Performance Analysis of PID and Fuzzy Logic Controlled Semi-Active and Passive Suspension Elements on Full Vehicle Model
Abstract
In this study, the dynamic performances of full vehicle models were extensively investigated through simulations conducted in the MATLAB-Simulink environment to evaluate their responses to various system inputs, especially passive suspension elements and models equipped with semi-active Magneto-Rheological (MR) dampers. Initially, a full vehicle model was created using passive suspension elements, and the system behaviors against different road inputs are analyzed. Subsequently, integration of a semi-active MR damper onto the same full vehicle model is performed, and this specific damper was controlled using two different control methods: the first control method is selected as PID, and the second one as a Fuzzy Logic Controller (FLC). The system's responses to various road inputs are examined for both control methods and the respective controllers. This study stands out as a method used in the design and performance analysis of suspension systems for full-vehicle models. The results, especially regarding the control of semi-active MR dampers with a Fuzzy Logic Controller, indicate that semi-active dampers can respond more effectively to different road conditions and enhance ride comfort.
References
- 1.Abdi H, Valentin D, Edelman BE (1999). Neural networks. Sage, Thousand Oaks
- 2.Abramowitz M, Stegun AI (1970) Handbook of mathematical function with formulas,
- 3.graphs, and mathematical tables. Applied Mathematical Series, N.B.S.
- 4.Agarwal M (1997) A systemic classification of neural-network-based control. IEEE Control
- 5.Syst Mag 17, 2:75–93
- 6.Agrawal A, Kulkarni P, Vieira SL, Naganathan NG (2001) An overview of magneto- and
- 7.electro-rheological fluids and their applications in fluid power systems. Int J Fluid
- 8.Power 2:5–36
- 9.Ahmadian M, Marjoram RH (1989) Effects of passive and semi-active suspension on body
- 10.and wheel-hop control. SAE paper 892487
- 11.METİN, MUZAFFER and GÜÇLÜ, RAHMİ (2011) "Vibrations control of light rail transportation vehicle via PID type fuzzy controller using parameters adaptive method," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 19: No. 5, Article 11
- 12.Al-Houlu N, Weaver J, Lahdhiri T, Joo DS (1999) Sliding mode-based fuzzy logic
- 13.controller for a vehicle suspension system. American Control Conference, San Diego,
- 14.USA, pp 4188–419
Esen, U. Y., Metin, M. (2024). Performance Analysis of PID and Fuzzy Logic Controlled Semi-Active and Passive Suspension Elements on Full Vehicle Model. *Orclever Proceedings of Research and Development*, 4(1), 59-72. https://doi.org/10.56038/oprd.v4i1.456
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