Paper Info
Title
Structured Moving Horizon Estimation For Linear System Chains
Abstract
Computational aspects of moving horizon state estimation are studied for a class of chain networks with bidirectional coupling in the linear state dynamics, and measured outputs. Moving horizon estimation involves solving a quadratic program to minimize the estimation error relative to a model over a fixed window of past input-output observations. By exploiting the spatial structure of a chain, two algorithms for solving this quadratic program are considered. Both algorithms can be distributed in the sense that the computations associated with each sub-system component of the state depend only on information associated with the immediate neighbours. The algorithms differ in the way that the linear Karush-Kuhn-Tucker conditions for optimality are solved. Computational and information dependency overheads are analyzed. Numerical results are presented for a 1-D mass-spring-damper chain.
Year
DOI
Venue
2019
10.23919/ECC.2019.8795717
2019 18TH EUROPEAN CONTROL CONFERENCE (ECC)
Field
DocType
Citations 
Coupling,Linear system,Computer science,Horizon,Algorithm,Moving horizon estimation,Quadratic programming,Spatial structure,Computation
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Meichen Guo100.34
Adair Lang200.68
Michael Cantoni323938.80