Sistem tanımlama yöntemi kullanılarak çeyrek araç süspansiyon sisteminin dinamik modelinin tahmini

Yıl 2025, Cilt: 14 Sayı: 1, 1 - 1

Murat Catalkaya

https://doi.org/10.28948/ngumuh.1544291

Öz

Araçların hareket halinde karşılaştıkları titreşimler, konfor ve güvenlik açısından olumsuz etkilere yol açmaktadır. Bu titreşimlerin bastırılmasında, yol tutuşu ve yolcu konforu için süspansiyon sistemleri kritik bir role sahiptir. Bu çalışmada, araç dinamiği araştırmalarında yaygın olarak kullanılan çeyrek araç süspansiyon modeli üzerine, sistemin dinamik davranışını doğru ve verimli şekilde temsil eden bir sistem tanımlama yaklaşımı geliştirilmiştir. İlk olarak, çeyrek araç süspansiyon sisteminin matematiksel hareket denklemleri oluşturulmuş ve Matlab-Simulink ortamında modellenmiştir. Farklı yol senaryoları bu modele uygulanarak zaman verileri toplanmıştır. Ardından, elde edilen veriler Auto Regressive with Exogenous İnputs (ARX), Output Error (OE), Box Jenkins (BJ) ve Autoregressive Moving Average with Exogenous İnput (ARMAX) gibi sistem tanımlama yöntemlerine tabi tutulmuş ve sistemin matematiksel transfer fonksiyonu modeli çıkarılmıştır. Son olarak ise bu modeller doğrulama kriterlerinden ve testlerinden geçirildikten sonra en uygun uyum sağlayan model, sistemi tanımlayan model olarak seçilmiştir. Sonuç olarak, ARMAX modeli süspansiyon dinamiğini en iyi temsil eden model olarak belirlenmiştir.

Anahtar Kelimeler

Süspansiyon sistemi, Dinamik model, Sistem tanımlama, MATLAB, Simulink

Estimation of dynamic model of quadrant vehicle suspension system using system identification method

Öz

The vibrations that vehicles experience in motion have a negative impact on comfort and safety. Suspension systems play a critical role in suppressing these vibrations for road holding and passenger comfort. In this study, a system identification approach that accurately and efficiently represents the dynamic behaviour of the system has been developed based on the quarter car suspension model, which is widely used in vehicle dynamics research. Firstly, the equations of motion of the quarter car suspension system were generated and modelled in the Matlab-Simulink environment. Different road scenarios were applied to this model and time data were collected. The data obtained were then subjected to system identification methods such as Autoregressive with Exogenous Inputs (ARX), Output Error (OE), Box Jenkins (BJ) and Autoregressive Moving Average with Exogenous Input (ARMAX) and the mathematical transfer function model of the system was derived. Finally, after subjecting these models to validation criteria and tests, the model that provided the best fit was selected as the model to describe the system. As a result, the ARMAX model was identified as the model that best represented the suspension dynamics.

Anahtar Kelimeler

Suspension system, Dynamic model, System identification, MATLAB, Simulink

Kaynakça

• D. N. Nguyen and T. A. Nguyen, The dynamic model and control algorithm for the active suspension system. Mathematical Problems in Engineering, 2023(1), 1-9, 2023. https://doi.org/10.1155/2023/2889435.
•    U. Kırbaş and M. Karasahin, Karayolu-demiryolu hemzemin geçitlerinde maruz kalınan titreşimin insan sağlığını etkileme seviyeleri. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(2), 487-500, 2023. https://doi.org/10.2894
•    T. E. Putra, N. Husaini, and M. Ikbal, Automotive suspension component behaviors driven on flat and rough road surfaces. Heliyon, 7(7), 1-9, 2021. https://doi.org/10.1016/j.heliyon.2021.e07528.
•    T. A. Nguyen, Advance the stability of the vehicle by using the pneumatic suspension system integrated with the hydraulic actuator. Latin American Journal of Solids and Structures, 18(7), 1-16, 2021. https://doi.org/10.1590/1679-78256621.
•    C. Poussot-Vassal, O. Sename, L. Dugard, P. Gáspár, Z. Szabó, and J. Bokor, A new semi-active suspension control strategy through LPV technique. Control Engineering Practice, 16(12), 1519–1534, 2008. https://doi.org/10.1016/j.conengprac.2008.
•    D. Hrovat, D. L. Margolis, and M. Hubbard, An approach toward the optimal Semi-Active suspension, Journal of Dynamic Systems Measurement and Control, 110(3), 288–296, 1988. https://doi.org/10.1115/1.3152684.
•    M. Z. Q. Chen, Y. Hu, C. Li, and G. Chen, Semi-active suspension with semi-active inerter and semi-active damper. IFAC Proceedings, 47(3), 11225–11230, 2014. https://doi.org/10.3182/20140824-6-ZA-1003.00138.
•    M. Canale, M. Milanese, and C. Novara, Semi-active suspension control using ‘fast’ model-predictive techniques. IEEE Transactions on Control Systems Technology, 14(6), 1034–1046, 2006. https://doi.org/10.1109/TCST.2006.880196.
•    A. Fien, Active suspension systems for rail vehicles, Vehicle System Dynamics. 6(2-3), 206, 1977. https://doi.org/10.1080/00423117708968542.
• A. Soliman and M. Kaldas, Semi-active suspension systems from research to mass-market – A review. Journal of Low Frequency Noise, Vibration and Active Control, 40(2), 1005–1023, 2019. https://doi.org/10.1177/1461348419876392.
• J. Yang, D. Ning, S.S. Sun, J. Zheng, H. Lu, M. Nakano, S. Zhang, H. Du, W.H. Li, A semi-active suspension using a magnetorheological damper with nonlinear negative-stiffness component. Mechanical Systems and Signal Processing, 147(2021), 1-21,
• M. Ghoniem, T. Awad, and O. Mokhiamar, Control of a new low-cost semi-active vehicle suspension system using artificial neural networks. Alexandria Engineering Journal, 59(5), 4013–4025, 2020. https://doi.org/10.1016/j.aej.2020.07.007.
• N. A. Saadabad, H. Moradi, and G. Vossoughi, Semi-active control of forced oscillations in power transmission lines via optimum tuneable vibration absorbers: With review on linear dynamic aspects. International Journal of Mechanical Sciences, 8
• H. Okuturlar and M. Tinkir, Araç süspansiyon sisteminin nümerik ve deneysel dinamik analizi. Konya Journal of Engineering Sciences, 9(1), 85–105, 2021. https://doi.org/10.36306/konjes.778390.
• M. Daş, E. Alıç, and E. K. Akpinar, Numerical and experimental analysis of heat and mass transfer in the drying process of the solar drying system. Engineering Science and Technology an International Journal, 24(1), 236–246, 2020. https://doi.o
• S. Gupta, R. Gupta, and S. Padhee, Parametric system identification and robust controller design for liquid–liquid heat exchanger system. IET Control Theory and Applications, 12(10), 1474–1482, 2018. https://doi.org/10.1049/iet-cta.2017.1128.
• L. Ljung, System identification. In Signal analysis and Prediction, Birkhäuser, Boston, MA, 1998.
• A. K. Tangirala, Principles of system identification. Taylor and Francis, Abingdon, 2018.
• P. Shah and R. Sekhar, Closed loop system ıdentification of a dc motor using fractional order model. 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE), pp. 69-74, Bali, Indonesia, 2019.
• P. Shah, S. Agashe, and V. Vyawahare, System identification with fractional-order models: A comparative study with different model structures. Progress in Fractional Differentiation and Applications, 4(4), 533–552, 2019. http://dx.doi.org/10.18
• H. Hjalmarsson, From experiment design to closed-loop control. Automatica, 41(3), 393–438, 2005. https://doi.org/10.1016/j.automatica.2004.11.021.
• P. Wolszczak, K. Łygas, and G. Litak, Dynamics identification of a piezoelectric vibrational energy harvester by image analysis with a high speed camera. Mechanical Systems and Signal Processing, 107, 43–52, 2018. https://doi.org/10.1016/j.ymss
• R. Cechowicz and P. Stączek, Computer supervision of the group of compressors connected in parallel. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 16(2), 198-202, 2014.
• A. Martynenko, Computer Vision for Real-Time control in drying. Food Engineering Reviews, 9(2), 91–111, 2017. https://doi.org/10.1007/s12393-017-9159-5.
• ., P. Yang, S.-L. Ding, G. Litak, E.-Z. Song, and X.-Z. Ma, Identification and quantification analysis of nonlinear dynamics properties of combustion instability in a diesel engine. Chaos an Interdisciplinary Journal of Nonlinear Science, 25(1),
• P. Harris, M. Arafa, G. Litak, C. R. Bowen, and J. Iwaniec, Output response identification in a multistable system for piezoelectric energy harvesting. The European Physical Journal B, 90(1), 1-11, 2017. https://doi.org/10.1140/epjb/e2016-70619
• G. Litak and R. Rusinek, Identification of turning and milling processes by stochastic langevin equations. 2012 IEEE 4th International Conference on Nonlinear Science and Complexity (NSC), pp. 41-44, Budapest, Hungary, 2012.
• L. Ljung, System identification: An Overview. in Encyclopedia of Systems and Control, pp. 2302–2317, 2021. https://doi.org/10.1007/978-3-030-44184-5_100.
• Y. H. Eng, K. M. Teo, M. Chitre and K. M. Ng, Online System Identification of an Autonomous Underwater Vehicle Via In-Field Experiments. in IEEE Journal of Oceanic Engineering, 41(1), 5-17, 2016. https://doi.org/10.1109/JOE.2015.2403576.
• A. Garg, K. Tai, and B. N. Panda, System identification: Survey on modeling methods and models. in Advances in intelligent systems and computing, 607–615, 2017. https://doi.org/10.1007/978-981-10-3174-8_51.
• Y.-C. Lai and Q. Le Tri, System identification and control of a small unmanned helicopter at hover mode, 2017 2nd International Conference on Control and Robotics Engineering (ICCRE), Bangkok, Thailand, pp. 92-96, 2017.
• Z. Hasiewicz, P. L. Śliwiński, and G. Mzyk, Nonlinear system identification under various prior knowledge. IFAC Proceedings Volumes, 41(2), 7849–7858, 2008. https://doi.org/10.1007/s11277-021-08954-7.
• G. Antonelli, S. Chiaverini and G. Fusco, A fuzzy-logic-based approach for mobile robot path tracking, in IEEE Transactions on Fuzzy Systems, 15(2), 211-221, 2007. https://doi.org/10.1109/TFUZZ.2006.879998.
• R. Aruna and S. T. J. Christa, Modeling, system identification and design of fuzzy PID controller for discharge dynamics of metal hydride hydrogen storage bed. International Journal of Hydrogen Energy, 45(7), 4703–4719, 2019. https://doi.org/10
• E. Alvarez-Sánchez, A Quarter-Car suspension system: car body mass estimator and sliding mode control. Procedia Technology, 7, 208–214, 2013. https://doi.org/10.1016/j.protcy.2013.04.026.
• T. S. Ng, G. C. Goodwin, and T. Söderström, Optimal experiment design for linear systems with input-output constraints. Automatica, 13(6), 571–577, 1977. https://doi.org/10.1016/0005-1098(77)90078-4.
• A. A. Mahfouz, M. M. K, and F. A. Salem, Modeling, simulation and dynamics analysis issues of electric motor, for mechatronics applications, using different approaches and verification by MATLAB/Simulink. International Journal of Intelligent Sy
• L. Ljung, System Identification. in: W. S. Levine. (eds.) The Control Systems Handbook: Control System Advanced Methods. Second Edition, pp. 57-1, CRC Press, Heidelberg, 2010.
• A. B. Singh, R. V. Murugan, K. Saravanan, A. S. Ahmed, and R. Vinoth, Fractional order control and comparative analysis of a hybrid system. Procedia Computer Science, 48, 37–44, 2015. https://doi.org/10.1016/j.procs.2015.04.107.
• T. Kumbasar, I. Eksin, M. Guzelkaya, and E. Yesil, Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm. Expert Systems With Applications, 38(10), 12356–12364, 2011. https://doi.org/10.1016/j.eswa.2011.04.015.
• S. Kumar, A. Medhavi, R. Kumar, and P.K. Mall, Modeling, analysis and PID controller implementation on suspension system for quarter vehicle model. J. Mech. Eng. Sci., 16(2), 8905–8916, 2022. https://doi.org/10.15282/jmes.16.2.2022.08.0704.
• O. Eser, A. Çakan, M. Kalyoncu, and F. Botsali, Arı algoritması (aa) ve parçacık sürü optimizasyonu (pso) kullanarak çeyrek araç modeli tasarım parametrelerinin belirlenmesi. Konya Journal of Engineering Sciences, 9(3), 621–632, 2021. https://d
• K. A. Mohideen, G. Saravanakumar, K. Valarmathi, D. Devaraj, and T. K. Radhakrishnan, Real-coded Genetic Algorithm for system identification and tuning of a modified Model Reference Adaptive Controller for a hybrid tank system. Applied Mathemat
• C. Lewis, International and Business Forecasting Methods Butterworths. London, 1982.
• H. İ. Çardaklı, İnsan-makina etkileşimli bilgisayar deneyi kullanarak insan operatörlerin parametrik ve akıllı sistemlerle modellenmesi, Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2005.