Identification of test rig for a quarter car active suspension systems

System Identification approach can be used to estimate model and parameters of the passive system and hydraulic actuator of a test rig for quarter car active suspension system. The passive suspension system is consists of mass, spring, damper, and the hydraulic actuator. Identification of the system...

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Main Author:	Zulfatman, Zulfatman
Format:	Thesis
Language:	English
Published:	2008
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/9675/1/ZulfatmanMFKE2008.pdf
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id	my-utm-ep.9675
record_format	uketd_dc
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering TK Electrical engineering Electronics Nuclear engineering Zulfatman, Zulfatman Identification of test rig for a quarter car active suspension systems
description	System Identification approach can be used to estimate model and parameters of the passive system and hydraulic actuator of a test rig for quarter car active suspension system. The passive suspension system is consists of mass, spring, damper, and the hydraulic actuator. Identification of the systems is carried out based on the experimental works. The input to the system is an input signal to control the valve and output from the system are the output signals from the accelerometer and LVDT sensors. Input signal generation and data acquisitions process is controlled by using the LabView. In order to estimate the model and parameter, the data are processes using the System Identification Toolbox in Matlab. Since the system is modelled as a linear model, linear ARX model is utilised as a model structure. Model parameter estimation for the passive system and hydraulic actuator are performed using ARX221 and ARX631, respectively. Through the validation process, percentage of the best fit can be reached more than 90%, and the smallest LF, PFC and AIC criterions also can be reached, hence the model parameters of the systems is acceptable.
format	Thesis
qualification_level	Master's degree
author	Zulfatman, Zulfatman
author_facet	Zulfatman, Zulfatman
author_sort	Zulfatman, Zulfatman
title	Identification of test rig for a quarter car active suspension systems
title_short	Identification of test rig for a quarter car active suspension systems
title_full	Identification of test rig for a quarter car active suspension systems
title_fullStr	Identification of test rig for a quarter car active suspension systems
title_full_unstemmed	Identification of test rig for a quarter car active suspension systems
title_sort	identification of test rig for a quarter car active suspension systems
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2008
url	http://eprints.utm.my/id/eprint/9675/1/ZulfatmanMFKE2008.pdf
_version_	1747814774458023936
spelling	my-utm-ep.96752018-07-25T08:00:00Z Identification of test rig for a quarter car active suspension systems 2008-11 Zulfatman, Zulfatman TK Electrical engineering. Electronics Nuclear engineering TL Motor vehicles. Aeronautics. Astronautics System Identification approach can be used to estimate model and parameters of the passive system and hydraulic actuator of a test rig for quarter car active suspension system. The passive suspension system is consists of mass, spring, damper, and the hydraulic actuator. Identification of the systems is carried out based on the experimental works. The input to the system is an input signal to control the valve and output from the system are the output signals from the accelerometer and LVDT sensors. Input signal generation and data acquisitions process is controlled by using the LabView. In order to estimate the model and parameter, the data are processes using the System Identification Toolbox in Matlab. Since the system is modelled as a linear model, linear ARX model is utilised as a model structure. Model parameter estimation for the passive system and hydraulic actuator are performed using ARX221 and ARX631, respectively. Through the validation process, percentage of the best fit can be reached more than 90%, and the smallest LF, PFC and AIC criterions also can be reached, hence the model parameters of the systems is acceptable. 2008-11 Thesis http://eprints.utm.my/id/eprint/9675/ http://eprints.utm.my/id/eprint/9675/1/ZulfatmanMFKE2008.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering Alleyne, A. and Hedrick, K. (1995). Nonlinear Adaptive Control of Active Suspension. IEEE Transaction On Control System Technology, Vol. 1 No. 1. March, pp 94-10. Andersson, L., Jonsson, U., Johansson, K.H., and Bengtsson, J. (1999). A Manual for System Identification. Kariya, Takeaki and and Kurata, Hiroshi (2004). Generalized Least Squares. John Wiley & Sons, Ltd. Hanafi, D., and Fuaad R. (2003). System Identification of Nonlinear of A Quarter Car Passive Suspension With Backpropagation Neural Network. Conference on Artificial Intelligence Application in Industry. Kuala Lumpur Huang, S. H., and Chen, Y. H. (2006). Adaptive Sliding Control With Self-Tuning Fuzzy Compensation for Vehicle Suspension Control. Mechatronics. Vol. 16, pp. 607-622. Kaddissi, C., Kenne, J-P., and Saad M (2007). Identification and Real-Time Control of An Eectrohydraulic Servo System Based on Nonlinear Backstepping. IEEE Transaction on Mechatronics. Vol. 12, No. 1. Februari, pp. 12-21. LabView (2007). LabView, Getting Started with LabView. National Instruments. LabView (2003). Introduction to LabView, Six-Hour Course. National Instruments Corporation. Lizarde, C., Loukianov, A., and Sanchez E. (2005). Force Tracking Neural Control for An Electro_Hydraulic Actuator via Second Order Sliding Mode. Proceeding of the 2005 IEEE International Symposium on Intelligent Control. June. Limassol. Cyprus, pp. 292-297. Ljung, L. (2008). System Identification Toolbox 7, User Guide. The MathWorks. Ljung, L. (2002). System Identification: Theory for the User. Prentice-Hall, 2002. Ljung, L. (1999). System Identification: Theory for the User. Upper Saddle River Prentice-Hall PTR. New Jersey. Ljung, L. (1987). System Identificationâ€”Theory for the User. Prentice-Hall, Englewood Cliffs. New Jersey. Merritt, H.E. (1967). Hydraulic Control System. John Wiley & Sons, Inc. New York. Rajamani, R., and Hedrick J. K. (1994). Performance of Active Automotive Suspensions with Hydraulic Actuator: Theory and Experiment. Proceeding of American Control Conference. Baltimore. Maryland. June, pp. 1214-1219. Renn, J. C., and Wu T. S. (2007). Modeling and Control of A New 1/4T Servo- Hydraulic Vehicle Active Suspension System. Journal of Marine Science and Technology. Vol. 15, pp. 265-272. Soderstrom, T., Fan, B. Carlsson, S. Bigi, (1997). Least Squares Parameter Estimation of Continuous-Time ARX Models from Discrete Time-Data, IEEE Transaction on Automatic Control. Vol. 42. No.5. May. Sam Y. M., Osman J.H.S, Ghani M.R.A. (2004). A Class of Proportional-Integral Sliding Mode Control with Application to Active Suspension System. System and Control Letter, pp. 51:217-223. Soderstrom, T. and Stoica, P. (1989). System Identification. Prentice-Hall Int. London. Zulfatman, Sam, Y.M., Ghazali, R., Shuaib, N.M. and Hamzah H. (2008). Identification of Quarter Car Suspension Systems. Proceedings of 2008 Student Conference on Research and Development (SCOReD 2008). 26-27 Nov. Johor, pp. 233:1-4.

Identification of test rig for a quarter car active suspension systems

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