Computing all minimal transition-based sensor activation policies for centralized supervisory control

Abstract

A controller activates sensors to acquire information in order to correctly implement a supervisory control law. Sensor activation is costly because systems often operate in restrictive environments with limited resources. In order to reduce unnecessary sensor activation, along with controlling the system, the supervisory controller continuously adjusts its selecting information in response to its observation of changing circumstances. Building upon existing methods for computing a transition-based minimal sensor activation policy, this paper presents algorithms for computing all minimal transition-based sensor activation policies. This result is also extended for minimizing the numerical sensor activation cost for systems modeled by stochastic automata.