Aktif Araç Süspansiyon Sistemi İçin Makine Öğrenimi Tabanlı Kontrol Sisteminin Geliştirilmesi

Year 2022, Volume: 11 Issue: 2, 421 - 428, 30.06.2022

Ali Rıza Kaleli Halil İbrahim Akolaş

https://doi.org/10.17798/bitlisfen.1014488

Cited By: 1

Abstract

Bu çalışmada, araç aktif süspansiyon sistemini (VASS) kontrol etmek için makine öğrenme yöntemlerinde biri olan Gaussian süreci (GP) algoritması tasarlanmıştır. Deneysel veriler denetimli öğrenme yöntemi (regresyon yöntemi) ile eğitildi. Veriler, tam durum geri beslemeli optimal kontrol yaklaşımına dayalı olarak ayarlanmış optimal bir doğrusal ikinci dereceden kontrolörden elde edildi. Sonuçlar, önerilen makine öğrenme (ML) tabanlı yere nüfuz eden radar (GPR) denetleyicisinin, yaylı kütle konumundaki salınımı azaltma açısından, sırasıyla kare ve rastgele yol koşulları için sırasıyla %15 ve %21,64 azalma ile optimal denetleyiciden daha iyi performans ortaya koyduğunu göstermiştir.

Keywords

Aktif Araç Süspansiyonu, Makine Öğrenme Yöntemleri, Kontrol, Doğrusal Kuadratik Optimal Kontrol, Rastgele Yol Profili

Development of Machine Learning Based Control System for Vehicle Active Suspension System

Abstract

In this paper, Gaussian process (GP) algorithm, which is one of the machine learning methods, is designed to control the vehicle active suspension system (VASS). Experimental data were trained by supervised learning method (regression method). The data were obtained from an optimal linear quadratic controller tuned based on a full state feedback optimal control approach. The results demonstrated that the proposed machine learning (ML) based ground-penetrating radar (GPR) controller outperforms the optimal controller under uncertainties in terms of reducing the oscillation in sprung mass position with a 15% and 21.64% reduction for square and random road conditions, respectively.

Keywords

Vehicle Active Suspension system, Machine Learning Methods, Control, Linear Quadratic Optimal Control, Random Road Profile.

References

• Sitnik L. J., Magdziak-Tokłowicz M., Wróbel R., Kardasz P. 2020. “Vehicle vibration in human health,” J. KONES, 20(4): 411–418.
• Shahid Y. and Wei M. 2020. “Comparative analysis of different model-based controllers using active vehicle suspension system,” Algorithms, 13(1): 10.
• Watton J., Holford K. M., Surawattanawan P. 2004. “The application of a programmable servo controller to state control of an electrohydraulic active suspension,” Proc. Inst. Mech. Eng. Part D J. Automob. Eng, 218(12): 1367–1377.
• Pang H., Zhang X., Yang J., Shang Y. 2019. “Adaptive backstepping‐based control design for uncertain nonlinear active suspension system with input delay,” Int. J. Robust Nonlinear Control, 29(16): 5781–5800.
• Ovalle L., Ríos H., Ahmed H. 2021 “Robust Control for an Active Suspension System via Continuous Sliding-Mode Controllers,” Eng. Sci. Technol. an Int. J.
• Sun W., Gao H., Kaynak O. 2012. “Adaptive backstepping control for active suspension systems with hard constraints,” IEEE/ASME Trans. mechatronics, 18(3): 1072–1079.
• Yagiz N. and Hacioglu Y. 2008. “Backstepping control of a vehicle with active suspensions,” Control Eng. Pract., 16(12): 1457–1467.
• Taskin Y., Hacioglu Y., Yagiz N. 2017. “Experimental evaluation of a fuzzy logic controller on a quarter car test rig,” J. Brazilian Soc. Mech. Sci. Eng, 39(7): 2433–2445.
• Rashid U., Jamil M., Gilani S. O., Niazi I. K. 201. “LQR based training of adaptive neuro-fuzzy controller,” in International Workshop on Neural Networks, 54: 311–322.
• Huang S. J. and Lin W. C. 2007. “A neural network based sliding mode controller for active vehicle suspension,” Proc. Inst. Mech. Eng. Part D J. Automob. Eng, 221(11): 1381–1397.
• Heidari M. and Homaei H. 2013. “Design a PID controller for suspension system by back propagation neural network,” Journal of Eng., vol: 2013.
• Brunton S. L. and Kutz J. N. 2019. Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, vol: 60(472).
• “Active Suspension - Quanser.”. https://www.quanser.com/products/active-suspension. (Available date: 21.09.2021).
• Kopsiaftis G., Protopapadakis E., Voulodimos A., Doulamis N., Mantoglou A. 2019. “Gaussian process regression tuned by bayesian optimization for seawater intrusion prediction,” Comput. Intell. Neurosci, vol: 2019.
• Zhang J., Li W., Zeng L., Wu L. 2016. “An adaptive Gaussian process‐based method for efficient Bayesian experimental design in groundwater contaminant source identification problems,” Water Resour. Res, 52(8): 5971–5984.
• Ceylan Z. 2020. “Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models,” J. Forecast, 39(6): 944–956.
• Chen X., Tian Y., Zhang T., Gao J. 2020. “Differential evolution based manifold Gaussian process machine learning for microwave Filter’s parameter extraction,” IEEE Access, 8: 146450–146462.
• González A., O’brien E. J., Li Y.-Y., Cashell K. 2008. “The use of vehicle acceleration measurements to estimate road roughness,” Veh. Syst. Dyn, 46,(6): 483–499.