Approximating the minimal sensor selection for supervisory control

Discrete Event Dynamic Systems: Theory and Applications

Volumn 16, Issue 1, 2006, Pages 143-170

  Rohloff, Kurt R ^a   Khuller, Samir ^b   Kortsarz, Guy ^c  

^a BBN TECHNOLOGIES   (United States)

^b University of Maryland ^*  (United States)

^c RUTGERS UNIVERSITY   (United States)

Author keywords

Approximation algorithms; Automata; Computational complexity; Sensor selection; Supervisory control

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; AUTOMATA THEORY; COMPUTATIONAL COMPLEXITY; CONTROL EQUIPMENT; INTEGER PROGRAMMING; PROBLEM SOLVING; SENSORS;

APPROXIMATION ALGORITHMS; SENSOR SELECTION; SUPERVISORY CONTROL;

CONTROL SYSTEM ANALYSIS;

EID: 32944458102     PISSN: 09246703     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10626-006-6187-3     Document Type: Conference Paper

References (22)

1. Arora S, Lund C (1997). Hardness of approximations. In: D S Hochbaum (ed) Algorithms for NP-Hard Problems. Boston, MA: PWS Publishing, pp 46-93.
2. Ausiello G, Crescenzi P, Gambosi G, Kahn V, Marchetti-Spaccamela A, Protasi M (1999) Complexity and Approximation Combinatorial Optimization Problems and Their Approxim-ability Properties, Berlin, Heidelberg, Springer-Verlag.
3. Cassandras CG, Lafortune S (1999). Introduction to Discrete Event Systems. Boston, MA: Kluwer Academic Publishers.
4. Cho H (1990) Designing observation functions in supervisory control. In Proc. Korean Automatic Control Conference, pp 523-528.
5. Garey MR, Johnson DS (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness. San Francisco, CA: W.H. Freeman.
6. Haji-Valizadeh A, Loparo KA (1996). Minimizing the cardinality of an events set for supervisors of discrete-event dynamical systems. IEEE Trans Automat Contr 41(11): 1579-1593.
7. Jiang S, Kumar R, Garcia HE (2003). Optimal sensor selection for discrete-event systems with partial observation. IEEE Trans Automat Contr 48(3):369-381.
8. Khuller S, Kortsarz G, Rohloff K (2004, September). Approximating the minimal sensor selection for supervisory control. In Proc. 7th Workshop on Discrete Event Systems (WODES), Reims, France, pp 85-90.
9. Kortsarz G (2001). On the hardness of approximating spanners, Algorithmica, special Approx-98 issue, 30(3):432-450.
10. Lin F, Wonham WM (1988). On observability of discrete-event systems. Inf Sci 44:173-198.
11. Papadimitriou CH, Steiglitz K (1982). Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Inc., Englewood Gifts, NJ.
12. Ramadge PJ, Wonham WM (1987). Supervisory control of a class of discrete-event processes. SIAM J Control Optim 25(1):206-230.
13. Ramadge PJ, Wonham WM (1989). The control of discrete-event systems. Proc. IEEE 77(1):81-98.
14. Raz R (1998). A parallel repetition theorem. SIAM J Comput 27(3):763-803.
15. Rohloff K (2004). Computations on Distributed Discrete-Event Systems. PhD thesis, The University of Michigan: Ann Arbor. Available at http://www.eecs.umich.edu/umdes.
16. Rohloff K, van Schuppen JH (2004, December). Approximating the minimal-cost sensor-selection for discrete-event systems. Technical Report MASR0404, Centrum voor Wiskunde en Informatica (CWI), Amsterdam, the Netherlands.
17. Rohloff K, van Schuppen JH (2005). Approximating minimal communicated event sets for decentralized supervisory control. In Proc. of the 16th IFAC World Congress.
18. Rudie K, Willems JC (1995) The computational complexity of decentralized discrete-event control problems. IEEE Trans Automat Contr 40(7):1313-1318.
19. Tsitsiklis J (1989). On the control of discrete-event dynamical systems. MCCS Math Control Signals and Syst 2:95-107.
20. Vazirani V (2001). Approximation Algorithms. Springer-Verlag, Berlin, Heidelberg.
21. Yoo T-S, Lafortune S (2002). NP-completeness of sensor selection problems arising in partially-observed discrete-event systems. IEEE Trans Automat Contr 47(9):1495-1499.
22. Young S, Garg VK (1994, October). Optimal sensor and actuator choices for discrete event systems. In Proc. 31st Allerton Conference on Communication, Control, and Computing, pp 36.