Quarter Car Active Suspension System Control Using Fuzzy Controller

Year 2022, Volume: 2 Issue: 4, 33 - 39, 31.12.2022

Turan Alp Arslan , Faruk Emre Aysal İbrahim Çelik Hüseyin Bayrakçeken , Tuğçe Nur Öztürk

https://doi.org/10.29228/eng.pers.66798

Abstract

The performance of active suspension systems is directly related to the mechanical design and control of the system. The stable operation of the controller improves driving comfort and handling. The quarter car model is frequently used in the analysis of suspension systems due to its simple structure. In this study, Matlab Simulink software was used in the modeling, control and simulation of the quarter car active suspension model. System performance was investigated for four different road profiles with PID and fuzzy logic control methods. Two of the road profiles used are in the form of impact signals consisting of pits and bumps. The other two road profiles are random road disturbances with high frequency. The effects of active and passive suspension systems on driving comfort are compared by taking into account the control methods used. As a result of the study, it has been determined that the fuzzy logic controller gives better results in pulse signals consisting of bumps and pits, and the PID controller gives better results in high-frequency random road disturbances. In addition, with the use of fuzzy logic control method in the active suspension system, a significant decrease in the actuator force has occurred. This result is very interesting in terms of minimizing energy, reducing actu-ator sizes and reducing costs.

Keywords

Active suspension, Fuzzy logic, Quarter car model, Vehicle dynamics

References

• 1. Samadi, F., and Moghadam-Fard, H. (2015). Active sus-pension system control using adaptive neuro fuzzy (anfis) controller. International Journal of Engineering, 28(3), 396-401.
• 2. Altun, Y. (2017). Çeyrek taşıt aktif süspansiyon sistemi için LQR ve LQI denetleyicilerinin karşılaştırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(3), 61-70.
• 3. Basari, A. A., and Saat, M. S. M. (2007). Control of a quarter car nonlinear active suspension system. 2007 Asia-Pacific Conference on Applied Electromagnetics Proceed-ings, APACE2007.
• 4. Gysen, B. L. J., Paulides, J. J. H., Janssen, J. L. G., and Lomonova, E. A. (2010). Active electromagnetic suspen-sion system for improved vehicle dynamics. IEEE Trans-actions on Vehicular Technology, 59(3), 1156–1163.
• 5. Shim, T., and Velusamy, P. C. (2011). Improvement of vehicle roll stability by varying suspension properties. Ve-hicle System Dynamics, 49(1–2), 129–152.
• 6. Cao, D., Song, X., and Ahmadian, M. (2011). Editors’ per-spectives: road vehicle suspension design, dynamics, and control. Vehicle System Dynamics, 49(1–2), 3–28.
• 7. Zeinali, M., and Darus, I. Z. M. (2012). Fuzzy PID control-ler simulation for a quarter-car semi-active suspension sys-tem using Magnetorheological damper. 2012 IEEE Confer-ence on Control, Systems & Industrial Informatics, 104–108.
• 8. Alvarez-Sánchez, E., (2013). A quarter-car suspension system: car body mass estimator and sliding mode control. Procedia Technology, 7, 208–214.
• 9. Van Der Sande, T. P. J., Gysen, B. L. J., Besselink, I. J. M., Paulides, J. J. H., Lomonova, E. A., and Nijmeijer, H. (2013). Robust control of an electromagnetic active sus-pension system: Simulations and measurements. Mecha-tronics, 23(2), 204–212.
• 10. Turnip, A., and Panggabean, J. H. (2020). Hybrid control-ler design based magneto-rheological damper lookup table for quarter car suspension. Int. J. Artif. Intell, 18(1), 193-206.
• 11. Wang, R., Sheng, F., Ding, R., Meng, X., and Sun, Z. (2021). Vehicle attitude compensation control of magneto-rheological semi-active suspension based on state observer. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(14), 3299-3313.
• 12. Kumar, J., and Bhushan, G. (2022). Dynamic analysis of quarter car model with semi-active suspension based on combination of magneto-rheological materials. Interna-tional Journal of Dynamics and Control, 1-9.
• 13. Jamadar, M. E. H., Desai, R. M., Saini, R. S. T., Kumar, H., and Joladarashi, S. (2021). Dynamic analysis of a quarter car model with semi-active seat suspension using a novel model for magneto-rheological (MR) damper. Journal of Vibration Engineering & Technologies, 9(1), 161-176.
• 14. Pang, H., Wang, Y., Zhang, X., and Xu, Z. (2019). Robust state-feedback control design for active suspension system with time-varying input delay and wheelbase preview in-formation. Journal of the Franklin Institute, 356(4), 1899-1923.
• 15. Meng, Q., Qian, C., and Liu, R. (2018). Dual rate sampled data stabilization for active suspension system of electric vehicle. International Journal of Robust and Nonlinear Control, 28(5), 1610-1623.
• 16. Min, X., Li, Y., and Tong, S. (2020). Adaptive fuzzy out-put feedback inverse optimal control for vehicle active suspension systems. Neurocomputing, 403, 257-267.
• 17. Pusadkar, U. S., Chaudhari, S. D., Shendge, P. D., and Phadke, S. B. (2019). Linear disturbance observer based sliding mode control for active suspension systems with non-ideal actuator. Journal of Sound and Vibration, 442, 428-444.
• 18. Chen, L., Xu, X., Liang, C., Jiang, X. W., and Wang, F. (2022). Semi-active control of a new quasi-zero stiffness air suspension for commercial vehicles based on H2H∞ state feedback. Journal of Vibration and Control, 10775463211073193.
• 19. Viadero-Monasterio, F., Boada, B. L., Boada, M. J. L., and Díaz, V. (2022). H∞ dynamic output feedback control for a networked control active suspension system under actuator faults. Mechanical Systems and Signal Processing, 162, 108050.
• 20. Thompson, A. (1976). An active suspension with optimal linear state feedback. Vehicle System Dynamics, 5 (4), 187-203.
• 21. Thompson, A. and Davis, B. (1989). Optimal linear active suspensions with vibration absorbers and integral output feedback control. Vehicle System Dynamics, 18(6), 321-344.
• 22. Cheok, K.C., Loh, N.-K., McGee, H.D. and Petit, T.F. (1985). Optimal model-following suspension with micro-computerized damping. Industrial Electronics, IEEE Trans-actions on, No. 4, 364-371.
• 23. Esmailzadeh, E. and Taghirad, H. (1998). Active vehicle suspensions with optimal state-feedback control. Interna-tional Journal of Modelling and Simulation, 18, 228-238.
• 24. Aubouet, S., Dugard, L. and Sename, O. (2009). H∞/lpv observer for an industrial semi-active suspension. Control Applications, (CCA) & Intelligent Control, 756-763.
• 25. Lin, J.-S. and Kanellakopoulos, I. (1997). Nonlinear de-sign of active suspensions. Control Systems, IEEE, 17(3), 45-59.
• 26. Sam, Y.M., Osman, J.H. and Ghani, M.R.A. (2004). A class of proportional-integral sliding mode control with application to active suspension system. Systems & Con-trol Letters, 51(3), 217-223.
• 27. Foda, S. G. (2000). Fuzzy control of a quarter-car suspen-sion system. Proceedings of the International Conference on Microelectronics, ICM, 231–234
• 28. Yoshimura, T. (1996). Active suspension of vehicle sys-tems using fuzzy logic. International Journal of Systems Science, 27(2), 215-219.
• 29. Rao, M. and Prahlad, V. (1997). A tunable fuzzy logic controller for vehicle-active suspension systems. Fuzzy Sets And Systems, 85(1), 11-21.
• 30. Campos, J., Lewis, F., Davis, L. and Ikenaga, S. (2000). Backstepping based fuzzy logic control of active vehicle suspension systems. in American Control Conference, Proceedings of IEEE, 6, 4030-4035.
• 31. Lauwerys, C., Swevers, J. and Sas, P. (2005). Robust line-ar control of an active suspension on a quarter car test-rig. Control Engineering Practice, 13(5), 577-586.
• 32. Park, S. and Rahmdel, S. (2013). A new fuzzy sliding mode controller with auto-adjustable saturation boundary layers implemented on vehicle suspension. International Journal of Engineering-Transactions C: Aspects, 26(12), 1401.
• 33. Mustafa, G. I., Wang, H., and Tian, Y. (2019). Model-free adaptive fuzzy logic control for a half-car active suspen-sion system. Studies in Informatics and Control, 28(1), 13-24.
• 34. Nagarkar, M., Bhalerao, Y., Bhaskar, D., Thakur, A., Hase, V., and Zaware, R. (2022). Design of passive suspension system to mimic fuzzy logic control active suspension sys-tem. Beni-Suef University Journal of Basic and Applied Sciences, 11(1), 1-15.
• 35. Khodadadi, H., and Ghadiri, H. (2018). Self-tuning PID controller design using fuzzy logic for half car active sus-pension system. International Journal of Dynamics and Control, 6(1), 224-232.
• 36. Bingül, Ö., and Yıldız, A. (2022). Fuzzy logic and propor-tional integral derivative based multi-objective optimiza-tion of active suspension system of a 4×4 in-wheel motor driven electrical vehicle. Journal of Vibration and Control, 10775463211062691.
• 37. Yatak, M. Ö., and Şahin, F. (2021). Ride comfort-road holding trade-off improvement of full vehicle active sus-pension system by interval type-2 fuzzy control. Engineer-ing Science and Technology, an International Journal, 24(1), 259-270.
• 38. Özdemir, A. ve Maden D. (2013). Aktif süspansiyon sis-temli çeyrek araç modelinin gözlemleyiciyle optimal kontrolü. SAÜ. Fen Bil. Der., 17(2), 181-187.
• 39. Palm, W., J., (2010). System Dynamics Second Edition”, Mcgraw-Hill, Newyork, Usa, 807.
• 40. Al-Ghanim, A. M. H. and Nassar, A. A. (2018). Modeling, simulation, and control of half car suspension system us-ing Matlab/Simulink. International Journal of Science and Research (IJSR), 7(1), 351-362.
• 41. Palanisamy, S. and Karuppan, S. (2016). Fuzzy control of active suspension system. JVE International LTD. Journal of Vibroengineering, 18(5), 3197-3204.
• 42. Salem, M. M. M. and Aly, A. A. (2009). Fuzzy control of a quarter-car suspension system. Internatonal Journal of Computer and Information Engineering, 3(5), 1277-1281.