Details
| Original language | English |
|---|---|
| Pages (from-to) | 2199-2214 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 68 |
| Issue number | 4 |
| Early online date | 10 May 2022 |
| Publication status | Published - Apr 2023 |
Abstract
Keywords
- Cost function, Estimation, Moving horizon estimation (MHE), Noise measurement, Nonlinear systems, Observers, Robust stability, Stability, Standards, State estimation, nonlinear systems, state estimation, stability
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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In: IEEE Transactions on Automatic Control, Vol. 68, No. 4, 04.2023, p. 2199-2214.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Suboptimal Nonlinear Moving Horizon Estimation
AU - Schiller, Julian D.
AU - Müller, Matthias A.
N1 - Publisher Copyright: © 1963-2012 IEEE.
PY - 2023/4
Y1 - 2023/4
N2 - In this paper, we propose a suboptimal moving horizon estimator for a general class of nonlinear systems. For the stability analysis, we transfer the "feasibility-implies-stability/robustness" paradigm from model predictive control to the context of moving horizon estimation in the following sense: Using a suitably defined, feasible candidate solution based on an auxiliary observer, robust stability of the proposed suboptimal estimator is inherited independently of the horizon length and even if no optimization is performed. We apply the proposed suboptimal estimator to a nonlinear chemical reactor process, verify the theoretical assumptions, and show that even a few iterations of the optimizer are sufficient to significantly improve the estimation results of the auxiliary observer. Furthermore, we illustrate the flexibility of the proposed design by employing different solvers and compare the performance with two state-of-the-art fast MHE schemes from the literature.
AB - In this paper, we propose a suboptimal moving horizon estimator for a general class of nonlinear systems. For the stability analysis, we transfer the "feasibility-implies-stability/robustness" paradigm from model predictive control to the context of moving horizon estimation in the following sense: Using a suitably defined, feasible candidate solution based on an auxiliary observer, robust stability of the proposed suboptimal estimator is inherited independently of the horizon length and even if no optimization is performed. We apply the proposed suboptimal estimator to a nonlinear chemical reactor process, verify the theoretical assumptions, and show that even a few iterations of the optimizer are sufficient to significantly improve the estimation results of the auxiliary observer. Furthermore, we illustrate the flexibility of the proposed design by employing different solvers and compare the performance with two state-of-the-art fast MHE schemes from the literature.
KW - Cost function
KW - Estimation
KW - Moving horizon estimation (MHE)
KW - Noise measurement
KW - Nonlinear systems
KW - Observers
KW - Robust stability
KW - Stability
KW - Standards
KW - State estimation
KW - nonlinear systems
KW - state estimation
KW - stability
UR - http://www.scopus.com/inward/record.url?scp=85132512423&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2108.13750
DO - 10.48550/arXiv.2108.13750
M3 - Article
VL - 68
SP - 2199
EP - 2214
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
SN - 0018-9286
IS - 4
ER -