Wireless sensor actuator networks are becoming a solution for control applications. Reliable data transmission and real time constraints are the most significant challenges. Control applications will have some Quality of Service (QoS) requirements from the sensor network, such as minimum delay and guaranteed delivery of packets. We investigate variable sampling method to mitigate the effects of time delays in wireless networked control systems using an observer based control system model. Our focus for variable sampling methodology is to determine the appropriate neural network topology for delay prediction and also investigate the impact of additional inputs to the neural network such as network packet loss rate and throughput. The major contribution of this work is the use of typical obtainable delay series for training the neural network. Most studies have used random generated numbers, which are not a correct representation of delays actually experienced in a wireless network. Our results here shows that adequate prediction of the time delay series using the observer based variable sampling is able to compensate for delays in the communication loop and influences the performance of the control system model.
References
[1]
Nkwogu, D.N.; Allen, A.R. Adaptive Learning and Reconfiguration in Wireless Sensor networks for Control Applications. In Proceedings of the 12th Annual Postgraduate Symposium on the Convergence of Telecommunication, Networking and Broadcasting, Liverpool, UK, 27–29 June 2011.
[2]
IEEE Standard for Information Technology—Telecommunications and Information Exchange between Systems—Local and Metropolitan Area Networks—Specific Requirement Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), IEEE Std 802.15.4a-2007.
[3]
Gmez, R.C.L.; Velasco, P.M.; Fuertes, J.M. Wireless Network Delay Estimation for Time Sensitive Applications Research Report; ESAII RR-06-12; Technical University of Catalonia: Barcelona, Spain, 2006.
[4]
Nilsson, J. Real-Time Control Systems with DelaysPh.D Thesis, Lund Institute of Technology, Lund, Sweden, 1998.
Nise, N.S. Control Systems Engineering; Benjamin/Cummings: San Fransisco, CA, USA, 1992.
[7]
Yi, J.; Wang, Q.; Zhao, D.; Wen, J. BP neural network prediction based variable sampling approach for networked control systems. Appl. Math. Comput. 2007, 185, 976–988.
[8]
Kawka, P.; Alleyne, A. Stability and Feedback Control of Wireless Networked Systems. In Proceedings of the 2005 American Control Conference, Portland, OR, USA, 8–10 June 2005; 4, pp. 2953–2959.
[9]
McKernan, A.; Arino, C.; Irwin, G.; Scanlon, W. A Multiple Observer Approach to Stability in Wireless Network Control Systems. In Proceeding of the UKACC International Control Conference, Manchester, UK; 2008.
[10]
Branicky, M.; Phillips, S.; Wei, Z. Stability of Networked Control Systems: Explicit Analysis of Delay. In Proceedings of the 2000 American Control Conference, Illinois, IL, USA, June 2000; 4, pp. 2352–2357.
[11]
Osterlind, N.; Dunkels, A. Approaching the Maximum 802.15.4 Multi-Hop Throughput. In Proceedings of ACM HotEmNets’08, Charlottesville, Virginia, USA, 2–3 June 2008.
[12]
Latré, N.; De Mil, P.; Moerman, I.; Van Dierdonck, N.; Dhoedt, B.; Demeester, P. Maximum throughput and minimum delay in IEEE 802.15.4. Lect. Note. Comp. Sci. 2005, 3794, 866–876, doi:10.1007/11599463_84.
[13]
Haykin, S. Neural Networks: A Comprehensive Foundation; Macmillian College Publishing: New York, NY, USA, 1994.
[14]
MATLAB？ & Simulink？ Student Version R2011 by The MathWorks, Inc.Matlab reference ？ Copyright 1984–2011
Guo, Z.; Malakooti, B. Delay Prediction for Intelligent Routing in Wireless Networks Using Neural Networks. In Proceedings of IEEE International Conference on Networking, Sensing and Control (ICNSC06), Ft. Lauderdale, FL, USA, 23–25 April 2006.
[17]
Sadeghzadeh, N.; Afshar, A.; Menhaj, M. An MLP Neural Network for Time Delay Prediction in Networked Control Systems. In Proceedings of Chinese Control and Decision Conference, Yantai, China, 2–4 July 2008.
[18]
Zhang, L.; Shi, Y.; Chen, T.; Huang, B. A new method for stabilization of networked control systems with random delays. IEEE Trans. Automatic Control 2009, 50, 1177–1181.
[19]
Nilsson, J.; Bernhardson, B.; Wittenmark, B. Stochastic analysis and control of real-time systems with random time delays. Automatica 1998, 34, 57–64, doi:10.1016/S0005-1098(97)00170-2.
[20]
OpnetModeler. Available online: https://enterprise1.opnet.com/opnet_directory/login/validate_static_pages?REDIRECT_URL=https://www.opnet.com/support/110-opnetreference (accessed on 28 March2012).
[21]
Friedland, B.; McKendrick, J.A. Control System Design an Introduction to State-Space Methods; McGraw-Hill: New York, NY, USA, 1987.
[22]
Alpaydin, E. Introduction to Machine Learning, 2nd ed.; The MIT Press Cambridge: Cambridge, MA, USA, 2010.
