Investigation of Control Behavior via Active Suspension System Considering Time-Delay and Variable Masses
محتوى المقالة الرئيسي
الملخص
The comfort and safety of drivers greatly depend on the design of the suspension system in road cars. The study presents modeling and control of an Active Suspension System (ASS) for a ¼ car with two degrees of freedom, considering unknown masses, time delay, and various types of road disturbances using the MATLAB/Simulink environment. The study examines the performance of the Model Predictive Control (MPC) scheme. Numerous controllers are utilized in literature, including Artificial Neural Networks (ANN), fuzzy logic controllers, Linear Quadratic Regulators (LQR), and Proportional Integral Derivative (PID) controllers. An MPC setup for the ASS model was presented in this paper. MPC is an optimal control strategy that predicts future output using a plant model. The performance of MPC was compared with that of an LQR control method. The results indicate The MPC can produce better road holding, and handling and ride quality are greatly improved by the LQR. The MPC control is 87% faster at eliminating oscillations compared to LQR which is 30%, The conclusion emphasizes that the effectiveness of the MPC scheme is far superior to LQR in all aspects. Despite certain challenges, such as reduced effectiveness under severe overload and increased energy consumption with larger loads.
تفاصيل المقالة
كيفية الاقتباس
تواريخ المنشور
الإستلام
النسخة النهائية
الموافقة
النشر الالكتروني
المراجع
Ahmed, A.A., 2021. Quarter car model optimization of active suspension system using fuzzy PID and linear quadratic regulator controllers. Global Journal of Engineering and Technology Advances, 6(3), pp.088-097. https://doi.org/10.1016/j.vacuum.2020.109766
Abut, T., and Salkim, E., 2023. Control of Quarter-Car Active suspension system based on optimized fuzzy linear quadratic regulator control method, Applied Sciences (Switzerland), 13(15). https://doi.org/10.3390/app13158802
Aktas, K.G., and Esen, I., 2020. State-Space modeling and active vibration control of smart flexible cantilever beam with the use of finite element method. Engineering Technology and Applied Science Research, 10(6), pp. 6549–6556. https://doi.org/10.48084/etasr.3949.
Al-Ashtar, W., 2023. Fuzzy logic control of active suspension system equipped with a hydraulic actuator. International Journal of Applied Mechanics and Engineering, 28(3), pp. 13–27. https://doi.org/10.59441/ijame/172895.
Aljarbouh, A., and Fayaz, M., 2020. Hybrid modeling and sliding mode control of semi-active suspension systems for both ride comfort and road-holding. Symmetry, 12(8). https://doi.org/10.3390/SYM12081286.
Alshamma, F. and Zainalaabdeen, S.A., 2017. The effect of cyclic bending loads on crack growth in pipes for inclined and transverse cracks with or without internal pulsing pressure. Journal of Engineering, 23(6), pp. 93–109. https://doi.org/10.31026/j.eng.2017.06.07.
Alzughaibi, A., Xue, Y. and Grosvenor, R., 2019. A new insight into modeling passive suspension real test rig system with consideration of nonlinear friction forces. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 233(8), pp. 2257–2266. https://doi.org/10.1177/0954407018764942.
Basargan, H., Mihaly, A. and Gaspar, 2023. Intelligent road-adaptive semi-active suspension and integrated cruise control. Machines, 11(2), pp. 1–18. https://doi.org/10.3390/machines11020204.
Bayar, K. and Khaneghah, F.S., 2020 Optimal sliding mode control method for active suspension control. IFAC-PapersOnLine, 53(2), pp. 14285–14291. https://doi.org/10.1016/j.ifacol.2020.12.1178.
Boulaaras, Z., Aouiche, A. and Chafaa, K., 2024. Optimal and robust control of active suspension system for a quarter car model. AIJR Abstracts, pp.39-41.
Bououden, S., Chadli, M. and Karimi, H., R., 2016. A robust predictive control design for nonlinear active suspension systems. Asian Journal of Control, 18(1), pp. 122–132. https://doi.org/10.1002/asjc.1180.
Chen, S., Preciado, V., Morari, M. and Matni, N., 2024. Robust model predictive control with polytopic model uncertainty through System Level Synthesis, Automatica, 162, pp. 1–26. https://doi.org/10.1016/j.automatica.2023.111431.
Das, R.R. and Kumar, V., 2019. Active suspension with model predictive control. Int. J. Eng. Adv. Technol., 8(6), pp.2826-2831. https://doi.org/10.35940/ijeat.F9038.088619.
Drdi, I., Hamza, A. and Ben Yahia, N., 2023. A new approach to controlling an active suspension system based on reinforcement learning. Advances in Mechanical Engineering, 15(6), pp. 1–21. https://doi.org/10.1177/16878132231180480.
Florea, A., Patrausanu, A., Cofaru, N. and Fiore, U., 2023. Superposition of populations in multi-objective evolutionary optimization of car suspensions. Engineering Applications of Artificial Intelligence, 126(PC), p. 107026. https://doi.org/10.1016/j.engappai.2023.107026.
Fu, Z.J. and Dong, X.Y., 2021. H∞ optimal control of vehicle active suspension systems in two-time scales. Automatika, 62(2), pp. 284–292. https://doi.org/10.1080/00051144.2021.1935610
Findeisen, R., 2006. Nonlinear model predictive control: A sampled-data feedback perspective (Doctoral dissertation, Stuttgart, Univ., Diss., 2004).
Ghoniem, M., Awad, T. and Mokhiamar, O., 2020. Control of a new low-cost semi-active vehicle suspension system using artificial neural networks. Alexandria Engineering Journal, 59(5), pp. 4013–4025. https://doi.org/10.1016/j.aej.2020.07.007.
Gu, B., Cong, J., Zhao., Chen, H. and Golshan M., F., 2022. A novel robust finite time control approach for a nonlinear disturbed quarter-vehicle suspension system with time delay actuation. Automatika, 63(4), pp. 627–639.
https://doi.org/10.1080/00051144.2022.2059205.
Han, S.Y., Dong, J., Zhou, J. and Chen, Y., 2022. Adaptive fuzzy PID control strategy for vehicle active suspension based on road evaluation. Electronics (Switzerland), 11(6). https://doi.org/10.3390/electronics11060921.
Huang, Y., Wu, J., Na, J., Han, S. and Gao, G., 2022. Unknown system dynamics estimator for active vehicle suspension control systems with time-varying delay IEEE Transactions on Cybernetics, 52(8), pp. 8504–8514. https://doi.org/10.1109/TCYB.2021.3063225.
Kim, J., Lee, T., Kim, G., J. and Yi, K., 2023. Model predictive control of a semi-active suspension with a shift delay compensation using preview road information. Control Engineering Practice, 137(December 2022), p. 105584. https://doi.org/10.1016/j.conengprac.2023.105584.
Kumar, S., Medhavi, A., Kumar, R. and Mall, P.K., 2022. Modeling analysis and PID controller implementation on suspension system for quarter vehicle model. Journal of Mechanical Engineering and Sciences. S. Kumar1, pp. 8905–8916. https://doi.org/10.15282/jmes.16.2.2022.08.0704.
Li, Q., Chen, Z., Song, H. and Dong, Y., 2024. Model predictive control for speed-dependent active suspension system with road preview information. pp. 1–17. https://doi.org/10.3390/s24072255.
Mahmoud, W. A. and Kadhim, D. J., 2023. A proposal algorithm to solve delay constraint least cost optimization problem. Journal of Engineering, 19(1), pp. 155–160. https://doi.org/10.31026/j.eng.2013.01.09.
Matrood, M.M. and Nassar, A.A., 2021. Vibration control of quarter car model using modified PID controller. Basrah Journal for Engineering Sciences, 6. https://doi.org/10.33971/bjes.21.2.1.
Montanez, G., Patino, D. and Mendez, D., 2015. Comparison of model predictive control techniques for active suspension. International Conference on Applied Electronics, 2015-Octob, pp. 157–160.
Nan, Y., Shao, S., Ren, G., Wu, K. and Cheng, Y., 2023. Simulation and experimental research on active suspension system with time-delay feedback control. IEEE Access, 11(August), pp. 88498–88510. https://doi.org/10.1109/ACCESS.2023.3305265.
Nguyen, D.N. and Nguyen, T.A., 2023. The dynamic model and control algorithm for the active suspension system. Mathematical Problems in Engineering. https://doi.org/10.1155/2023/2889435.
Nguyen, D.N. and Nguyen, T.A., 2022. Evaluate the stability of the vehicle when using the active suspension system with a hydraulic actuator controlled by the OSMC algorithm. Scientific Reports, 12(1), pp. 1–15. https://doi.org/10.1038/s41598-022-24069-w.
Nguyen, T. A., 2021. Advance the efficiency of an active suspension system by the sliding mode control algorithm with five state variables. IEEE Access, 9, pp. 164368–164378. https://doi.org/10.1109/ACCESS.2021.3134990.
Rodriguez-Guevara, D., Favela-Contreras, A., Beltran-Carbajal, F. and Sotelo, D., 2021. Active suspension control using an MPC-LQR-LPV controller with attraction sets and quadratic stability conditions. Mathematics, 9(20), pp. 1–17. https://doi.org/10.3390/math9202533.
Schwenzer, M., Ay, M., Bergs, T. and Abel, D., 2021. Review on model predictive control: an engineering perspective. International Journal of Advanced Manufacturing Technology, 117(5–6), pp. 1327–1349. https://doi.org/10.1007/s00170-021-07682-3.
Udwadia, F. E. and Phohomsiri, P., 2006. Active control of structures using time-delayed positive Feedback proportional control designs. Structural Control and Health Monitoring, 13(1), pp. 536–552. https://doi.org/10.1002/stc.128.
Wang, D., 2022. Adaptive control for the nonlinear suspension systems with stochastic disturbances and unknown time delay Systems Science and Control Engineering, 10(1), pp. 208–217. https://doi.org/10.1080/21642583.2021.1949403.
Wu, K. and Ren, C., 2020. Control and stability analysis of double time-delay active suspension based on particle swarm optimization. Shock and vibration, 2020. https://doi.org/10.1155/2020/8873701.
Xue, W., Li, K., Chen, Q. and Liu, G., 2019. Mixed FTS/H ∞ control of vehicle active suspensions with shock road disturbance. Vehicle System Dynamics, 57(6), pp. 841–854. https://doi.org/10.1080/00423114.2018.1490023.
Yan, G., Fang, M. and Xu, J., 2019. Analysis and experiment of time-delayed optimal control for vehicle suspension system. Journal of Sound and Vibration, 446(1239), pp. 144–158. https://doi.org/10.1016/j.jsv.2019.01.015.
Yang, H., Qin, Y., Xiang, C., Bai, W. and Xu., B., 2023. Active suspension robust preview control by considering actuator delay. IEEE Transactions on Intelligent Vehicles, 8(9), pp. 4263–4274. https://doi.org/10.1109/TIV.2023.3280599.
Yu, M., Evangelou, S. and Dini, D., 2023. Advances in active suspension systems for road vehicles. Engineering. 33, pp. 160-177. https://doi.org/10.1016/j.eng.2023.06.014.
Zhang, Y. and Chen, C., 2024. Robust predictive compensation control for lateral magnetorheological semi-active suspension of high-speed trains with time delay Automatika, 65(1), pp. 14–33. https://doi.org/10.1080/00051144.2023.2277492