation problem, which essentially states that the omniscient infinite-horizon solution involving all past and future data serves as turnpike for the solutions of finite-horizon estimation problems involving a subset of the data. This leads to the surprising observation that MHE problems naturally exhibit a leaving arc, which may have a strong negative impact on the estimation accuracy. To address this, we propose a delayed MHE scheme, and we show that the resulting performance (both averaged and non-averaged) is approximately optimal and achieves bounded dynamic regret with respect to the infinite-horizon solution, with error terms that can be made arbitrarily small by an appropriate choice of the delay. In various simulation examples, we observe that already a very small delay in the MHE scheme is sufficient to significantly improve the overall estimation error by 20-25 % compared to standard MHE (without delay). This finding is of great importance for practical applications (especially for monitoring, fault detection, and parameter estimation) where a small delay in the estimation is rather irrelevant but may significantly improve the estimation results.