Deep learning based road recognition for intelligent suspension systems

• Autorzy: Sun Jinwei , Cong Jingyu

Identyfikatory

DOI

10.15632/jtam-pl/140239

• Języki publikacji: EN

Abstrakty

EN

This paper presents a deep learning-based road recognition strategy for advanced suspension systems. A four-quarter suspension model with a magnetorheological (MR) damper is developed, and four typical road images with corresponding roughness data are collected. A back-propagation neural network based autoencoder and Convolutional Neural Networks (CNN) are utilized to form the deep learning structure. By utilizing the multi-object genetic algorithm, the optimal parameters can be obtained, and the control current can be adaptively adjusted. Simulation results indicate that the designed structure can identify the road type accurately, and the recognition-based control strategy can improve the suspension performance effectively.

Słowa kluczowe

EN

intelligent suspension system; deep learning; road recognition

• Wydawca: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
• Czasopismo: Journal of Theoretical and Applied Mechanics
• Rocznik: 2021
• Tom: Vol. 59 nr 3
• Strony: 493--508
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Sun Jinwei

• School of Vehicle Engineering, Xi’an Aeronautical University, Xi’an, China

• https://orcid.org/0000-0003-4971-2327

autor

Cong Jingyu

• School of Information and Communication Engineering, Hainan University, Haikou, China

Bibliografia

• 1. Bekhti M.A., Kobayashi Y., 2015, Prediction of vibrations as a measure of terrain travers ability in outdoor structured and natural environments, Proceedings of the 7th Pacific-Rim Symposium on Image and Video Technology, Auckland, New Zealand: Springer, 282-294.
• 2. Doumiati M., Victorino A., Charara A., Lechner D., 2011, Estimation of road profile for vehicle dynamics motion: experimental validation, Proceedings of the 2011 American Control Conference, San Francisco, CA, USA, 5237-5242.
• 3. Fauriat W., Mattrand C., Gayton N., Beakou A., Cembrzynski T., 2016, Estimation of road profile variability from measured vehicle responses, Vehicle System Dynamics, 54, 5, 585-605.
• 4. Gong, M., Chen, H., 2020, Variable damping control strategy of a semi-active suspension based on the actuator motion state, Journal of Low Frequency Noise, Vibration and Active Control, 39, 3, 787-802.
• 5. Hinton G.E., Zemel R.S., 1994, Autoencoders, minimum description length, and Helmholtz free energy, Advances in Neural Information Processing Systems, 6, 3-10.
• 6. Krzyżyński T., Maciejewski I., 2019, Computational method for shaping the vibro-isolation properties of semi-active and active systems, Archives of Mechanics, 71, 4-5, 291-313.
• 7. Kwok N.M., Ha Q.P., Nguyen Thi H., Li J., Samali B., 2006, A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization, Sensors and Actuators A. Physical, 132, 2, 441-451.
• 8. Li Z., Kalabic U.V., Kolmanovsky I.V., Atkins E., Lu J., Filev D., 2016, Simultaneous road profile estimation and anomaly detection with an input observer and a jump diffusion proces estimator, 2016 American Control Conference (ACC), IEEE, 1693-1698.
• 9. Liu W., Wang R., Ding R., Meng X., Yang L., 2020, On-line estimation of road profile in semi-active suspension based on unsprung mass acceleration, Mechanical Systems and Signal Processing, 135, 1, 106370.
• 10. Maciejewski I., Krzyżyński T., Pecolt S., Chamera S., 2019, Semi-active vibration control of horizontal seat suspension by using magneto-rheological damper, Journal of Theoretical and Applied Mechanics, 57, 2,411-420.
• 11. Morales A.L., Nieto A.J., Chicharro J.M., Pintado P., 2016, A semi-active vehicle suspension based on pneumatic springs and magnetorheological dampers, Journal of Vibration and Control, 24, 4, 808-821.
• 12. Peterson K., Ziglar J., Rybski P.E., 2008, Fast feature detection and stochastic parameter estimation of road shape using multiple lidar, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 612-619.
• 13. Qin Y., He C., Shao X., Du H., Xiang C., Dong M., 2018, Vibration mitigation for in-wheel switched reluctance motor driven electric vehicle with dynamic vibration absorbing structures, Journal of Sound and Vibration, 419, 249-267.
• 14. Qin Y., Langari R., Wang Z., Xiang C., Dong M., 2017, Road excitation classification for semi-active suspension system with deep neural networks, Journal of Intelligent and Fuzzy Systems, 33, 3, 1907-1918.
• 15. Qin Y., Reza L., Gu L., 2014, The use of vehicle dynamic response to estimate road profile input in time domain, ASME Dynamic System Control Conference (DSCC), San Antonio, TX, USA, DOI: 10.1115/DSCC2014-5978.
• 16. Qin Y., Wang Z., Xiang C., Hashemi E., Khajepour A., Huang Y., 2019, Speed independent road classification strategy based on vehicle response: Theory and experimental validation, Mechanical Systems and Signal Processing, 117, 653-666.
• 17. Qin Y., Wei C., Tang X., Zhang N., Dong M., Hu Ch., 2019, A novel nonlinear road profile classification approach for controllable suspension system: Simulation and experimental validation, Mechanical Systems and Signal Processing, 125, 6, 79-98.
• 18. Rath J.J., Veluvolu K.C., Defoort M., 2015, Simultaneous estimation of road profile and tire road friction for automotive vehicle, IEEE Transactions on Vehicular Technology, 64, 10, 4461-4471.
• 19. Sun J., Cong J.Y., Gu L., Dong M., 2019, Higher order sliding mode control for active suspension systems subject to actuator faults and disturbances, Proceedings of the Institution of Mechanical Engineers Part K-Journal of Multi-Body Dynamics, 233, 2, 280-298.
• 20. Viikari V.V., Varpula T., Kantanen M., 2009, Road-condition recognition using 24-GHz automotive radar, IEEE Transactions on Intelligent Transportation Systems, 10, 4, 639-648.
• 21. Zhang Z., Deng F., Huang Y., Bridgelall R., 2015, Road roughness evaluation using inpavement strain sensors, Smart Materials and Structures, 24, 11, 115029.

Uwagi

„Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).”