Home Articles List Article Information
AUT Journal of Modeling and Simulation
Journal Archive
Volume Volume 49 (2017)
Volume Volume 48 (2016)
Volume Volume 47 (2015)
Volume Volume 46 (2014)
Volume Volume 45 (2013)
Volume Volume 44 (2012)
Volume Volume 43 (2011)
Volume Volume 42 (2010)
Volume Volume 41 (2009)
Rahmani, B., D.Markaziii, A. (2010). Markovian Delay Prediction-Based Control of Networked Systems. AUT Journal of Modeling and Simulation, 42(1), 21-28. doi: 10.22060/miscj.2010.185
Behrooz Rahmani; Amir H. D.Markaziii. "Markovian Delay Prediction-Based Control of Networked Systems". AUT Journal of Modeling and Simulation, 42, 1, 2010, 21-28. doi: 10.22060/miscj.2010.185
Rahmani, B., D.Markaziii, A. (2010). 'Markovian Delay Prediction-Based Control of Networked Systems', AUT Journal of Modeling and Simulation, 42(1), pp. 21-28. doi: 10.22060/miscj.2010.185
Rahmani, B., D.Markaziii, A. Markovian Delay Prediction-Based Control of Networked Systems. AUT Journal of Modeling and Simulation, 2010; 42(1): 21-28. doi: 10.22060/miscj.2010.185

Markovian Delay Prediction-Based Control of Networked Systems

Article 4, Volume 42, Issue 1, Spring and Summer 2010, Page 21-28  XML PDF (597 K)
Document Type: Research Paper
DOI: 10.22060/miscj.2010.185
Authors
Behrooz Rahmani; Amir H. D.Markaziii
Abstract
A new Markov-based method for real time prediction of network transmission time delays is introduced. The method considers a Multi-Layer Perceptron (MLP) neural model for the transmission network, where the number of neurons in the input layer is minimized so that the required calculations are reduced and the method can be implemented in the real-time. For this purpose, the Markov process order is estimated offline, using pr-recorded network time delay history. Unlike most of the previously existing methods, the proposed approach is both accurate and fast enough for a real time implementation. Using such a scheme for real-time estimation of the upcoming time delays, a variable state feedback gain control scheme is also proposed and applied to the predicted discretized model of the plant. The proposed approach is shown, through well-known benchmark problems, to be both accurate and fast enough for a real time implementation.
Keywords
neural network; Delay Prediction; Entropy; Markov Order Estimation; Networked Control Systems (NCS); Variable Controller Design
References

[1]     T.C. Yang, “Networked control system: a brief survey”, IEE Proc.-Control Theory Appl., Vol. 153, 2006, pp. 403-412.

[2]     W. Zhang, M. S. Branicky, and S. M. Phillips, “Stability of Networked Control Systems”, IEEE Control Systems Magazine, 2001, pp. 84-99.

[3]     Y. Tipsuwan and M. Y. Chow, “Control methodologies in networked control systems”, Control Engineering Practice 11, 2003, pp. 1099–1111.

[4]     C. Ma, S. Chen and W. Liu, “Maximum allowable delay bound of networked control systems with multi-step delay”, Simulation Modeling Practice and Theory, 2007, pp. 513–520.

[5]     G. P. Liu, Y. Xia, D. Rees, and W. Hu, “Design and Stability Criteria of Networked Predictive Control Systems With Random Network Delay in the Feedback Channel”, IEEE Transaction on Systems, Vol. 37, No. 2, 2007, pp. 173-184.

[6]     L. Zhang, Y. Shi, T. Chen, and B. Huang, “A new method for stabilization of networked control systems with random delays”, IEEE Transaction on Automatic Control, 2005, pp. 1177–1181.

[7]     L. Zhang and F. Huajing, “A novel controller design and evaluation for networked control systems with time-variant delays”, Journal of the Franklin Institute, (2006), pp. 161–167.

[8]     L. Zhang and F. Huajing, “Fuzzy controller design for networked control system with time-variant delays”, Journal of Systems Engineering and Electronics, Vol.17, No. 1, 2006, pp.172– 176.

[9]     J. Yi, Q. Wang, D. Zhao and J. T. Wen, “BP neural network prediction-based variable-period sampling approach for networked control systems”, Applied Mathematics and Computation, 2006, pp. 976–988.

[10]  Zhang, L., Shi, Y., Chen, T., and Huang, B., “A new method for stabilization of networked control systems with random delays, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol. 50, 2005, pp. 1177–1181.

[11]  Q.P. Wang, D.L. Tan, Ning Xi, Y.C. Wang, “The Control Oriented QoS: Analysis and Prediction”, Proceedings of the 2001, IEEE International Conference on Robotics 8 Automation.

[12]  North, S. Sahin, F., "Picasso: real-time estimation of network delay over a tele-robotic link", IEEE International Conference on Systems, Man and Cybernetics, 2002.

[13]  S. Soucek and G. koler, “Impact of QOS parameter on Internet-Based EIA-709.1 Control Applications”, IEEE Conference Proceeding, USA, Vol. 4, 2002, pp. 3176-3181. 

[14]  N. N. R. Ranga Suri, D. Deodhare and P. Nagabhushan, “Parallel Levenberg -Marquardt-based Neural Network Training on Linux Clusters - A Case Study”, Proceedings of the Third Indian Conference on Computer Vision, Graphics & Image Processing, India, 2002.

[15]  S. Miller and D. Childers, Probability and Random Processes: With Applications to Signal Processing and Communications, Academic Press, 2004.

[16]  S. U. Pillai and A. Papoulis, Probability, Random Variables, and Stochastic Processes, McGraw-Hill, 2002.

[17]  H. Kong and E. Shwedyk, “A Measure for the Length of Probabilistic Dependence”, IEEE, ISlT, Ulm, Germany, 1997, pp. 469.

[18]  T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley-Interscience, 1991.

[19]  K. J. Astrom (Author), and B. Wittenmark, Computer-Controlled Systems: Theory and Design, Prentice Hall; 3 edition, Nov 20 1996.

[20]  J. Xiong and J. Lam, “Stabilization of Networked Control Systems with a Logic ZOH”, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 54, NO. 2, FEBRUARY 2009.

Statistics
Article View: 953
PDF Download: 818