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Intelligent Estimation of System Parameters for Active Vehicle Suspension Control
Technical Paper
1999-01-0729
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Active control of vehicle suspension systems typically relies on linear, time-invariant, lumped-parameter dynamic models. While these models are convenient, nominally accurate, and tractable due to the abundance of linear control techniques, they neglect potentially significant nonlinearities and time-varying dynamics present in real suspension systems. One approach to improving the effectiveness of such linear control applications is to introduce time and spatially-dependent coefficients, making the model adaptable to parameter variations and unmodeled dynamics. In this paper, the authors demonstrate an intelligent parameter estimation approach, using structured artificial neural networks, to continually adapt the lumped parameters of a linear, quarter-car suspension model. Results are presented for simulated and experimental quarter-vehicle suspension system data, and clearly demonstrate the viability of this approach.
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Citation
Buckner, G. and Schuetze, K., "Intelligent Estimation of System Parameters for Active Vehicle Suspension Control," SAE Technical Paper 1999-01-0729, 1999, https://doi.org/10.4271/1999-01-0729.Also In
References
- Hrovat D. Survey of advanced suspension developments and related optimal control applications Automatica 33 10 1997
- Schuetze K.T. Beno J.H. Weldon W.F. Sreenivasan S.V. A comparison of controller designs for an active, electromagnetic, offroad vehicle suspension system traveling at high speed presented at the SAE International Congress and Exposition Detroit, MI February 23-26 1998
- Ljung L. Glad T. Modeling of Dynamic Systems Prentice Hall 1994
- Sunwoo M. Cheok K.C. Investigation of adaptive control approaches for vehicle active suspension systems Proceedings of 1991 American Control Conference 2 1542 1547 1991
- Kim E.S. Nonlinear indirect adaptive control of a quarter car active suspension Proceedings of the 1996 IEEE International Conference on Control Applications 61 66 1996
- Ljung L. Issues in system identification IEEE Control Systems 25 29 January 1991
- Haykin S. Neural Networks, A Comprehensive Foundation Prentice Hall 1994
- Bruzzone L. Roli F. Serpico S.B. Structured neural networks for signal classification Signal Pocessing 64 3 271 290 1998
- Bastow D. Howard G.P. Car Suspension and Handling 3 Society of Automotive Engineers, Inc. 1993
- Beno J.H. Bresie D.A. Ingram S.K. Weeks D.A. Weldon W.F. Electromechanical suspension system Final Report to U.S. Army Tank and Automotive Command Warren, MI June 1995
- Weeks D.A. Beno J.H. Bresie D.A. Guenin A.M. Control system for single wheel station tracked vehicle active electromagnetic suspension presented at the SAE International Congress and Exposition Detroit, MI February 24-27, 1997
- Weeks D.A. Beno J.H. Bresie D.A. Guenin A.M. Laboratory testing of active electromagnetic near constant force suspension (NCFS) concept on subscale four corner, full vehicle test-rig presented at the SAE International Congress and Exposition Detroit, MI February 24-27, 1997
- Åström K. J. Wittenmark B. Adaptive Control 2 Addison Wesley 1995
- Fernandez B. Parlos A.G. Tsai W.K. Non-linear dynamic system identification using artificial neural networks Proceedings of the 1990 International Joint Conference on Neural Networks 2 133 141 June 1990
- Murty V.V. Sreedhar R. Fernandez B. Masada G.Y. Hill A.S. Boiler system identification using sparse neural networks, DSC advances in robust and nonlinear control systems Proceedings of the 1993 ASME Winter Annual Meeting November 1993