Fuzzy Logic Controller for Half Vehicle Active Suspension System: An Assessment on Ride Comfort and Road Holding

Abstract

Vehicle suspension system results in a trade-off between the crucial requirements of road holding and ride comfort. To increase the suspension performance and overcome this difficulty, vehicle dynamic systems must be equipped with new technologies and intelligent materials. This paper proposes a fuzzy logic control strategy to enhance ride comfort and road holding dynamics for a half-vehicle active suspension system. The fuzzy logic controller offers a control approach that enables systems with uncertainty and complexity to be managed effectively. Two fuzzy logic controllers for each tire are executed and arranged for the characteristics of the unsprung and sprung masses. The controllers’ inputs are suspension deflection and sprung mass acceleration for each tire. Moreover, actuator force is generated as the controllers’ outputs. Bump road disturbances are applied to each tire for performance evaluation of the controllers. The performance criteria for the suspension system are selected as acceleration and displacement of sprung mass, suspension deflection, and dynamic tire load. These parameters are compared to the passive suspension system and evaluated on ride comfort and road holding. Ride comfort is enhanced by 34% with the active suspension system, including a fuzzy logic controller. Furthermore, road holding is improved by about 13% regarding suspension deflection and dynamic tire load. In conclusion of the simulation, the proposed control approach enhances ride comfort and road-holding dynamics concurrently.

Keywords

• Active suspension system
• Fuzzy logic
• Half-vehicle model
• Ride comfort
• Road holding

References

1. [1] Yatak MO, Sahin F. Ride comfort-road holding trade-off im-provement of full vehicle active suspension system by inter-val type-2 fuzzy control. Eng Sci Technol Int J. 2021;24:259–270. https://doi.org/10.1016/j.jestch.2020.10.006
2. [2] Fischer D, Isermann R. Mechatronic semi-active and active vehicle suspensions. Control Eng Pract. 2004;12(11):1353–1367. https://doi.org/10.1016/j.conengprac.2003.08.003
3. [3] Tseng HE, Hrovat D. State of the art survey: active and semi-active suspension control. Veh Syst Dyn. 2015;53(7):1034–1062. https://doi.org/10.1080/00423114.2015.1037313
4. [4] Sharp RS, Peng H. Vehicle dynamics applications of optimal control theory. Veh Syst Dyn. 2011;49(7):1073–1111. https://doi.org/10.1080/00423114.2011.586707
5. [5] Josee M, Sosthene K, Pacifique T. Review of semi-active suspension based on Magneto-rheological damper. Eng Per-spective.2021;1(2):38–51. http://dx.doi.org/10.29228/eng.pers.50853
6. [6] Unguritu MG, Nichițelea TC, Selișteanu D. Design and per-formance assessment of adaptive harmonic control for a half-car active suspension system. Complexity. 2022:1–14. https://doi.org/10.1155/2022/3190520
7. [7] Guo Y, Ren C. Research on vibration reduction of half-vehicle active suspension system based on time-delayed feed-back control with wheel displacement. Mechanika. 2022;28(1):58–66. https://doi.org/10.5755/j02.mech.28809
8. [8] Eroğlu M., Koç MA, Kozan R, Esen İ. Comparative analysis of full car model with driver using PID and LQR controllers. Int J Automot Sci Technol. 2022;6:178–188. https://doi.org/10.30939/ijastech..1076443

9. [9] Bay OF, Yatak MO. Type-2 fuzzy logic control of a photovol-taic sourced two stages converter. J Intell Fuzz Syst.2018;35(1):1103–1117. https://doi.org/10.3233/JIFS-17865
10. [10] Demir O, Keskin I, Cetin S. Modeling and control of a nonlin-ear half-vehicle suspension system: a hybrid fuzzy logic ap-proach. Nonlinear Dyn. 2012;67(3):2139–2151. https://doi.org/10.1007/s11071-011-0135-y
11. [11] Ozbek C, Ozguney OC, Burkan R, Yagiz N. Ride comfort improvement using robust multi-input multi-output fuzzy logic dynamic compensator. Proc Inst Mech Eng I: J Syst Control Eng. 2023;237(1):72–85. https://doi.org/10.1177/09596518221118407
12. [12] Gandhi P, Adarsh S, Ramachandran KI. Performance Analysis of half car suspension model with 4 DOF using PID, LQR, FUZZY, and ANFIS controllers, Procedia Comput Sci.2017;115:2–13. https://doi.org/10.1016/j.procs.2017.09.070
13. [13] Nagarkar M, Bhalerao Y, Bhaskar D. Thakur A, Hase V, Zaware R. Design of passive suspension system to mimic fuzzy logic control active suspension system. Beni-Suef Univ J Basic Appl Sci. 2022;11(109):1–15. https://doi.org/10.1186/s43088-022-00291-3
14. [14] Yoshimura T, Nakaminami K, Kurimoto M, Hino J. Active suspension of passenger cars using linear and fuzzy-logic con-trols, Control Eng Pract. 1999;7(1):41–47. https://doi.org/10.1016/S0967-0661(98)00145-2
15. [15] Arslan TA, Aysal FE, Çelik İ, Bayrakçeken H, Öztürk:TN. Quarter Car Active Suspension System Control Using Fuzzy Controller. Eng Perspective.2022;2(4):33–39. http://dx.doi.org/10.29228/eng.pers.66798
16. [16] Jibril M, Tadesse M,Hassen N. Nonlinear active suspension system control using fuzzy model predictive controller. J Eng Appl Sci. 2021;16 (9):289–295. doi:10.7537/marsnys140721.01.
17. [17] Ozbek C, Ozguney OC, Burkan R, Yagiz N. Design of a fuzzy robust-adaptive control law for active suspension systems. Sādhanā. 2020;45(194):1–16. https://doi.org/10.1007/s12046-020-01433-y
18. [18] Hsiao CY, Wang YH. Evaluation of ride comfort for active suspension system based on self-tuning fuzzy sliding mode control. Int J Control Autom Syst. 2022;20:1131–1141. https://doi.org/10.1007/s12555-020-0736-7
19. [19] Rao MVC, Prahlad V. A tunable fuzzy logic controller for vehicle-active suspension systems. Fuzzy Sets Syst. 1997;85(1):11–21. https://doi.org/10.1016/0165-0114(95)00369-X
20. [20] Robert JJ, Kumar PS, Nair ST, Moni DHS, Swarneswar B. Fuzzy control of active suspension system based on quarter car model, Mater. Today Proc.2022;6(3):902–908. https://doi.org/10.1016/j.matpr.2022.04.575.
21. [21] Barr AJ, Ray JI. Control of an active suspension using fuzzy logic. Proceedings of IEEE 5th International Fuzzy Systems, New Orleans. LA, USA. 1996;1:42–48. doi: 10.1109/FUZZY.1996.551717
22. [22] Montazeri-Gh M, Soleymani M. Genetic optimization of a fuzzy active suspension system based on human sensitivity to the transmitted vibrations. P I Mech Eng D-J Aut. 2008;222(10):1769–1780. https://doi.org/10.1243/09544070JAUTO854
23. [23] Nagarkar MP, Bhalerao YJ, Patil GJV, Patil RNZ. GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control. Int J Mech Mater Eng. 2018;13(10):1–20. https://doi.org/10.1186/s40712-018-0096-8
24. [24] Talib MHA, Rosli NHM, Darus IZM, Yatim HM, et al. Fuzzy logic controller by particle swarm optimization discoverer for semi-active suspension system. In: Abdullah, M.A., et al. Ad-vances in Intelligent Manufacturing and Mechatronics. Lecture Notes in Electrical Engineering.2023;988:199–209.https://doi.org/10.1007/978-981-19-8703-8_17
25. [25] Chao CT, Liu MT, Wang CJ, Chiou JS. A fuzzy adaptive con-troller for cuckoo search algorithm in active suspension sys-tem. J Low Freq Noise V A. 2020;39(3):761–771. https://doi.org/10.1177/1461348418811473
26. [26] Chiou JS, Liu MT. Using fuzzy logic controller and evolution-ary genetic algorithm for automotive active suspension system. Int J Automot Technol. 2009;10:703–710. https://doi.org/10.1007/s12239-009-0083-4
27. [27] Pekgökgöz R K, Gürel M.A, Bilgehan M, Kısa M. Active sus-pension of cars using fuzzy logic controller optimized by ge-netic algorithm. International Journal of Engineering and Ap-plied Sciences. 2010;2(4):27–37.
28. [28] Sun W, Gao H, Kaynak O. Adaptive backstepping control for active suspension systems with hard constraints. IEEE ASME Trans Mechatron. 2012;18(3):1072–1079. doi:10.1109/TMECH.2012.2204765
29. [29] Afshar KK, Javadi A. Constrained H∞ control for a half-car model of an active suspension system with actuator time delay by predictor feedback. J Vib Control. 2019;25(10):1673–1692. https://doi.org/10.1177/1077546319828457
30. [30] Sosthene K, Emmanuel K, Josee M. Vehicle ride comfort optimization based on Magneto-rheological damper. Int J Au-tomot Sci Technol. 2018;2(4):1–8.
31. [31] Heißing B, Ersoy M. (2011). Ride Comfort and NVH. In: Heißing, B., Ersoy, M. (eds) Chassis Handbook. 2011;421–448 https://doi.org/10.1007/978-3-8348-9789-3_5
32. [32] Zadeh LA. Fuzzy sets. Inf Control. 1965;8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
33. [33] Yatak MO, Bay OF. Fuzzy control of a grid connected three phase two stage photovoltaic system. POWERENG Int. Conf. Malaga, Spain, 2011:1-6. doi: 10.1109/PowerEng.2011.6036477