Abstract

In this study 3-DOF quarter car model with the three bumps on the rigid road, the assumption has been modeled with the non-random irregularity. To reduce the excessive vibrations occurred on the vehicle body, an active suspension system with the linear actuator has been considered. Moreover, to control this actuator, an adaptive neuro-fuzzy algorithm is designed. The training and testing data of the ANFIS has been obtained from Proportional Integral Derivative (PID) control algorithm. After that the successful training process, a testing procedure has been applied to ANFIS for the measure of the adaptive neuro-fuzzy system with data that are not considered in the training process. Then, the performance of the ANFIS is compared by the PID algorithm and passive suspension system in terms of vehicle body vertical acceleration, vehicle body vertical displacement, and control force. The road model used in the study has been modeled according to non-random road profile mathematical formulation considering periodical and discrete road profile cases. In this formulation, one can easily determine the height, width, and number of the road defect with the series mathematical formulation. Consequently, with the results obtained from the presented study, it is proven that ANFIS is a very effective controlling algorithm to suppress vibration occurred on the vehicle body due to vehicle road interaction. Furthermore, the performance of the ANFIS has been tested with different parameters, for example, different number membership functions (MF), which used the fuzzification of the input parameters.

References

[1] Koç MA, Esen İ. Modelling and analysis of vehicle-structure-road coupled interaction considering structural flexibility, vehicle parameters and road roughness. J Mech Sci Technol 2017;31. https://doi.org/10.1007/s12206-017-0403-y.

[2] Pacejka HB. Tire Characteristics and Vehicle Handling and Stability. 2012. https://doi.org/10.1016/b978-0-08-097016-5.00001-2.

[3] Ventsel E, Krauthammer T. Thin plates and shells?: theory, analysis, and applications. Marcel Dekker; 2001.

[4] Kokowski P, Makarewicz R. Predicted effects of a speed bump on light vehicle noise. Appl Acoust 2006;67:570–9. https://doi.org/10.1016/j.apacoust.2005.10.001.

[5] Salau TAO, Adeyefa AO, Oke SA. Vehicle speed control using road bumps. Transport 2004;19:130–6. https://doi.org/10.1080/16484142.2004.9637965.

[6] Yang S-C, Jang S-Y. Investigation of the Maintenance Criteria for the Rail Surface Defects in High-Speed Railways. J Korean Soc Railw 2011;14:535–44. https://doi.org/10.7782/jksr.2011.14.6.535.

[7] Zhu M, Cheng X, Miao L, Sun X, Wang S. Advanced Stochastic Modeling of Railway Track Irregularities. Adv Mech Eng 2013;2013. https://doi.org/10.1155/2013/401637.

[8] Valášek M, Kortüm W, Šika Z, Magdolen L, Vaculín O. Development of semi-active road-friendly truck suspensions. Control Eng Pract 1998;6:735–44. https://doi.org/10.1016/S0967-0661(98)00079-3.

[9] Weiss EJ. The New Securities Fraud Pleading Requirement: Speed Bump or Road Block?, 1998.

[10] Pau M, Angius S. Do speed bumps really decrease traffic speed? An Italian experience. Accid Anal Prev 2001;33:585–97. https://doi.org/https://doi.org/10.1016/S0001-4575(00)00070-1.

[11] Elvik R. Area-wide urban traffic calming schemes: A meta-analysis of safety effects. Accid Anal Prev 2001;33:327–36. https://doi.org/10.1016/S0001-4575(00)00046-4.

[12] Zhu P, Hessling P, Liu D. Optimal road hump for comfortable speed reduction. Proc SPIE - Int Soc Opt Eng 2008;7130. https://doi.org/10.1117/12.819725.

[13] Guenounou O, Dahhou B, Athmani B. Optimal design of PID controller by Multi-objective genetic algorithms. n.d.

[14] Carlucho I, De Paula M, Acosta GG. Double Q-PID algorithm for mobile robot control. Expert Syst Appl 2019;137:292–307. https://doi.org/10.1016/J.ESWA.2019.06.066.

[15] Baxter R, Hastings N, Law  a., Glass EJ. [ No Title ]. Anim Genet 2008;39:561–3.