Abstract
This study conducts a comparative analysis of sliding mode control (SMC) and fractional-order sliding mode control (FOSMC) for application in antilock braking systems (ABS). Based on foundational principles of theoretical mechanics, the ABS dynamics are modeled as a single-input system to analyze wheel-slip regulation under diverse and variable road conditions. Both the conventional SMC and the proposed FOSMC are designed using a Lyapunov-based approach to ensure robust stability, with the latter incorporating fractional-order derivatives to refine the sliding surface and dynamic response. The conventional SMC method, while demonstrating strong robustness and disturbance rejection capabilities, is found to induce persistent chattering during transient phases, which can compromise system reliability and actuator longevity. By contrast, the FOSMC controller enhances transient behavior by attenuating chattering and yielding smoother, more consistent wheel-slip tracking. The inclusion of fractional-order terms contributes to faster convergence and improved adaptation to abrupt changes in road friction, though it introduces increased computational complexity. Numerical simulations validate the performance of both controllers across multiple driving scenarios, including dry, wet, and icy road conditions. Results confirm that FOSMC significantly reduces chattering, accelerates system convergence, and maintains stable braking performance with greater consistency compared to conventional SMC, establishing its potential for implementation in advanced ABS designs.
References
Abdul Zahra, A. K. and T. Y. Abdalla, 2020. Design of fuzzy super twisting sliding mode control scheme for unknown full vehicle active suspension systems. Asian Journal of Control.
Bi, J. et al., 2024. Adaptive second-order sliding mode wheel slip control for electric vehicles with in-wheel motors. World Electric Vehicle Journal 15: 538.
Chen, H. C., A. Wisnujati, A. M. Widodo, Y. L. Song, and C. W. Lung, 2022. Antilock braking system (ABS) based control type regulator implemented by neural network in various road conditions. In Springer Nature Switzerland.
Chereji, E., M.-B. Radac, et al., 2021. Sliding mode control algorithms for anti-lock braking systems with performance comparisons. Algorithms 14.
Garcia Torres, C. J. et al., 2022. Lyapunov stability of modified HOSM controllers using a PID-sliding surface applied to an ABS laboratory setup. Applied Sciences 12: 3796.
Geleta, A. F., M. Gospal, and M. T. Lemi, 2023. Vehicle anti-lock brake system: Dynamic modeling and simulation based on MATLAB/Simulink and CarSim. Research on Engineering Structures and Materials.
Gengxin, Q. et al., 2022. Research on robust control of automobile anti-lock braking system based on road recognition. Journal of Engineering and Applied Sciences 16: 343–352.
Gowda, D. and A. Ramachandra, 2017. Slip ratio control of anti-lock braking system with bang-bang controller. International Journal of Computer Techniques 4: 97–104.
Jazar, R. N., 2008. Vehicle Dynamics. Springer.
Khadr, A. et al., 2024. Anti-lock braking system control design using a non-linear multibody dynamic model for a two-wheeled vehicle. Engineering Research Express 6: 015527.
Latreche, S. and S. Benaggoune, 2015. Robust fuzzy sliding control for uncertain systems: Application to ABS system. In International Conference on Applied Automation and Industrial Diagnostics (ICAAID).
Max, N. M., N. Y. J. Maurice, E. Samuel, M. C. Jordan, A. Biboum, et al., 2021. DTC with fuzzy logic for multi-machine systems: Traction applications. International Journal of Power Electronics and Drive Systems 12: 2044–2058.
Namaghi, S. S. and M. Moavenian, 2019. An adaptive modified fuzzy-sliding mode longitudinal control design and simulation for vehicles equipped with ABS system. Journal of Applied Engineering.
Nemah, M. N., 2018. Modelling and development of linear and nonlinear intelligent controllers for anti-lock braking systems (ABS). Journal of University of Babylon, Engineering Sciences 26.
Pretagostini, F., L. Ferranti, G. Berardo, V. Ivanov, and B. Shyrokau, 2020. Survey on wheel slip control design strategies, evaluation and application to antilock braking systems. IEEE Access.
Sabanovic, E., P. Kojis, V. Ivanov, M. Dhaens, and V. Skrickij, 2024. Development and evaluation of artificial neural networks for real-world data-driver virtual sensors in vehicle suspension. IEEE Access.
Schinkel, M. and K. Hunt, 2002. Anti-lock braking control using a sliding mode like approach. In Proceedings of the American Control Conference.
Tang, Y., X. Zhang, D. Zhang, G. Zhao, and X. Guan, 2013. Fractional order sliding mode controller design for antilock braking systems. Neurocomputing.
Vodovozov, V., A. Aksjonov, E. Petlenkov, and Z. Raud, 2021. Neural network based model reference control of braking electric vehicles. Energies 14: 2373.
Xiao, Y., 2022. DFM-GCN: A multi-task learning recommendation based on a deep graph neural network. Mathematics 10: 721.
Zhao, F., J. An, Q. Chen, and Y. Li, 2024. Integrated path following and lateral stability control of distributed drive autonomous unmanned vehicle. World Electric Vehicle Journal 15: 122.

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