期刊
AUTOMATICA
卷 61, 期 -, 页码 302-307出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2015.08.016
关键词
Moving horizon estimation; System partitioning; Sensitivity-driven optimization; Iterative algorithm; Stability
We propose an iterative, partition-based moving horizon state estimator for large-scale linear systems that consist of interacting subsystems. Every subsystem estimates its own state and disturbance variables, taking into account the estimates received from neighboring subsystems. Compared to other partition-based moving horizon estimators, the proposed method has two unique features: it can handle coupled inequality constraints on the estimated variables and its state estimates come arbitrarily close to the optimal state estimates of a centralized moving horizon estimator. The applicability and performance of the proposed method are demonstrated on a numerical example and convergence and asymptotic stability are rigorously proven. (C) 2015 Elsevier Ltd. All rights reserved.
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