Neural network-based adaptive feedback linearization control of antilock braking system

International Journal of Artificial Intelligence

Volumn 10, Issue 13 S, 2013, Pages 21-40

  John, Samuel ^a   Pedro, Jimoh O ^b  

^a NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Namibia)

^b UNIVERSITY OF THE WITWATERSRAND   (South Africa)

Author keywords

Antilock braking system; Feedback linearization; Friction model; Neural network; Slip control

Indexed keywords

EID: 84873824627     PISSN: 09740635     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. Adaryani, M. R. and Afrakhte, H. 2011. NARMA-L2 controller for three-area load frequency control, Proceedings of the 19th Iranian IEEE Conference on Electrical Engineering (ICEE), Tehran, Iran, pp. 1-6.
2. Akbarimajd, A. and Kia, S. 2010. NARMA-L2 controller for 2-DoF underactuated planar manipulator, Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision, Singapore, pp. 195-200.
3. Awwad, A., Abu-Rub, H. and Toliyat, H. 2008. Nonlinear autoregressive moving average (NARMA-L2) controller for advanced ac motor control, Proceedings of the 34th IEEE Conference on Industrial Electronics, IECON 2008, Orlando, Florida, USA, pp. 1287-1292.
4. Bakker, E., Pacejka, H. and Lidner, L. 1989. A new tire model with an application in vehicle dynamics studies, SAE Technical Series (890087): 83-95.
5. Behera, L. and Kar, I. 2009. Intelligent Systems and Control Principles and Applications, Oxford University Press, India.
6. Bruckner, S. and Rudolph, S. 2000. Neural networks applied to smart structure control, Proceedings of SPIE, San Jose, CA, USA, Vol. 4055, p. 288.
7. Chikhi, F., El Hadri, A. and Cadiou, J. 2005. ABS control design based on wheel-slip peak localization, Proceedings of the Fifth International Workshop on Robot Motion and Control RoMoCo2005, Dymaczewo, Poland, pp. 73-77.
8. Corno, M., Savaresi, S. M. and Balas, G. J. 2009. On linear-parameter-varying (LPV) slipcontroller design for two-wheeled vehicles, International Journal of Robust and Nonlinear Control 19(12): 1313-1336.
9. Hagan, M. T., Demuth, H. B. and De-Jesus, O. 2002. An introduction to the use of neural networks in control systems, International Journal of Robust and Nonlinear Control 12: 959-985.
10. Hornik, K. M., Stinchcombe, M. and White, H. 1989. Multilayer feedforward networks are universal approximators, Journal of Neural Networks 2: 359-366.
11. Hsu, C.-F., Lee, T.-T., Hsu, C.-Y. and Lin, C.-M. 2008. Neurocontroller design and stability analysis for antilock braking systems, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3388-3393.
12. Jiang, F. and Gao, Z. 2001. An application of nonlinear PID control to a class of truck ABS problems, Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida, USA, Vol. 1, pp. 516-521.
13. Lidner, L. 1993. Experience with the magic formula tyre model, in H. B. Pacejka (ed.), Proceedings of 1st International Colloquium on Tyre Models for Vehicle Dynamics Analysis (Supplement to Vehicle System Dynamics), Vol. 21, SWETS & ZEITLINGER B.V. AMSTERDAM / LISSE, pp. 30-46.
14. Lin, C.-M. and Hsu, C.-F. 2003. Neural-network hybrid control for antilock braking systems, IEEE Transactions on Neural Networks 14(2): 351-359.
15. Lin, J.-S. and Ting, W.-E. 2007. Nonlinear control design of anti-lock braking systems with assistance of active suspension, Control Theory Applications, IET 1(1): 343-348.
16. Manual, I. U. 2009. The laboratory antilock braking system controlled from PC Technical Report, Inteco Ltd., Poland.
17. Mirzaei, A., Moallem, M., Dehkordi, B. and Fahimi, B. 2006. Design of an optimal fuzzy controller for antilock braking systems, IEEE Transactions on Vehicular Technology 55(6): 1725-1730.
18. Norgaard, M. 2000. Neural network based system identification toolbox, Technical report, Department of Automation, Technical University of Denmark.
19. Norgaard, M., Ravn, O., Poulsen, N. and Hansen, L. 2003. Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook, Springer-Verlag, London.
20. O'Dwyer, A. 2009. Handbook of PI and PID Controller Tuning Rules, 3rd edn, Imperial College Press, London.
21. Oosten, J. V. and Bakker, E. 1993. Determination of magic tyre model parameters, in H. B. Pacejka (ed.), Proceedings of 1st International Colloquium on Tyre Models for Vehicle Dynamics Analysis (Supplement to Vehicle System Dynamics Vol. 21), Vol. 21, SWETS & ZEITLINGER B.V. AMSTERDAM / LISSE, pp. 19-29.
22. Pacejka, H. and Bakker, E. 1993. The magic formula tyre model, in H. B. Pacejka (ed.), Proceedings of 1st International Colloquium on Tyre Models for Vehicle Dynamics Analysis (Supplement to Vehicle System Dynamics Vol. 21), Vol. 21, SWETS & ZEITLINGER B.V. AMSTERDAM / LISSE, pp. 1-18.
23. Park, K.-S. and Lim, J.-T. 2008. Wheel slip control for ABS with time delay input using feedback linearization and adaptive sliding mode control, Proceedings of the International Conference on Control, Automation and Systems (ICCAS), Seoul, Korea, pp. 290-295.
24. Pedro, J., Nyandoro, O. and John, S. 2009. Neural network based feedback linearisation slip control of an anti-lock braking system, Proceedings of the 7th Asian Control Conference (ASCC), pp. 1251-1257.
25. Poursamad, A. 2009. Adaptive feedback linearization control of antilock braking systems using neural networks, Mechatronics 19(5): 767-773.
26. Precup, R. E., Spataru, S., Petriu, E. M., Preitl, S., Radac, M.-B. and Dragos, C.-A. 2010. Stable and optimal fuzzy control of a laboratory antilock braking system, Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Montreal, Canada, pp. 593-598.
27. Shin, H.-S., Choi, H.-L. and Lim, J.-T. 2006. Feedback linearisation of uncertain nonlinear systems with time delay, IEE Proceedings on Control Theory and Applications 153(6): 732-736.
28. Topalov, A. V., Oniz, Y., Kayacan, E. and Kaynak, O. 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm, Neurocomputing 74(11): 1883-1893.
29. Vassileiou, A., Maris, F., Kitikidou, K. and Angelidis, P. 2012. Artifical neural networks for improved predictions in flow estimation, International Journal of Artificial Intellgence 9(A12): 186-201.
30. Wang, H., Tanaka, K. and Griffin, M. 1995. Parallel distributed compensation of nonlinear systems by takagi-sugeno fuzzy model, Proceedings of the 4th International Joint Conference on Fuzzy Systems, Yokohama, Japan, Vol. 2, pp. 531-538 vol.2.
31. Xu, C., Cheng, K., Sha, L., Ting, W. and Ding, K. 2009. Simulation of the integrated controller of the anti-lock braking system, Proceedings of the 3rd International Conference on Power Electronics Systems and Applications, Hong Kong, pp. 1-3.
32. Yeşildirek, A. and Lewis, F. L. 2001. Adaptive feedback linearization using efficient neural networks, J. Intell. Robotics Syst. 31: 253-281.
33. Yoo, D. 2006. Model based wheel slip control via constrained optimal algorithm, Master's thesis, RMIT University, Melbourne, Australia.
34. Yu, F., Feng, J. and Li, J. 2002. A fuzzy logic controller design for vehicle ABS with a on-line optimized target wheel slip ratio, International Journal of Automotive Technology 3(4): 165-170.
35. Zanten, A., Erhardt, R. and Lutz, A. 1990. Measurement and simulation of transients in longitudinal and lateral tire forces, SAE Technical Series 99(900210): 300-318.