Stochastic Sensor Scheduling for Networked Control Systems
Abstract
Optimal sensor scheduling with applications to networked estimation and control systems is considered. We model sensor measurement and transmission instances using jumps between states of a continuoustime Markov chain. We introduce a cost function for this Markov chain as the summation of terms depending on the average sampling frequencies of the subsystems and the effort needed for changing the parameters of the underlying Markov chain. By minimizing this cost function through extending Brockett's recent approach to optimal control of Markov chains, we extract an optimal scheduling policy to fairly allocate the network resources among the control loops. We study the statistical properties of this scheduling policy in order to compute upper bounds for the closedloop performance of the networked system, where several decoupled scalar subsystems are connected to their corresponding estimator or controller through a shared communication medium. We generalize the estimation results to observable subsystems of arbitrary order. Finally, we illustrate the developed results numerically on a networked system composed of several decoupled water tanks.
 Publication:

arXiv eprints
 Pub Date:
 September 2012
 DOI:
 10.48550/arXiv.1209.5180
 arXiv:
 arXiv:1209.5180
 Bibcode:
 2012arXiv1209.5180F
 Keywords:

 Mathematics  Optimization and Control;
 Computer Science  Systems and Control;
 Mathematics  Probability
 EPrint:
 Corrected Typos