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Optimal sensor scheduling for multiple linear dynamical systems.

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Title: Optimal sensor scheduling for multiple linear dynamical systems.
Authors: Han, Duo1,2 dhanaa@ntu.edu.sg, Wu, Junfeng3 junfengw@kth.se, Zhang, Huanshui4 hszhang@sdu.edu.cn, Shi, Ling2 eesling@ust.hk
Source: Automatica. Jan2017, Vol. 75, p260-270. 11p.
Subjects: Markov processes, Dynamical systems, Parameter estimation, Optimal control theory, Kalman filtering
Abstract: We consider the design of an optimal collision-free sensor schedule for a number of sensors which monitor different linear dynamical systems correspondingly. At each time, only one of all the sensors can send its local estimate to the remote estimator. A preliminary work for the two-sensor scheduling case has been studied in the literature. The generalization into multiple-sensor scheduling case is shown to be nontrivial. We first find a necessary condition of the optimal solution which can significantly reduce the feasible optimal solution space without loss of performance. By modelling a finite-state Markov decision process (MDP) problem, we can numerically search an asymptotic periodic schedule which is proven to be optimal. Some simple but effective suboptimal schedules for any systems are proposed. We also find a lower bound of the optimal cost, which enables us to quantify the performance gap between any suboptimal schedule and an optimal one. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:We consider the design of an optimal collision-free sensor schedule for a number of sensors which monitor different linear dynamical systems correspondingly. At each time, only one of all the sensors can send its local estimate to the remote estimator. A preliminary work for the two-sensor scheduling case has been studied in the literature. The generalization into multiple-sensor scheduling case is shown to be nontrivial. We first find a necessary condition of the optimal solution which can significantly reduce the feasible optimal solution space without loss of performance. By modelling a finite-state Markov decision process (MDP) problem, we can numerically search an asymptotic periodic schedule which is proven to be optimal. Some simple but effective suboptimal schedules for any systems are proposed. We also find a lower bound of the optimal cost, which enables us to quantify the performance gap between any suboptimal schedule and an optimal one. [ABSTRACT FROM AUTHOR]
ISSN:00051098
DOI:10.1016/j.automatica.2016.09.015