Optimal Dispatching of Electrical Sensors for Automatic Control

Abstract:

In this paper, we consider an electrical sensor dispatching problem for automatic control systems(ACS). We propose an algorithm which selects one (or a group of) electrical sensor at each time from a set of electrical sensors. Then, the automatic control prediction algorithm computes the estimates of the continuous state and the discrete state of the ACS based on the observation from the selected electrical sensors. As the electrical sensor dispatching algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. At the same time, the continuous state prediction performance of the proposed algorithm is better than that of automatic control prediction algorithms using only predetermined electrical sensors. Finally, our algorithm is validated though an illustrative target tracking example.

• Periodical: Advanced Materials Research (Volume 676)
• Pages: 246-250
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.676.246
• Online since: March 2013
• Authors: Jian Liang Liu, Jian Yang, Wei Yi Liu
• Keywords: Automatic Control Systems, Decision Risk, Electrical Sensor, Optimal Dispatching

References

[1] K. Herring. and J. Melsa. Optimum measurements for estimation. IEEE Transactions on Automatic Control, 19(3):264 – 266, 1974.

DOI: 10.1109/tac.1974.1100568

[2] J. S. Baras and A. Bensoussan. Optimal sensor scheduling in nonlinear filtering of diffusion processes. SIAM J. Control Optim., 27(4):786–813, 1989.

DOI: 10.1137/0327042

[3] M. Athans. On the determination of optimal costly measurement strategies. Automatica, 18:397–412, 1972.

DOI: 10.1016/0005-1098(72)90099-4

[4] J. Shin F. Zhao and J. Reich. Information-driven dynamic sensor collaboration for tracking applications. IEEE Signal Processing Magazine, 19(2):61–72, 2002.

DOI: 10.1109/79.985685

[5] B. Sinopli L. Shi, M. Epstein and R. M. Murray. Effective sensor scheduling schemes in a sensor network by employing feedback in the communication loop. In Proceedings of the 2007 IEEE Multiconference on Systems and Control, Singapore, October 2007.

DOI: 10.1109/cca.2007.4389365

[6] Y. He and E.K.P. Chong. Sensor scheduling for target tracking: A monte carlo sampling approach. Digital Signal Processing, 16(5):533– 545, 2006.

DOI: 10.1016/j.dsp.2005.02.005

[7] Y. Oshman. Optimal sensor selection strategy for discrete-time stateestimator. IEEE Transactions on Aerospace and Electronic Systems.

[8] Abate A, Hu J, Vitus M P, Zhang W and Tomlin C. On efficient sensor scheduling for linear dynamical systems[C]//In Proc. American Control Conference, Baltimore, MD, USA, June 2010.

DOI: 10.1109/acc.2010.5530899

[9] Bar-Shalom Y, Li X R, and Kirubarajan T. Estimation with Applications to Tracking and Navigation[M]. John Wiley & Sons, 2001.

[10] Hart P, Duda R and Stork D. Pattern Classification[M].New York: Wiley Interscience , 2000.

[11] Rajamanoharan S, Blackmore L and Williams B C. Active estimation for jump markov systems[C]//IEEE Transactions on Automatic Control, 2008，53(10):2223-2236.

DOI: 10.1109/tac.2008.2006100