Adaptive neuro-fuzzy wheel slip control

Expert Systems with Applications

Volumn 40, Issue 13, 2013, Pages 5197-5209

  Cirovic, Velimir ^a   Aleksendric, Dragan ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

Author keywords

Commercial vehicles; Intelligent braking; Neuro fuzzy control; Wheel slip

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; AUTOMOBILES; BRAKES; BRAKING; COMMERCIAL VEHICLES; COMPLEX NETWORKS; FORECASTING; FRICTION; FUZZY CONTROL; FUZZY INFERENCE; FUZZY LOGIC; LONGITUDINAL CONTROL; MANEUVERABILITY; NEURAL NETWORKS; ROADS AND STREETS; THREE TERM CONTROL SYSTEMS; TRANSPORTATION; VEHICLE WHEELS; VEHICLES; WHEELS;

AUTOMOTIVE BRAKING SYSTEMS; DYNAMIC NEURAL NETWORKS; HYBRID CONTROL APPROACH; INTERFACE TEMPERATURES; LONGITUDINAL DIRECTION; MODEL BASED CONTROLS; NEUROFUZZY CONTROL; WHEEL SLIPS;

BRAKING PERFORMANCE;

EID: 84878331996     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.03.012     Document Type: Article

References (41)

1. Aleksendrić , D. (2010). Neural network prediction of brake friction materials wear. Wear, 268, 117-125.
2. Aleksendrić , D., & Barton, D. C. (2009). Neural network prediction of disk brake performance. Tribology International, 42, 1074-1080.
3. Aleksendrić , D., Barton, D. C., & Vasić , B. (2010). Prediction of brake friction materials recovery performance using artificial neural networks. Tribology International, 43, 2092-2099.
4. Aleksendrić , D., Ćirović , V., & Sovrović , V. (2012). Dynamic control of disc brake performance. SAE International Journal of Passenger Cars-Mechanical Systems, 5(4).
5. Aleksendrić , D., Jakovljević, Z?., & C'irović , V. (2012). Intelligent control of braking process. Expert Systems with Applications, 39, 11758-11765.
6. Baker, A. K. (1986). Vehicle braking. Pentech Press, p. 347.
7. Barton, D. C., & Haigh, M. J. (1998). The second international seminar on automotive braking: Recent developments and future trends. Professional Engineering Publishing, p. 293.
8. Becerikli, Y., Konar, A. F., & Samad, T. (2003). Intelligent optimal control with dynamic neural networks. Neural Networks, 16, 251-259. (Pubitemid 36293558)
9. Bhandari, R., Patil, S., & Singh, R. K. (2012). Surface prediction and control algorithms for antilock brake system. Transportation Research Part C: Emerging Technologies, 21, 181-195.
10. Cheli, F., Concas, A., Giangiulio, E., & Sabbioni, E. (2008). A simplified ABS numerical model: Comparison with HIL and full scale experimental tests. Computers & Structures, 86, 1494-1502. (Pubitemid 351609091)
11. Chikhi, R., El Hadri, A., & Cadiou, J. C. (2005). Optimal control for anti-braking system. Proceedings of the IEEE International Symposium on Intelligent Control, 2005, 581-585. (Pubitemid 43903959)
12. Chun, K., & Sunwoo, M. (2005). Wheel slip tracking using moving sliding surface. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 219, 31-41. (Pubitemid 40303166)
13. Ćirović , V., Aleksendrić , D., & Mladenović , D. (2012). Braking torque control using recurrent neural networks. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226, 754-766.
14. Ćirović V., Aleksendrić , D., & Smiljanić , D. (2013). Longitudinal wheel slip control using dynamic neural networks. Mechatronics, 23, 135-146.
15. David, R. C., et al. (2012). An approach to fuzzy modeling of anti-lock braking systems. In 17th online world conference on soft computing in industrial applications (WSC 17), December 10-21.
16. Dreyfus, G. (2005). Neural networks -Methodology and applications. Berlin: Springer.
17. Han, S. I., & Lee, K. S. (2010). Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control. Mechatronics, 20, 384-401.
18. Kim, H. M., Park, S. H., & Han, S. I. (2009). Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks. Mechatronics, 19, 805-815.
19. Lin, S. S., Horng, S. C., & Lin, C. H. (2009). A neural network theory based expert system -ICRT. Expert Systems with Applications, 36, 5240-5247.
20. Lolas, S., & Olatunbosun, O. A. (2008). Prediction of vehicle reliability performance using artificial neural networks. Expert Systems with Applications, 34, 2360-2369. (Pubitemid 351173747)
21. Matusko, J., Petrovic, I., & Peric, N. (2008). Neural network based tire/road friction force estimation. Engineering Applications of Artificial Intelligence, 21, 442-456. (Pubitemid 351680680)
22. Merzouki, R., Ould-Bouamama, B., Djeziri, M. A., & Bouteldja, M. (2007). Modelling and estimation of tire-road longitudinal impact efforts using bond graph approach. Mechatronics, 17, 93-108. (Pubitemid 46210048)
23. Milanés, V., Llorca, D., Villagrá, J., Pérez, J., Parra, I., González, C., et al. (2012). Visionbased active safety system for automatic stopping. Expert Systems with Applications, 39, 11234-11242.
24. Miljković , Z., & Aleksendrić , D. (2009). Artificial neural networks -Solved examples with theoretical background, Faculty of Mechanical Engineering University of Belgrade (in Serbian). ISBN:978-86-7083- 685-3.
25. Naderi, P., Farhadi, A., Mirsalim, M., & Mohammadi, T. (2010). Anti-lock and antislip braking system, using fuzzy logic and sliding mode controllers. In Proceedings of IEEE vehicle power and propulsion conference. Lille, France.
26. Papelis, Y. E., Watson, G. S., & Brown, T. L. (2010). An empirical study of the effectiveness of electronic stability control system in reducing loss of vehicle control. Accident Analysis & Prevention, 42, 929-934.
27. Park, S., Hwang, J. P., Kim, E., Lee, H., & Gi Jung, H. (2010). A neural network approach to target classification for active safety system using microwave radar. Expert Systems with Applications, 37, 2340-2346.
28. Pearlmutter, B. A. (1990). Dynamic recurrent neural networks, Technical report CMU-CS-90-196 Carnegie Mellon University, School of Computer Science.
29. Peng, X., Zhe, H., Guifang, G., Gang, X., Binggang, C., & Zengliang, L. (2011). Driving and control of torque for direct-wheel-driven electric vehicle with motors in serial. Expert Systems with Applications, 38, 80-86.
30. Petersen, I. (2003). Wheel slip control in ABS brakes using gain scheduled optimal control with constraints, Doctor Thesis, Norwegian University of Science and Technology, Trondheim, Norway.
31. Poursamad, A. (2009). Adaptive feedback linearization control of antilock braking systems using neural networks. Mechatronics, 19, 767-773.
32. Precup, R. E. et al. (2012). Experimental results of model-based fuzzy control solutions for a laboratory antilock braking system. In Z. S. Hippe, J. L. Kulikowski, & T. Mroczek (Eds.). Human-computer systems interaction: Backgrounds and applications 2, Part 2 (Vol. 99, pp. 223-234). Springer-Verlag, AICS.
33. Savaresi, S. M., & Tanelli, M. (2010). Active braking control system design for vehicle (1st ed.). Springer-Verlag.
34. Savaresi, S. M., Tanelli, M., & Cantoni, C. (2007). Mixed slip-deceleration control in automotive braking systems. ASME Journal of Dynamic Systems, Measurements and Control, 129, 20-31. (Pubitemid 46715220)
35. Tanelli, M., Osorio, G., di Bernardo, M., Savaresi, S. M., & Astolfi, A. (2009). Existence, stability and robustness analysis of limit cycles in hybrid anti-lock braking systems. International Journal of Control, 82, 659-678.
36. Tanelli, M., Piroddi, L., Piuri, M., & Savaresi, S. M. (2008). Real-time identification of tire-road friction conditions. IEEE International Conference on Control Applications, 25-30.
37. Topalov, A. V., Oniz, Y., Kayacan, E., & Kaynak, O. (2011). Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing, 74, 1883-1893.
38. Wang, W. Y., Chen, M. C., & Su, S. F. (2012). Hierarchical T-S fuzzy-neural control of anti-lock braking system and active suspension in a vehicle. Automatica, 48, 1698-1706.
39. Wu, M., & Shih, M. (2003). Simulated and experimental study of hydraulic anti-lock braking system using sliding-mode PWM control. Mechatronics, 13, 331-351.
40. Zhao, Z., Yu, Z., & Sun, Z. (2006). Research on fuzzy road surface identification and logic control for anti-lock braking system. In Proceedings of IEEE international conference on vehicular electronics and safety (pp. 380-387). Shanghai, China.
41. Zheng, S., Tang, H., Han, Z., & Zhang, Y. (2006). Controller design for vehicle stability enhancement. Control Engineering Practice, 14, 1413-1421. (Pubitemid 44276234)