Vibration Control of Light Rail Vehicle Active Suspension System Using Bargaining Game-Based Distributed Model Predictive Control
DOI:
10.29303/jppipa.v12i11.15923Published:
2026-11-11Downloads
Abstract
This paper proposes a distributed model predictive control (DMPC) scheme with preview disturbance based on bargaining game theory for the active suspension control of a light rail vehicle (LRV). The objective is to improve ride comfort and safety by minimizing the deflection of suspension system and the vertical acceleration of the car bodies. The LRV is modeled as a multi-body dynamical system consisting of two power carbodies and one trailer carbody. The proposed DMPC framework decomposes the vehicle into three dynamically interacting subsystems, each governed by a local MPC controller. The optimal control inputs are determined by formulating the control problem as a bargaining game, which is then solved by a negotiation algorithm. The proposed approach is evaluated through numerical simulations under sinusoidal track disturbances and compared with a centralized MPC scheme. The simulation results demonstrate that the proposed DMPC effectively attenuates vertical acceleration and suspension deflections, thereby enhancing ride comfort and operational safety while achieving superior overall control performance. These findings indicate that the proposed DMPC provides an effective and computationally attractive alternative to centralized MPC.
Keywords:
Active suspension system Bargaining game Distributed control scheme Light rail vehicle Model predictive controlReferences
Cavone, G., Boom, T. V. D., Blenkers, L., Dotoli, M., Seatzu, C., & Schutter, B. D. (2022). An MPC-Based Rescheduling Algorithm for Disruptions and Disturbances in Large-Scale Railway Networks. IEEE Transactions on Automation Science and Engineering, 19(1). https://doi.org/10.1109/TASE.2020.3040940
Farina, M., & Rocca, M. (2023). A Novel Distributed Algorithm for Estimation and Control of Large-Scale Systems. European Journal of Control, 72. https://doi.org/10.1016/j.ejcon.2023.100820
Felez, J., Kim, Y., & Borrelli, F. (2019). A Model Predictive Control Approach for Virtual Coupling in Railways. IEEE Transactions on Intelligent Transportation Systems, 20(7). https://doi.org/10.1109/TITS.2019.2914910
Jho, H., & Ha, M. (2024). Towards Effective Argumentation: Design and Implementation of a Generative AI-Based Evaluation and Feedback System. Journal of Baltic Science Education, 23(2), 280–291. https://doi.org/10.33225/jbse/24.23.280
Lin, Y. C., Lin, C. L., & Shieh, N. C. (2006). A Hybrid Evolutionary Approach for Robust Active Suspension Design of Light Rail Vehicles. IEEE Transactions on Control Systems Technology, 14(4). https://doi.org/10.1109/TCST.2006.876639
Luo, X., Liu, H., Zhang, L., & Xun, J. (2021). A Model Predictive Control Based Inter-Station Driving Strategy for Virtual Coupling Trains in Railway System. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2021-September. https://doi.org/10.1109/ITSC48978.2021.9564895
Maxim, A., Pauca, O., & Caruntu, C. F. (2023). Distributed Model Predictive Control and Coalitional Control Strategies—Comparative Performance Analysis Using an Eight-Tank Process Case Study. Actuators, 12(7). https://doi.org/10.3390/act12070281
Nash, J. F. (1950). The Bargaining Problem. (2nd ed., Vol. 18). Econometrica.
Nie, B., & Tong, Y. (2022). A Survey of Applications of Model Predictive Control to Rail Traffic Regulation. Proceedings - 2022 Chinese Automation Congress, CAC 2022, 2022-January. https://doi.org/10.1109/CAC57257.2022.10054712
Okada, A. (2010). The Nash Bargaining Solution in General N-Person Cooperative Games. Journal of Economic Theory, 145(6). https://doi.org/10.1016/j.jet.2010.07.001
Omar, M., El-kassaby, M. M., & Abdelghaffar, W. (2017). A Universal Suspension Test Rig for Electrohydraulic Active and Passive Automotive Suspension System. Alexandria Engineering Journal, 56(4). https://doi.org/10.1016/j.aej.2017.01.024
Pannocchia, G. (2019). Distributed Model Predictive Control. In Encyclopedia of Systems and Control (Vol. 5). Springer.
Peters, H. J. (2013). Axiomatic Bargaining Game Theory ((V, Ed.; 9th ed.). Springer Science & Business Media.
Schwenzer, M., Ay, M., Bergs, T., & Abel, D. (2021). Review on Model Predictive Control: An Engineering Perspective. International Journal of Advanced Manufacturing Technology, 117(5–6). https://doi.org/10.1007/s00170-021-07682-3
Ulum, Z., Affaf, M., Salmah, S., & Suparwanto, A. (2017). Active Suspension Systems Design of a Light Rail Vehicle Using MPC with Preview Information Disturbance. Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation, ICA 2017. https://doi.org/10.1109/ICA.2017.8068406
Valencia, F., López, J. D., Patiño, J. A., & Espinosa, J. J. (2014). Bargaining Game Based Distributed MPC. Intelligent Systems, Control and Automation: Science and Engineering, 69. https://doi.org/10.1007/978-94-007-7006-5_2
Venkat, A. N., Rawlings, J. B., & Wright, S. J. (2007). Distributed Model Predictive Control of Large-Scale Systems. In Assessment and Future Directions of Nonlinear Model Predictive Control, 591–605. https://doi.org/10.1007/978-3-540-72699-9_50
Wang, G., Chen, C., & Yu, S. (2017). Robust Non-Fragile Finite-Frequency H∞ Static Output-Feedback Control for Active Suspension Systems. Mechanical Systems and Signal Processing, 91. https://doi.org/10.1016/j.ymssp.2016.12.039
Wang, Y., Zhu, S., Li, S., Yang, L., & Schutter, B. D. (2022). Hierarchical Model Predictive Control for On-Line High-Speed Railway Delay Management and Train Control in a Dynamic Operations Environment. IEEE Transactions on Control Systems Technology, 30(6). https://doi.org/10.1109/TCST.2022.3140805
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