Document Type : Review Article

Authors

Islamic Azad University, Gonabad, Iran

Abstract

Anti-lock braking system is designed to optimize braking procedure while maintaining automobile steerability through controlling wheels slip. However, due to nonlinearity and uncertainty of ABS structure, designing the controller for wheel slip encounters so many problems which necessitate a robust control system. In this paper a hybrid controller is proposed for ABS to address this issue. The designed controller is a combination of sliding mode control and fuzzy control. In fact, the fuzzy system determines switching factor of sliding mode controller proportional to automobile speed employing fuzzy rules. In this way, it would be able to avoid braking command fluctuations in lower speeds. Simulations are performed in ¼ model of automobile in MATLAB environment. The simulation results revealed the capability of proposed system to maintain slip ratio in optimal value as well as avoiding braking fluctuations in low speeds.

Keywords

[1] Wei-Yen Wang I-Hsum Li Ming-Chang Chen Shun-Feng
Su Shi-Boun Hsu," Dynamic Slip-Ratio Estimation and Control of Antilock Braking Systems Using an Observer-Based Direct Adaptive Fuzzy–Neural Controller",Industrial Electronics, IEEE ,pp.1746-1756 ,May 2009.
[2] A. V.Topalov, Y.Oniz, E. Kayacan and O. Kaynak, "Neuro-fuzzy control of antilock braking system using sliding mode Incremental learning algorithm," Neurocomputing ,vol.74 , pp.1883-189 , 2011.
[3] M.R. Akbarzade, et al, " Adaptive discrete time fuzzy sliding mode control for anti-lock braking systems," IEE Proceedings Control Theory Applications , pp. 554-559 , 2002
[4] Yen Wang, M.Chang Chen and S.Feng Su , " Hierarchical T–S fuzzy-neural control of anti- lock braking system and active suspension in a vehicle," Automatica , Vol. 48 , pp. 1698–1706 , 2012.
[5] N.Raesian, N.Khajehpour, and M.Yaghoobi , " A New Approach in Anti-lock Braking System (ABS) Based on Adaptive Neuro-Fuzzy Self-tuning PID Controller" 2nd International Conference on Control, Instrumentation and Automation (ICCIA), 2011.
[6] J. Song, H. Kim and K. Boo, "A study on an Anti-Lock Braking System Controller and Rear Wheel Controller to Enhance Vehicle Lateral Stability," Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering , Vol. 221, No. 7, pp. 777- 787, 2007.
[7] C. M. Lin and C. F. Hsu, "Self-Learning Fuzzy Sliding-Mode Control for Antilock Braking Systems," IEEE Transactions on Control System Technology , Vol . 11, No 2, 2003.
[8] C. M . Lin and C . F. Hsu, " Neural-Network Hybrid Control for Antilock Braking Systems ," IEEE Transactions on Neural Networks , Vol. 14, No. 2, 2003.
[9] Burckhardt, M., 1993. Fahrwerktechnik: radschlupf-regelsysteme
[10] V. Cirovic , D.Aleksendric, " Adaptive neuro-fuzzy wheel slip control," Expert Systems with Applications,vol. 40, pp. 5197–5209 , 2013.
[11] C. Liang, J. Su, “A new approach to the design of a fuzzy sliding mode controller”, Fuzzy Sets and Systems, Vol. 139, pp. 111-124, 2003.
[12] J. Slotine, W. Li, “Applied nonlinear control”, Englewood Cliffs, Prentice-Hall Inc. New Jersey, 1991.