Enhancing Road Holding and Vehicle Comfort for an Active Suspension System utilizing Model Predictive Control and Deep Learning


  • Do Trong Tu Mechanical and Power Engineering Faculty, Electric Power University, Vietnam
Volume: 14 | Issue: 1 | Pages: 12931-12936 | February 2024 | https://doi.org/10.48084/etasr.6582


Active Suspension Systems (ASS) with control are gaining traction as researchers strive for optimal system performance. They are significant in diverse commercial vehicle applications, catering to user demands. This study employs the advanced Model Predictive Control (MPC) technique to enhance the smoothness and safety of a half-car model. The simulation results showed the prowess of MPC controllers under varied control force signal constraints, demonstrating superiority in curtailing vehicle chassis rotation angle and speed by up to 46.93% and 43.34%, respectively. The controller was compared with an artificial neural network controller utilizing only two state signals of the system, trained from MPC data, demonstrating high accuracy with R2 reaching 0.97024 and mean squared error at 7.3557×10-5. This study contributes to the refinement of ASS by focusing on practical implementation and performance enhancement.


active suspension system, model predictive control, machine learning, deep learning, artificial neural networks, ride comfort, road holding


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P. Gandhi, S. Adarsh, and K. I. Ramachandran, "Performance Analysis of Half Car Suspension Model with 4 DOF using PID, LQR, FUZZY and ANFIS Controllers," Procedia Computer Science, vol. 115, pp. 2–13, Jan. 2017.

M. G. Unguritu, T. C. Nichițelea, and D. Selișteanu, "Design and Performance Assessment of Adaptive Harmonic Control for a Half-Car Active Suspension System," Complexity, vol. 2022, Jul. 2022, Art. no. e3190520.

P. Swethamarai and P. Lakshmi, "Adaptive-Fuzzy Fractional Order PID Controller-Based Active Suspension for Vibration Control," IETE Journal of Research, vol. 68, no. 5, pp. 3487–3502, Sep. 2022.

M. Al-Ashmori and X. Wang, "A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems," Applied Sciences, vol. 10, no. 3, Jan. 2020, Art. no. 1148.

M. Gohari and M. Tahmasebi, "Active Off-Road Seat Suspension System Using Intelligent Active Force Control," Journal of Low Frequency Noise, Vibration and Active Control, vol. 34, no. 4, pp. 475–489, Dec. 2015.

V. Deshpande and Y. Zhang, "Multivariable Receding Horizon Control of Aircraft with Actuator Constraints," in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, Jul. 2020, pp. 1846–1851.

J. Kim, T. Lee, C. J. Kim, and K. Yi, "Model predictive control of a semi-active suspension with a shift delay compensation using preview road information," Control Engineering Practice, vol. 137, Aug. 2023, Art. no. 105584.

J. Narayan, S. A. Gorji, and M. M. Ektesabi, "Power reduction for an active suspension system in a quarter car model using MPC," in 2020 IEEE International Conference on Energy Internet (ICEI), Sydney, NSW, Australia, Dec. 2020, pp. 140–146.

D. Rodriguez-Guevara, A. Favela-Contreras, F. Beltran-Carbajal, D. Sotelo, and C. Sotelo, "Active Suspension Control Using an MPC-LQR-LPV Controller with Attraction Sets and Quadratic Stability Conditions," Mathematics, vol. 9, no. 20, Jan. 2021, Art. no. 2533.

A. S. Gad, "Preview Model Predictive Control Controller for Magnetorheological Damper of Semi-Active Suspension to Improve Both Ride and Handling," SAE International Journal of Vehicle Dynamics, Stability, and NVH, vol. 4, no. 3, pp. 305–326, Sep. 2020.

J. Narayan, S. A. Gorji, and M. M. Ektesabi, "Force Optimization for an Active Suspension System in a Quarter Car Model Using MPC," in Advances in Industrial Machines and Mechanisms, 2021, pp. 459–474.

V. N. Mai, D. S. Yoon, S. B. Choi, and G. W. Kim, "Explicit model predictive control of semi-active suspension systems with magneto-rheological dampers subject to input constraints," Journal of Intelligent Material Systems and Structures, vol. 31, no. 9, pp. 1157–1170, May 2020.

W. Jia, W. Zhang, F. Ma, and L. Wu, "Attitude Control of Vehicle Based on Series Active Suspensions," Actuators, vol. 12, no. 2, Feb. 2023, Art. no. 67.

Z. Houzhong, L. Jiasheng, Y. Chaochun, S. Xiaoqiang, and C. Yingfeng, "Application of explicit model predictive control to a vehicle semi-active suspension system," Journal of Low Frequency Noise, Vibration and Active Control, vol. 39, no. 3, pp. 772–786, Sep. 2020.

M. Papadimitrakis and A. Alexandridis, "Active vehicle suspension control using road preview model predictive control and radial basis function networks," Applied Soft Computing, vol. 120, May 2022, Art. no. 108646.

K. Chen, Z. Li, W. C. Tai, K. Wu, and Y. Wang, "MPC-based Vibration Control and Energy Harvesting Using an Electromagnetic Vibration Absorber With Inertia Nonlinearity," in 2020 American Control Conference (ACC), Denver, CO, USA, Jul. 2020, pp. 3071–3076.

J. O. Pedro, S. M. S. Nhlapo, and L. J. Mpanza, "Model Predictive Control of Half-Car Active Suspension Systems Using Particle Swarm Optimisation," IFAC-PapersOnLine, vol. 53, no. 2, pp. 14438–14443, Jan. 2020.

M. Brand et al., "A Parallel Quadratic Programming Algorithm for Model Predictive Control," IFAC Proceedings Volumes, vol. 44, no. 1, pp. 1031–1039, Jan. 2011.

D. Rodriguez-Guevara, A. Favela-Contreras, F. Beltran-Carbajal, C. Sotelo, and D. Sotelo, "A Differential Flatness-Based Model Predictive Control Strategy for a Nonlinear Quarter-Car Active Suspension System," Mathematics, vol. 11, no. 4, Jan. 2023, Art. no. 1067.

B. Zaparoli Cunha, C. Droz, A. M. Zine, S. Foulard, and M. Ichchou, "A review of machine learning methods applied to structural dynamics and vibroacoustic," Mechanical Systems and Signal Processing, vol. 200, Oct. 2023, Art. no. 110535.

J. Niresh, N. Archana, and G. Anand Raj, "Optimisation of Linear Passive Suspension System Using MOPSO and Design of Predictive Tool with Artificial Neural Network," Studies in Informatics and Control, vol. 28, no. 1, pp. 105–110, Mar. 2019.

M. P. Nagarkar, M. A. El-Gohary, Y. J. Bhalerao, G. J. Vikhe Patil, and R. N. Zaware Patil, "Artificial neural network predication and validation of optimum suspension parameters of a passive suspension system," SN Applied Sciences, vol. 1, no. 6, May 2019, Art. no. 569.

M. G. Pisino Giampiero Mastinu, Carlo Doniselli, Luca Guglielmetto, Enrico, "Optimal & Robust Design of a Road Vehicle Suspension System," in The Dynamics of Vehicles on Roads and on Tracks, Boca Raton, FL, USA: CRC Press, 2000.

F. Beltran-Carbajal, H. Yañez-Badillo, R. Tapia-Olvera, J. C. Rosas-Caro, C. Sotelo, and D. Sotelo, "Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems," Mathematics, vol. 11, no. 10, Jan. 2023, Art. no. 2272.

A. Bataineh, W. Batayneh, and M. Okour, "Intelligent Control Strategies for Three Degree of Freedom Active Suspension System," International Review of Automatic Control (IREACO), vol. 14, no. 1, Jan. 2021, Art. no. 17.

A. Hamza and N. Ben Yahia, "Heavy trucks with intelligent control of active suspension based on artificial neural networks," Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 235, no. 6, pp. 952–969, Jul. 2021.

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, vol. 59, no. 5, pp. 4013–4025, Oct. 2020.

Z. Ding, F. Zhao, Y. Qin, and C. Tan, "Adaptive neural network control for semi-active vehicle suspensions," Journal of Vibroengineering, vol. 19, no. 4, pp. 2654–2669, Jun. 2017.

J. Lin, H. Li, Y. Huang, Z. Huang, and Z. Luo, "Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System," IEEE Access, vol. 8, pp. 118673–118686, 2020.

G. N. Sahu, S. Singh, A. Singh, and M. Law, "Static and Dynamic Characterization and Control of a High-Performance Electro-Hydraulic Actuator," Actuators, vol. 9, no. 2, Jun. 2020, Art. no. 46.

G. Yang and J. Yao, "Multilayer neuroadaptive force control of electro-hydraulic load simulators with uncertainty rejection," Applied Soft Computing, vol. 130, Nov. 2022, Art. no. 109672.


How to Cite

D. T. Tu, “Enhancing Road Holding and Vehicle Comfort for an Active Suspension System utilizing Model Predictive Control and Deep Learning”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12931–12936, Feb. 2024.


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