Cover
Vol. 6 No. 2 (2010)

Published: November 30, 2010

Pages: 97-106

Original Article

Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems

A. Aldair 1 W. J. Wang
1School of Engineering and Design, University of Sussex, Falmer, East Sussex, BN1 9QT, UK
History
  • Received: September 30, 2010
  • Available Online: November 30, 2010
DOI

https://doi.org/10.37917/ijeee.6.2.3

Abstract

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PI λ D μ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

Keywords

References

  1. K. Sung, Y. Han, K. Lim and S. Choi. Discrete-time Fuzzy Sliding Mode Control for a Vehicle Suspension System Featuring an Electrorheological fluid damper. Smart Materials and Structures 16: pp. 798-808, 2007.
  2. Y. Kuo and T. Li. GA Based Fuzzy PI/PD Controller for Automotive Active Suspension System. IEEE Transactions on Industrial Electronics, Vol. 46, No. 6: pp.1051-1056, 1999.
  3. J. Feng and F. Yu. GA-Based PID and Fuzzy Logic Controller for Active Vehicle Suspension System. International Journal of Automotive Technology, Vol. 4, No. 4: pp. 181-191, 2003.
  4. M. Smith and G. Walker. Performance Limitations and Constraints for Active and Passive Suspensions: a Mechanical Multi-port Approach, Vehicle System Dynamics, Vol. 33, No. 3: pp.137-168, 2000.
  5. M. Biglarbegian, W. Melek and F. Golnaraghi. A Novel Neuro-fuzzy Controller to Enhance the Performance of Vehicle Semi-active Suspension Systems, Vehicle System Dynamics, Vol. 46, No.8: pp. 691-711, 2008.
  6. M. Biglarbegian, W. Melek and F. Golnaraghi. Design of a Novel Fuzzy Controller to Enhance Stability of Vehicles, North American Fuzzy Information Processing Society: pp. 410-414, 2007.
  7. L. Yue, C. Tang and H. Li. Research on Vehicle Suspension System Based on Fuzzy Logic Control, International Conference on Automation and Logistics, Qingdao, China, 2008.
  8. M. Kumar. Genetic Algorithm-Based Proportional Derivative Controller for the Development of Active Suspension System, 2007.
  9. Y. He and J. Mcphee. A design methodology for mechatronics vehicles: application of Multidisciplinary optimization, multimode dynamics and genetic algorithms, Vehicle System Dynamics, Vol. 43, No. 10: pp.697-733, 2005.
  10. P. Gaspar, I. Szaszi and J. Bokor. Design of Robust controller for Active vehicle Suspension Using the Mixed μ Synthesis, vehicle Dynamic System, Vol. 40, no. 4: pp. 193– 228, 2003.
  11. A. Chamseddine, H. Noura and T. Raharijana. Control of linear Full Vehicle Active Suspension System Using Sliding Mode Techniques,
  12. C. March and T. Shim. Integrated Control of Suspension and front Steering to enhance Vehicle Handling, Processing IMechE, Vol. 221 Part D: pp. 377-391, 2006.
  13. S. Lee, G. Kim and T. Lim. Fuzzy logic Based Fast Gain Scheduling Control for Nonlinear Suspension System, IEEE Transaction on
  14. S. Li, S. Yang and W. Guo. Investigation on Chaotic Motion in Hysteretic Non-linear Suspension System with Multi-frequency Excitations, Mechanics Research Communication 31, pp. 229-236, 2004.
  15. J. Dixon. "The Shock Absorber Handbook," Society of Automotive Engineers, Inc., USA, p. chap, 1999. [ 16] D. Joo, N. Al-Holou, J. Weaver, T. Lahdhir and F. Al-Abbas. Nonlinear Modelling of vehicle suspension System,” Proceeding of the American Control Conference, Chicago,
  16. C. Isik and M. Farrokhi. Recurrent neurofuzzy system, Annual meeting of the North American Fuzzy Information Processing Society Nafips, 1997.
  17. M. Brown and C. Harris. Neurofuzzy adaptive modeling and control, prentice hall international (UK) limited, 1994.
  18. Y. Zhang and A. Kandel. Compensatory neurofuzzy systems with fast learning algorithms, IEEE transactions on neural network, Vol. 9, No. 1: pp.80-105, 1998.
  19. H. Nguyen, N.Rasad, C.Alker and E. Walker. A First Course 2003.
  20. J. Jang. ANFIS: Adaptive Network Based Fuzzy Inference System, IEEE Transaction on System, Man and Cybernetics 23, 665-686, 1993.
  21. D. Xue, Y. Chen and D. Atherton. Linear Feedback Controller Analysis and Design with MATLABE, USA, The Society for Industrial and Applied Mathematics, 2007.
  22. Y. Ando and M. Suzuki. Control of Active Suspension Systems Using the Singular Perturbation method, Control Engineering Practice, Vol. 4, No. 33, pp. 287-293, 1996.
  23. H. Merritt. Hydraulic Control Systems, John Wiley and Sons,
  24. R. Rajamany and J. Hedrick. Adaptive Observers for Active Automotive Suspensions: Theory and Experiment, IEEE Transaction on Control Systems Technology, Vol. 3, NO. 1: pp 86-92, 1995.