Calculating All Minimal Transition-Based Sensor Activation Policies for the Purpose of Supervisory Control

Consider the problem of designing a supervisory control in which the supervisor has control not merely over the inputs (or actuators), but can also choose to turn sensors on or off. In this context, the supervisor can flexibly adjust the set of activated sensors to reduce sensor utilization without compromising the correctness of control decisions. Building upon existing methods for computing a transition-based minimal sensor activation policy, algorithms are presented in this technical note for computing all minimal transition-based sensor activation policies. This result is then extended for minimizing the numerical sensor activation cost for systems modeled by stochastic automata.

[1]  K. C. Wong,et al.  Decentralized supervisory control of discrete-event systems with communication , 1996 .

[2]  Stéphane Lafortune,et al.  On Most Permissive Observers in Dynamic Sensor Activation Problems , 2014, IEEE Transactions on Automatic Control.

[3]  Shengbing Jiang,et al.  Optimal sensor selection for discrete-event systems with partial observation , 2003, IEEE Trans. Autom. Control..

[4]  Stéphane Lafortune,et al.  An algorithm for calculating indistinguishable states and clusters in finite-state automata with partially observable transitions , 2007, Syst. Control. Lett..

[5]  Krishnendu Chatterjee,et al.  Controller Synthesis with Budget Constraints , 2008, HSCC.

[6]  Stavros Tripakis,et al.  Fault Diagnosis with Static and Dynamic Observers , 2008, Fundam. Informaticae.

[7]  J.H. van Schuppen,et al.  Decentralized failure diagnosis for discrete-event systems with costly communication between diagnosers , 2002, Sixth International Workshop on Discrete Event Systems, 2002. Proceedings..

[8]  Stéphane Lafortune,et al.  On Codiagnosability and Coobservability With Dynamic Observations , 2011, IEEE Transactions on Automatic Control.

[9]  Benoît Caillaud,et al.  Mind the gap: Expanding communication options in decentralized discrete-event control , 2007, 2007 46th IEEE Conference on Decision and Control.

[10]  Kenneth A. Loparo,et al.  Minimizing the Cardinality of an Events Supervisors of Discrete-Event Dynamical Set for Systems , 1996 .

[11]  Ling Shi,et al.  Optimal sensor hop selection: Sensor energy minimization and network lifetime maximization with guaranteed system performance , 2008, 2008 47th IEEE Conference on Decision and Control.

[12]  Walter Murray Wonham,et al.  On observability of discrete-event systems , 1988, Inf. Sci..

[13]  P. Ramadge,et al.  Supervisory control of a class of discrete event processes , 1987 .

[14]  Stéphane Lafortune,et al.  Minimization of Dynamic Sensor Activation in Discrete Event Systems for the Purpose of Control , 2010, IEEE Transactions on Automatic Control.

[15]  Robert Nowak,et al.  Distributed optimization in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[16]  Feng Lin,et al.  Online Sensor Activation for Detectability of Discrete Event Systems , 2013, IEEE Transactions on Automation Science and Engineering.

[17]  Stéphane Lafortune,et al.  Optimal sensor activation for diagnosing discrete event systems , 2010, Autom..

[18]  Demosthenis Teneketzis,et al.  Active Acquisition of Information for Diagnosis and Supervisory Control of Discrete Event Systems , 2007, Discrete event dynamic systems.

[19]  Stéphane Lafortune,et al.  Minimization of Communication of Event Occurrences in Acyclic Discrete Event Systems , 2008, IEEE Transactions on Automatic Control.

[20]  Karen Rudie,et al.  Minimal sensor activation and minimal communication in discrete-event systems , 2016, Discret. Event Dyn. Syst..

[21]  J. H. vanSchuppen Decentralized control with communication between controllers , 2003 .

[22]  Stéphane Lafortune,et al.  On the Minimization of Communication in Networked Systems with a Central Station , 2008, Discret. Event Dyn. Syst..

[23]  Weilin Wang Online minimization of sensor activation for supervisory control , 2016, Autom..

[24]  Daiheng Ni,et al.  A Sampling Theorem Approach to Traffic Sensor Optimization , 2008, IEEE Transactions on Intelligent Transportation Systems.

[25]  S. Laurie Ricker,et al.  Decentralized failure diagnosis with asynchronous communication between supervisors , 2001, 2001 European Control Conference (ECC).