Volume 15, Issue 3 (Journal of Control, V.15, N.3 Fall 2021)                   JoC 2021, 15(3): 1-12 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Fathizadeh N, Mirzaeinejad H, Hosseini Salari A. Modeling and Design of Traction Control System of vehicle using Nonlinear Predictive Control and Neural Network. JoC. 2021; 15 (3) :1-12
URL: http://joc.kntu.ac.ir/article-1-723-en.html
1- Shahid Bahonar university of Kerman
Abstract:   (3458 Views)
Traction control system (TCS) is one of the necessary systems for increasing vehicle safety. This control system is used to prevent excessive slipping of wheels especially when the vehicle suddenly starts to move. Keeping the wheels slip in a desirable range under unfavorable weather condition is a challenging issue due to unknown effects of road surface and severe nonlinear behavior of tire during the acceleration process. On the other hand, in designing a controller, the existence of some unknown uncertainties such as un-model dynamics and variation of vehicle parameters should be considered. Therefore, the presence of a nonlinear robust control law seems avoidable for TCS. In this paper, at first, using nonlinear predictive control method, a modern nonlinear optimal controller is designed for TCS. Then, unknown uncertainties of the system are adaptively estimated using a radial basis function neural network (RBFNN). Finally, some simulation results are presented for tracking the reference wheel slip in the presence of uncertainties for different maneuvers in order to assess the behavior of the proposed control system. The results show the effectiveness of the proposed control system against the nonlinear effects and uncertainties.
Full-Text [PDF 1567 kb]   (230 Downloads)    
Type of Article: Review paper | Subject: Special
Received: 2019/12/25 | Accepted: 2020/11/1 | ePublished ahead of print: 2020/11/10 | Published: 2022/02/1

1. [1] Bryson, A. E. and Ho, Y. C., 1975, "Applied Optimal Control", Hemisphere, Washington DC.
2. [2] Van Zanten, A. T., Ertarad, R., Pfaff, G., Kost, F., Hartmann, U. and Ehret, T., 1996, "Control aspects of the Bosch-VDC", AVEC'96, 573−608.
3. [3] Mirzaei, M., and Mirzaeinejad, H., 2017, "Fuzzy Scheduled Optimal Control of Integrated Vehicle Braking and Steering Systems", IEEE/ASME Transactions on Mechatronics. 22,2369-2379. [DOI:10.1109/TMECH.2017.2749002]
4. [4] Mirzaei, M., Mirzaeinejad, H., Vahidi S., Heidarien, D., Khosrowjerdi, M. J., 2012, "Nonlinear control and estimation of tire longitudinal slip for using in anti-lock braking system", Journal of Control, Vol. 5, No. 4, pp. 31-42. (In Persian)
5. [5] Mirzaeinejad, H., Mirzaei, M., and Kazemi, R. 2015, "Enhancement of vehicle braking performance on split-μ roads using optimal integrated control of steering and braking systems", Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics. 230, 401-415. [DOI:10.1177/1464419315617332]
6. [6] Shafei, A.M., Mirzaeinejad, H., 2020, "A General Formulation for Managing Trajectory Tracking in Non-holonomic Moving Manipulators with Rotary-Sliding Joints", Journal of Intelligent Robotic Systems. 99, 729-746. [DOI:10.1007/s10846-019-01143-6]
7. [7] Li, S., Liao, C., Chen, S. and Wang, L., 2009, "Traction control of hybrid electric vehicle", VPPC '09, 1535−1540. [DOI:10.1109/VPPC.2009.5289563]
8. [8] Khatun, P., Bingham, C. M., Schofield, N. and Mellor, P. H., 2003, "Application of fuzzy control algorithm for electric vehicle antilock braking/traction control systems", IEEE Trans. Vehicular Technology 52, 1356−1364. [DOI:10.1109/TVT.2003.815922]
9. [9] Lee, H. and Tomizuka, M., 2003, "Adaptive vehicle traction force control for intelligent vehicle highway systems (IVHSs)", IEEE Trans. Industrial Electronics 50, 37−47. [DOI:10.1109/TIE.2002.807677]
10. [10] Borrelli, F., Bemporad, A., Fodor, M. and Hrovat, D., 2006, "An MPC/hybrid system approach to traction control", IEEE Trans. Control Systems Technology 14, 3, 541-552. [DOI:10.1109/TCST.2005.860527]
11. [11] Jung, H., Kwak, B. and Park, Y., 2000, "Slip controller design for traction control system", Int. J. Automotive Technology 1, 48−55.
12. [12] Chun, K. and Sunwoo, M., 2004, "Wheel slip control with moving sliding surface for traction control system", Int. J. Automotive Technology 5, 123−133.
13. [13] Kuntanapreeda, S., 2015, "Super-twisting sliding-mode traction control of vehicles with tractive force observer", Control Engineering Practice, 38, 26-36. [DOI:10.1016/j.conengprac.2015.01.004]
14. [14] Precup, R. E., Radac, M. B., Roman, R. C. and Petriu, E. M., 2017, "Model-free sliding mode control of nonlinear systems: algorithms and experiments, Inf. Sci., 381, 176-192. [DOI:10.1016/j.ins.2016.11.026]
15. [15] Harifi, A., Aghagolzadeh, A., Alizadeh, G. and Sadeghi, M., 2008, "Designing a sliding mode controller for slip control of antilock brake systems", Transportation research, Part C, 16, 731-741. [DOI:10.1016/j.trc.2008.02.003]
16. [16] Mirzaeinejad, H. and Mirzaei, M., 2010, "A novel method for non-linear control of wheel slip in anti-lock braking systems", Control Engineering Practice, 18, 918-926. [DOI:10.1016/j.conengprac.2010.03.015]
17. [17] Mirzaeinejad, H., 2019, "Optimization-based nonlinear control laws with increased robustness for trajectory tracking of non-holonomic wheeled mobile robots", Transportation Research Part C: Emerging Technologies, 101,1-17. [DOI:10.1016/j.trc.2019.02.003]
18. [18] Mirzaeinejad, H., Mirzaei, M., and Rafatnia, S. 2018, "A novel technique for optimal integration of active steering and differential braking with estimation to improve vehicle directional stability", ISA Transactions. 80, 513-527. [DOI:10.1016/j.isatra.2018.05.019]
19. [19] Mirzaeinejad, H., 2018, "Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network", Applied soft computing, 70, 318-329. [DOI:10.1016/j.asoc.2018.05.043]
20. [20] Smith, D. E. and Starky, J. M., 1995, "Effects of model complexity on the performance of automated vehicle steering controllers, model development, validation and comparison", Vehicle System Dynamics, 24, 163-181. [DOI:10.1080/00423119508969086]
21. [21] Chen, W. H., Balance, D. J. and Gawthrop, P. J., 2003, "Optimal control of nonlinear systems: a predictive control approach", Automatica, 39, 633-641. [DOI:10.1016/S0005-1098(02)00272-8]
22. [22] Slotine, J. J. E. and Li, W., 1991, "Applied Nonlinear Control", Prentice Hall, Englewood Cliffs.
23. [23] Wenzel, T. A., Burnham, K. J., Williams, R. A., 2004, "Blundell Hybrid Genetic Algorithms Extended Kalman Filter Approach for Vehicle State and Parameter Estimation," University of Bath, UK.
24. [24] Simon, D., 2006, "Optimal State Estimation," John Wiley and Sons, Inc., Hoboken, New Jersey.
25. [25] Hsu, C. F., Lin, C. M. and Guan, Y. R., 2013, "Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems", Applied soft computing, 13, 1620-1626. [DOI:10.1016/j.asoc.2012.12.028]
26. [26] Fu, Y. Y., Wu, C. J. and Ko, C. N., 2011, "Radial basis function networks with hybrid learning for system identification with outliers", Applied soft computing, 11, 2278-2283. [DOI:10.1016/j.asoc.2010.12.010]
27. [27] Park, J. and Sandberg, I. W., 1991, "Universal approximation using radial-basis-function networks", Neural Computing, 3, 246-257. [DOI:10.1162/neco.1991.3.2.246]
28. [28] Chen, T. and Chen, H., 1995, "Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks", IEEE Trans. Neural Network, 904-910. [DOI:10.1109/72.392252]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2022 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb