An iterative partition-based moving horizon estimator with coupled inequality constraints. (November 2015)

• Record Type: Journal Article
• Authors: Schneider, René; Hannemann-Tamás, Ralf; Marquardt, Wolfgang
• Is Part Of: Automatica. Volume 61(2015)
• Page Start: 302
• Page End: 307
• Publication Date: 2015-11
• Subjects: Moving horizon estimation -- System partitioning -- Sensitivity-driven optimization -- Iterative algorithm -- Stability; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2015.08.016
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: 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.