Group Method of Data Handling for Modeling Magnetorheological Dampers

Khaled Assaleh, Tamer Shanableh, Yasmin Abu Kheil

Department of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab Emirates.
Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates.
Mechatronics Graduate Program, American University of Sharjah, Sharjah, United Arab Emirates.

DOI: 10.4236/ica.2013.41010                

Abstract

This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons that offers an effective solution to modeling non-linear systems. As such, we propose the use of GMDH to approximate the forward and inverse dynamic behaviors of MR dampers. We also introduce two enhanced GMDH-based solutions. Firstly, a two-tier architecture is proposed whereby an enhanced GMD model is generated by the aid of a feedback scheme. Secondly, stepwise regression is used as a feature selection method prior to GMDH modeling. The proposed enhancements to GMDH are found to offer improved prediction results in terms of reducing the root-mean-squared error by around 40%.

Keywords

System Identification; Magneto-Rheological Dampers; Group Method of Data Handling; Polynomial Classifier

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	G. P. Liu, “Nonlinear Identification and Control: A Neural Network Approach,” Springer, New York, 2001. doi:10.1007/978-1-4471-0345-5
[2]	P. R. Water, E. J. H. Kerckhoffs and D. Van Welden, “GMDH-Based Dependency Modeling in the Identification of Dynamic Systems,” European Simulation MultiConference (ESM), 2000, pp. 211-218.
[3]	X. Huang, J. Xu and S. Wang “Nonlinear System Identification with Continuous Piecewise Linear Neural Network,” Neurocomputing, Vol. 77, No. 1, 2011, pp. 167-177.
[4]	M. Gonzalez-Olvera and Y. Tang, “A New Recurrent Neurofuzzy Network for Identification of Dynamic Systems,” Fuzzy Sets and Systems, Vol. 158, No. 10, 2007, pp. 1023-1035. doi:10.1016/j.fss.2006.10.002
[5]	G. Wang, “Application of Hybrid Genetic Algorithm to System Identification,” Structural Control and Health Monitoring, Vol. 16, No. 2, 2009, pp. 125-153. doi:10.1002/stc.306
[6]	K. Assaleh, “Extraction of Fetal Electrocardiogram Using Adaptive Neuro-Fuzzy Inference Systems,” IEEE Transactions on Biomedical Engineering, Vol. 54, No. 1, 2007, pp. 59-68. doi:10.1109/TBME.2006.883728
[7]	K. Assaleh and H. Al-Nashash, “A Novel Technique for the Extraction of Fetal ECG Using Polynomial Networks,” IEEE Transactions on Biomedical Engineering, Vol. 52, No. 6, 2005, pp. 1148-1152. doi:10.1109/TBME.2005.844046
[8]	W. Campbell, K. Assaleh and C. Broun, “Speaker Recognition with Polynomial Classifiers,” IEEE Transactions on Speech and Audio Processing, Vol. 10, No. 4, 2002, pp. 205-212. doi:10.1109/TSA.2002.1011533
[9]	A. G. Ivakhnenko, “The Group Method of Data Handling-A Rival of the Method of Stochastic Approximation,” Soviet Automatic Control, Vol. 1, No. 3, 1968, pp. 43-55.
[10]	X.-M. Zhao, Z.-H. Song and P. Li, “A Novel NF-GMDH-IFL and Its Application to Identification and Prediction of Non-Linear Systems,” IEEE Proceedings of the Region 10 Conference on Computers, Communications, Control and Power Engineering, Beijing, 28-31 October 2002, pp. 1286-1289.
[11]	C.-C. Chang and P. Roschke, “Neural Network Modeling of a Magnetorheological Damper,” Journal of Intelligent Material Systems and Structures, Vol. 9, No. 9, 1998, pp. 755-764. doi:10.1177/1045389X9800900908
[12]	M. Askari and A. Davaie-Markazi, “Application of A New Compact Optimized T-S Fuzzy Model to Nonlinear System Identification,” Proceeding of the 5th International Symposium on Mechatronics and Its Applications (ISMA08), Amman, 27-29 May 2008, pp. 1-6.
[13]	M. Duan and J. Shi, “Design for Automobiles MagnetoRheological Damper and Research on Polynomial Model,” International Conference on Electric Information and Control Engineering (ICEICE), Wuhan, 15-17 April 2011, pp. 2085-2088.
[14]	J. Korbicz and M. Mrugalski, “Confidence Estimation of GMDH Neural Networks and Its Application in Fault Detection Systems,” International Journal of Systems Science, Vol. 39, No. 8, 2008, pp. 783-800. doi:10.1080/00207720701847745
[15]	S. Spencer Jr., M. S. Dyke and J. Carlson, “Phenomenological Model of a Magnetorheological Damper,” ASCE Journal of Engineering Mechanics, Vol. 123, No. 3, 1997, pp. 230-238. doi:10.1061/(ASCE)0733-9399(1997)123:3(230)
[16]	K. Assaleh, T. Shanableh and Y. A. Kheil, “System Identification of Magneto-Rheological Damper Using Group Method of Data Handling (GMDH),” 6th International Symposium on Mechatronics and Its Applications, ISMA '09, Sharjah, 23-26 March 2009, pp. 1-6.
[17]	D. Montgomery and G. Runger, “Applied Statistics and Probability for Engineers,” Wiley, Hoboken, 1994.
[18]	T. Shanableh and K. Assaleh, “Feature Modeling Using Polynomial Classifiers and Stepwise Regression,” Neurocomputing, Vol. 73, No. 10-12, 2010, pp. 1752-1759. doi:10.1016/j.neucom.2009.11.045
[19]	V. Atray and P. Roschke, “Design, Fabrication, Testing, and Fuzzy Modeling of a Large Magnetorheological Damper for Vibration Control in a Railcar,” Proceedings of the Joint ASME/IEEE Conference on Railroad Technology, Chicago, 22-24 April 2003, pp. 223-229.