MULTI-OBJECTIVE OPTIMIZATION WITH GENETIC ALGORITHM OF AIR SUSPENSION SYSTEM FOR ENHANCING RIDE COMFORT AND ROAD-HOLDING PERFORMANCES
Abstract
This study presents a systematic investigation of air suspension control strategies for vehicular applications, employing computational optimization techniques to enhance both ride comfort and road holding performances. A comprehensive simulation framework was developed, incorporating a quarter-truck model with nonlinear air spring dynamics subjected to spectrally rich road excitations. The research methodology integrates conventional PID control with multi-objective genetic algorithm optimization to determine Pareto-optimal solutions for conflicting performance criteria. Extensive numerical simulations reveal that the optimized controller achieves significant improvements in vibration attenuation (58.4% reduction in sprung mass displacement) while maintaining superior road contact characteristics (41.5% enhancement in tire-road contact force) compared to a passive system. The analysis provides quantitative insights into the fundamental trade-space between ride comfort-oriented and stability-focused control objectives, demonstrating that intelligent optimization approaches can effectively navigate these design compromises. Furthermore, the results establish practical boundaries for control system performance under realistic operating conditions, offering valuable guidelines for automotive suspension system design. The technical contributions include novel control architectures, advanced performance evaluation metrics, and a rigorous methodology for controller optimization in nonlinear air suspension systems. These findings advance the state-of-the-art in vehicle dynamics control and identify several promising avenues for future research in adaptive control systems and hybrid optimization algorithms.
References
Atindana, V. A., Xu, X., Nyedeb, A. N., Quaisie, J. K., Nkrumah, J. K., & Assam, S. P. (2023). The evolution of vehicle pneumatic vibration isolation: A systematic review. Shock and Vibration, 2023, 1–23. https://doi.org/10.1155/2023/1716615
Pradhan, P., & Singh, D. (2023). Review on air suspension system. Materials Today: Proceedings, 81(2), 486–488. https://doi.org/10.1016/j.matpr.2021.03.640
Maljković, M., Blagojević, I., Popović, V., & Stamenković, D. (2018). Impact of the damper characteristics on the behavior of suspension system and the whole vehicle. Journal of Applied Engineering Science, 16(3), 349–357. https://doi.org/10.5937/jaes16-17342
Demić, M. (2020). A contribution to design of semiactive vehicle suspension system. Istraživanja i projektovanja za privredu, 3(9), 7–16.
Xia, Q., Bai, R., & Wang, H. (2018). Fuzzy control of damping force in the air suspension system. In 2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) (pp. 328–330). IEEE. https://doi.org/10.1109/ICCSS.2018.8572384
Bai, X., Lu, L., Zhang, C., & Geng, W. (2023). Research on height adjustment characteristics of heavy vehicle active air suspension based on fuzzy control. World Electric Vehicle Journal, 14(8), 210. https://doi.org/10.3390/wevj14080210
Wang, J., Lv, K., Wang, H., Guo, S., & Wang, J. (2022). Research on nonlinear model and fuzzy fractional order PIλDμ control of air suspension system. Journal of Low Frequency Noise, Vibration and Active Control, 41(2), 712–731. https://doi.org/10.1177/14613484211051854
S., G. P., & K., M. M. (2019). A contemporary adaptive air suspension using LQR control for passenger vehicles. ISA Transactions, 93, 244–254. https://doi.org/10.1016/j.isatra.2019.02.031
Cao, K., Li, Z., Gu, Y., Zhang, L., & Chen, L. (2021). The control design of transverse interconnected electronic control air suspension based on seeker optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(8), 2200–2211. https://doi.org/10.1177/0954407020984667
Zhang, S., Li, M., Li, J., Xu, J., Wang, Z., & Liu, S. (2024). Research on ride comfort control of air suspension based on genetic algorithm optimized fuzzy PID. Applied Sciences, 14(17), 7787. https://doi.org/10.3390/app14177787
Hung, T. M. (2022). Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms. Heliyon, 8(3), e09059. https://doi.org/10.1016/j.heliyon.2022.e09059
Shalabi, M. E., Fath Elbab, A. M. R., El-Hussieny, H., & Abouelsoud, A. A. (2021). Neuro-fuzzy volume control for quarter car air-spring suspension system. IEEE Access, 9, 77611–77623. https://doi.org/10.1109/ACCESS.2021.3081872
Jiang, X., & Cheng, T. (2023). Design of a BP neural network PID controller for an air suspension system by considering the stiffness of rubber bellows. Alexandria Engineering Journal, 74, 65–78. https://doi.org/10.1016/j.aej.2023.05.012
Zhang, J., Yang, Y., & Hu, C. (2023). An adaptive controller design for nonlinear active air suspension systems with uncertainties. Mathematics, 11(12), 2626. https://doi.org/10.3390/math11122626
Ma, X., Wong, P. K., Zhao, J., Zhong, J.-H., Ying, H., & Xu, X. (2018). Design and testing of a nonlinear model predictive controller for ride height control of automotive semi-active air suspension systems. IEEE Access, 6, 63777–63793. https://doi.org/10.1109/ACCESS.2018.2876496
Sun, X., Cai, Y., Wang, S., Liu, Y., & Chen, L. (2016). A hybrid approach to modeling and control of vehicle height for electronically controlled air suspension. Chinese Journal of Mechanical Engineering, 29(1), 152–162. https://doi.org/10.3901/CJME.2015.1202.141
Karimi Eskandary, P., Khajepour, A., Wong, A., & M. A. (2016). Analysis and optimization of air suspension system with independent height and stiffness tuning. International Journal of Automotive Technology, 17(5), 807–816. https://doi.org/10.1007/s12239-016-0079-9
Nazemian, H., & Masih-Tehrani, M. (2020). Development of an optimized game controller for energy saving in a novel interconnected air suspension system. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234(13), 3068–3080. https://doi.org/10.1177/0954407020927147
Van Tan, V., Tu, D. T., Tat Thang, P., Mihaly, A., & Gaspar, P. (2025). Utilizing dynamic road stress factor to determine optimal pressure for air suspension system on tractor semi-trailer to minimize dynamic tire forces impacting on road. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. https://doi.org/10.1177/09544070251321033
Zhang, H., Zhang, H., Zhao, L., Ou, C., Liu, Y., & Shan, X. (2024). Height control strategy design and simulation of electronic control air suspension for trucks. World Electric Vehicle Journal, 15(6), 273. https://doi.org/10.3390/wevj15060273
Hu, Q., Lu, W., & Jiang, J. (2022). Dynamic modeling and adjustable damping layered control of air suspension hybrid system. Australian Journal of Mechanical Engineering, 20(1), 1–13. https://doi.org/10.1080/14484846.2019.1643643
Guowei, D., Wenhao, Y., Zhongxing, L., Khajepour, A., & Senqi, T. (2020). Sliding mode control of laterally interconnected air suspensions. Applied Sciences, 10(12), 4320. https://doi.org/10.3390/app10124320
Johnson, M. A., & Moradi, M. H. (2005). PID control: New identification and design methods. Springer.
Vilanova, R., & Visioli, A. (2012). PID control in the third millennium: Lessons learned and new approaches. Springer.
Van Tan, V., Sename, O., Gaspar, P., & Do, T. T. (2024). Active anti-roll bar control design for heavy vehicles. Springer. https://doi.org/10.1007/978-981-97-1359-2
