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 -