Active suspension for all-terrain vehicle with intelligent control using artificial neural networks

Anis Hamza; Issam Dridi; Kamel Bousnina; Noureddine Ben Yahia

doi:10.15282/jmes.18.1.2024.7.0782

Authors

Anis Hamza Mechanical, Production and Energy Laboratory (LMPE), National School of Engineering of Tunis (ENSIT), Avenue Taha Hussein, Montfleury, 1008 Tunis, University of Tunis, Tunisia. Phone: +216 71 496 066, Fax.: +216 1 391166 https://orcid.org/0000-0003-4283-5236
Issam Dridi Mechanical, Production and Energy Laboratory (LMPE), National School of Engineering of Tunis (ENSIT), Avenue Taha Hussein, Montfleury, 1008 Tunis, University of Tunis, Tunisia. Phone: +21671496066, Fax.: +21671391166
Kamel Bousnina Mechanical, Production and Energy Laboratory (LMPE), National School of Engineering of Tunis (ENSIT), Avenue Taha Hussein, Montfleury, 1008 Tunis, University of Tunis, Tunisia. Phone: +21671496066, Fax.: +21671391166
Noureddine Ben Yahia Mechanical, Production and Energy Laboratory (LMPE), National School of Engineering of Tunis (ENSIT), Avenue Taha Hussein, Montfleury, 1008 Tunis, University of Tunis, Tunisia. Phone: +21671496066, Fax.: +21671391166

DOI:

https://doi.org/10.15282/jmes.18.1.2024.7.0782

Keywords:

All-terrain Vehicle (ATV), Active Suspension, Vibrations, MATLAB, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN)

Abstract

The automotive industry focuses on developing advanced protection and stability control systems, particularly for suspension and steering, to enhance vehicle comfort, luxury, and safety. This research presents an intelligent controller for all-terrain vehicle (ATV) suspension systems based on Artificial Neural Network (ANN) technology. The controller leverages ANN capabilities to optimize system performance. MATLAB simulations were conducted to evaluate its effectiveness under various disturbances. A comparative analysis compared the ANN regulator, classic ANFIS regulator, and passive performance in different disturbance scenarios. The simulation results demonstrate exceptional performance of the ANN-based controller in displacement reduction, speed, acceleration, and robustness. The controller effectively mitigates disturbances, enhancing overall suspension system performance. These findings highlight the advantages of employing ANN technology in ATV suspensions. This research contributes to intelligent control systems advancement in the automotive industry, specifically in ATV suspensions. The demonstrated improvements have the potential to enhance passenger comfort, vehicle stability, and safety across terrains. By implementing ANN-based controllers, automotive manufacturers can optimize suspension systems, leading to improved vehicle performance. Several indicators, including RMSE, MRE, and R², were utilized to test and validate the models. The R² values for the three quality parameters ranged from 0.989 to 0.999, indicating a high level of consistency in the predictions made by the ANN, a "5-12-1" structure is employed. The results of this study add to the expanding body of knowledge endorsing the efficacy of ANNs in simulating and optimizing quarter-vehicle dynamics.

References

A. Hamza, S. Hayadi and E. H.-Taïeb, “The natural frequencies of waves in helical springs,” Comptes Rendus Mecanique, vol. 341, pp. 672-686, 2013.

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, pp. 952-969, 2021.

A. Hamza and N. Ben Yahia, “Artificial neural networks controller of active suspension for ambulance based on ISO standards,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 237, pp. 34-47, 2023.

T. R. M. Rao, G. V. Rao, K. S. Rao and A. Purushottam, “Analysis of passive and semi active controlled suspension systems for ride comfort in an omnibus passing over a speed bump, analysis of passive and semi active controlled suspension systems,” International Journal of Recent Research and Applied Studies, vol. 5, pp. 7-17, 2010.

K. Nitish and K. S. Sanjay, “Optimization of PID controller for quarter-car suspension system using genetic algorithm,” International Journal of Advanced Research in Computer Engineering and Technology, vol. 1, pp. 30-32, 2012.

A. Abdolvahab, S. G. Shafiei and B. Armin, “Simulation and analysis of passive and active suspension system using quarter car model for different road profile,” International Journal of Engineering Trends and Technology, vol. 3, pp. 636-644, 2012.

B. P. Amit, J. G. Jagrut, G. M. Nikunj and M. V. Nirav, “Development of active suspension system for car using fuzzy logic controller, PID and genetically optimize PID controller,” Journal of Information, Knowledge and Research In Electrical Engineering, vol. 2, pp. 347-351, 2013.

J. Q. Abroon, A. F. Umar, K. Afzal, K. M. Tahir, M. Farrukh and F. Ali, “Optimization of semi-active suspension system using particle swarm optimization algorithm,” in AASRI Conference on Intelligent Systems and Control, vol. 4, pp. 160-166, 2013.

G. Swati and J. Sheilza, “Vibration control of bus suspension system using PI and PID controller,” International Journal of Advances in Engineering Sciences, vol. 3, pp. 94-99, 2013.

M. Heidari, and H. Homaei, “Design a PID controller for suspension system by back propagation neural network,” Journal of Engineering, vol. 3, pp. 1-9, 2014.

A. S. Ahmed, A. S. Ali, N. M. Ghazaly and G. A. Jaber, “PID controller of active suspension system for a quarter car model,” International Journal of Advances in Engineering & Technology, vol. 8, pp. 890-909, 2015.

V. S. Dixit and S. C. Borse, “Semi-active suspension system design for quarter car model and its analysis with passive suspension model,” International Journal of Engineering Sciences & Research Technology, vol. 6, pp. 203-211, 2017.

D. Ramasastry, K. Ramana, N. M. Rao, M. P. Kumar and V. R. C. Reddy, “Evaluation of human exposure to vibrations using quarter car model with semi-active suspension,” International Journal of Vehicle Structures and Systems, vol. 10, pp. 268-272, 2018.

N. Vivekanandan and A. M. Fulambarkar, “Design and testing of fuzzy logic based controller for active suspension system of a quarter car model,” International Journal of Engineering and Advanced Technology, vol. 9, pp. 3522-3532, 2020.

A. Al Aela, J. P. Kenne and H. A. Mintsa, “A novel adaptive and nonlinear electrohydraulic active suspension control system with zero dynamic tire liftoff,” Machines, vol. 8, pp. 38-65, 2020.

M. Haemers, C. M. Ionescu, K. Stockman and S. Derammelaere, “Optimal hardware and control co-design applied to an active car suspension setup,” Machines, vol. 9, pp. 55-81, 2021.

M. Perrelli, F. Cosco, G. Carbone, B. Lenzo and D. Mundo, “On the benefits of using object-oriented programming for the objective evaluation of vehicle dynamic performance in concurrent simulations,” Machines, vol. 9, pp. 41-57, 2021.

D. Rodriguez-Guevara, A. Favela-Contreras, F. Beltran-Carbajal, C. Sotelo and D. Sotelo, “An MPC-LQR-LPV controller with quadratic stability conditions for a nonlinear half-car active suspension system with electro-hydraulic actuators,” Machines, vol. 10, pp. 137-155, 2022.

X. Chen, H. Song, S. Zhao and L. Xu, “Ride comfort investigation of semi-active seat suspension integrated with quarter car model,” Mechanics & Industry, vol. 23, pp. 18-35, 2022.

G. Luan, P. Liu, D. Ning, G. Liu and H. Du, “Semi-active vibration control of seat suspension equipped with a variable equivalent inertance-variable damping device,” Machines, vol. 11, pp. 284-305, 2023.

B. Zhang, M. Liu, K. Wang, B. Tan, Y. Deng, A. Qin and J. Liu, “Takagi–Sugeno fuzzy model-based control for semi-active cab suspension equipped with an electromagnetic damper and an air spring,” Machines, vol. 11, pp. 226-247, 2023.

H. Basargan, A. Mihály, P. Gáspár and O. Sename, “Intelligent road adaptive semi-active suspension and integrated cruise control,” Machines, vol. 11, pp. 204-222, 2023.

Q. Fu, J. Wu, C. Yu, T. Feng, N. Zhang and J. Zhang, “Linear quadratic optimal control with the finite state for suspension system,” Machines, vol. 11, pp. 127-141, 2023.

M. Haddar, M. Bouslema, S. Caglar Baslamisli, F. Chaari and M. Haddar, “Improving the ride comfort of full car model with a decoupling intelligent model free controller,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 237, pp. 3214-3231, 2022.

O. Mokhiamar, M. Ghoniem and T. Awad, “Implementation of fuzzy logic control on a new low cost semi-active vehicle shock absorber,” Journal of Mechanical Engineering and Sciences, vol. 16, pp. 8965-8975, 2022.

D. Nguyen and T. Nguyen, “Evaluate the stability of the vehicle when using the active suspension system with a hydraulic actuator controlled by the OSMC algorithm,” Scientific Reports, vol. 12, p. 19364, 2022.

N. Ngoc and T. A. Nguyen, “Enhancing the performance of the vehicle active suspension system by an Optimal Sliding Mode Control algorithm,” PLoS ONE, vol. 17, no. 12, p. e0278387, 2022.

L. Xiaorong, Z. Lijun, L. Dongchen and G. Dan, “Construction and simulation of a strategic HR decision model based on recurrent neural network,” Journal of Mathematics, vol. 2022, p. 5390176, 2022.

ISO 2631-1:1997, Mechanical vibration and shock – evaluation of human exposure to whole-body vibration, International Organization for Standardization, 2nd Ed., Geneva, Switzerland, 1997.

BSI 72/34562, Proposals for generalised road inputs to vehicles, British Standard Institution (BSI), London, 1972.

ISO 8608:1995, Mechanical vibration - Road surface profiles - Reporting of measured data, International Organization for Standardization, 1st Ed., Geneva, Switzerland, 1995.