This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Ride Comfort Improvement with Preview Control Semi-active Suspension System Based on Supervised Deep Learning

Journal Article
10-05-01-0003
ISSN: 2380-2162, e-ISSN: 2380-2170
Published February 04, 2021 by SAE International in United States
Ride Comfort Improvement with Preview Control Semi-active Suspension System Based on Supervised Deep Learning
Sector:
Citation: Zhu, Y., Bian, X., Su, L., Gu, C. et al., "Ride Comfort Improvement with Preview Control Semi-active Suspension System Based on Supervised Deep Learning," SAE Int. J. Veh. Dyn., Stab., and NVH 5(1):31-44, 2021, https://doi.org/10.4271/10-05-01-0003.
Language: English

References

  1. Huang , K. , Hu , B. , Chen , L. , Alois , K. et al. ADAS on COTS with OpenCL: A Case Study with Lane Detection IEEE Transactions on Computers 67 4 559 565 2018 https://doi.org/10.1109/TC.2017.2759203
  2. Panou , M.C. Intelligent Personalized ADAS Warnings European Transport Research Review 10 2 1 12 2018 https://doi.org/10.1186/s12544-018-0324-6
  3. Borrego-Carazo , J. , Castells-Rufas , D. , Biempica , E. , and Carrabina , J. Resource-Constrained Machine Learning for ADAS: A Systematic Review IEEE Access 8 40573 40598 2020 https://doi.org/10.1109/ACCESS.2020.2976513
  4. Zhang , H.L. , Liu , J. , Wang , E.R. , Rakheja , S. et al. Nonlinear Dynamic Analysis of a Skyhook-Based Semi-Active Suspension System with Magneto-Rheological Damper IEEE Trans. Veh. Technol. 67 11 10446 10456 2018 https://doi.org/10.1109/TVT.2018.2870325
  5. Kaldas , M. , Soliman , A. , Abdallah , S. , and Amien , F. Robustness Analysis of the Model Reference Control for Active Suspension System SAE Int. J. Veh. Dyn., Stab., and NVH 4 2 165 177 2020 https://doi.org/10.4271/10-04-02-0012
  6. Pang , H. , Chen , J.N. , and Liu , K. Adaptive Backstepping Tracking Control for Vehicle Semi-Active Suspension System with Magnetorheological Damper Acta Armamentarii 38 7 1430 1442 2017 https://doi.org/10.3969/j.issn.1000-1093.2017.07.023
  7. Pang , H. , Zhang , X. , Yang , J.J. , and Shang , Y.T. Adaptive Backstepping-Based Control Design for Uncertain Nonlinear Active Suspension System with Input Delay Int J Robust Nonlinear Control 29 16 5781 5800 2019 https://doi.org/10.1002/rnc.4695
  8. Yuvapriya , T. , Lakshmi , P. , and Rajendiran , S. Vibration Suppression in Full Car Active Suspension System Using Fractional Order Sliding Mode Controller Journal of the Brazilian Society of Mechanical Sciences and Engineering 40 4 217 228 2018 https://doi.org/10.1007/s40430-018-1138-0
  9. Zhou , C. , Liu , X.H. , Chen , W. , Xu , F.X. et al. Optimal Sliding Mode Control for an Active Suspension System Based on a Genetic Algorithm Algorithms 11 12 205 221 2018 https://doi.org/10.3390/a11120205
  10. Yang , M.L. , Peng , C. , Li , G.L. , Wang , Y.L. et al. Event-Triggered H∞ Control for Active Semi-Vehicle Suspension System with Communication Constraints Information Sciences 486 101 113 2019 https://doi.org/10.1016/j.ins.2019.02.047
  11. Kumar , V. , Rana , K.P.S. , Kumar , J. , and Mishra , P. Self-Tuned Robust Fractional Order Fuzzy PID Controller for Uncertain and Nonlinear Active Suspension System Neural Comput. Appl. 30 6 1827 1843 2018 https://doi.org/10.1007/s00521-016-2774-x
  12. Nguyen , V. , Jiao , R. , and Zhang , J. Control Performance of Damping and Air Spring of Heavy Truck Air Suspension System with Optimal Fuzzy Control SAE Int. J. Veh. Dyn., Stab., and NVH 4 2 179 194 2020 https://doi.org/10.4271/10-04-02-0013
  13. Kararsiz , G. , Paksoy , M. , Metin , M. , and Basturk , H.I. An Adaptive Control Approach for Semi-Active Suspension Systems under Unknown Road Disturbance Input Using Hardware-in-the-Loop Simulation Transactions of the Institute of Measurement and Control 2018 https://doi.org/10.1177/0142331219895935
  14. Sun , W.C. , Gao , H.J. , and Yao , B. Adaptive Robust Vibration Control of Full-Car Active Suspensions with Electrohydraulic Actuators IEEE Trans. Control Syst. Technol. 21 6 2417 2422 2013 https://doi.org/10.1109/TCST.2012.2237174
  15. Wang , D.Z. , Zhao , D.X. , Gong , M.D. , and Yang , B. Research on Robust Model Predictive Control for Electro-Hydraulic Servo Active Suspension Systems IEEE Access 6 3231 3240 2018 https://doi.org/10.1109/ACCESS.2017.2787663
  16. Wang , Z.F. , Xu , S.J. , Li , F. , Wang , X.Y. et al. Integrated Model Predictive Control and Adaptive Unscented Kalman Filter for Semi-Active Suspension System Based on Road Classification SAE Technical Paper 2020-01-0999 2020 https://doi.org/10.4271/2020-01-0999
  17. Liu , Y.J. , Zeng , Q. , Tong , S.C. , Chen , C.L.P. et al. Adaptive Neural Network Control for Active Suspension Systems with Time-Varying Vertical Displacement and Speed Constraints IEEE Transactions on Industrial Electronics 66 12 9458 9466 2019 https://doi.org/10.1109/TIE.2019.2893847
  18. Soliman , A. and Crolla , D. Preview Control for a Semi-Active Suspension System Int. J. Vehicle Design 17 4 384 396 1996
  19. Van Der Aa , M.A.H. , Muijderman , J.H.E.A. , and Veldpaus , F.E. Constrained Optimal Control of Semi-Active Suspension Systems with Preview Vehicle System Dynamics 28 4-5 307 323 1997
  20. Gordon , T. , and Sharp , R. On Improving the Performance of Automotive Semi-Active Suspension Systems Through Road Preview Journal of Control Sound and Vibration 217 1 163 182 1998
  21. Calıskan , K. , Henze , R. , and Kucukay , F. Potential of Road Preview for Suspension Control under Transient Road Inputs IFAC 9 3 117 122 2016 https://doi.org/10.1016/j.ifacol.2016.07.020
  22. Bei , S.Y. , Yuan , C.Y. , Chen , L. , and Zhang , L.C. Fuzzy Neural Network Control of Semi-Active Suspension Based on Wheelbase Preview Automotive Engineering 32 12 1067 1070 2010
  23. Qin , M. , Dong , B. , and Ma , T.F. A Study of Active Suspension Using Interaxle Preview Automotive Engineering 26 5 2004
  24. Zou , Q. , Jiang , H.W. , Dai , Q.Y. , Yue , Y.H. et al. Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks IEEE Transactions on Vehicular Technology 69 1 41 54 2020 https://doi.org/10.1109/TVT.2019.2949603
  25. Tian , Y. , Judith , G. , Wang , X. , Chen , W.G. et al. Lane Marking Detection via Deep Convolutional Neural Network Neurocomputing 280 46 55 2018 https://doi.org/10.1016/j.neucom.2017.09.098
  26. Karaduman , M. and Eren , H. Deep Learning Based Traffic Direction Sign Detection and Determining Driving Style 2017 International Conference on Computer Science and Engineering (UBMK) 2017 https://doi.org/10.1109/UBMK.2017.8093453
  27. Ribeiro , D. , Mateus , A. , Nascimento , J. , and Miraldo , P. A Real-time Deep Learning Pedestrian Detector for Robot Navigation 2016
  28. Konoiko , A. , Kadhem , A. , Saiful , I. , Ghorbanian , N. et al. Deep Learning Framework for Controlling an Active Suspension System Journal of Vibration and Control 25 17 2316 2329 2019 https://doi.org/10.1177/1077546319853070
  29. Ren , H.B. , Chen , S.Z. , and Feng , Z.Z. The Skyhook On-Off Control of Semi-Active Suspension with Magneto-Rheological Damper Transactions of Beijing Institute of Technology 34 2 148 152 2014 https://doi.org/10.15918/j.tbit1001-0645.2014.02.008
  30. Zhou , C. and Wen , G. Hydraulic-Electrical Energy Regenerative Semi-Active Hydro-Pneumatic Suspension System Based on a Modified Skyhook Damping Control Algorithm Journal of Vibration and Shock 37 14 168 207 2018 https://doi.org/10.13465/j.cnki.Jvs.2018.14.023
  31. Li , K. and Wang , Y. Artificial Intelligence Beijing Cultural Development Press 2017 40 49
  32. Zhang , Y. Data Processing and Best Practices in the AI Era of the Beauty of Deep Learning Beijing Electronic Industry Press 2018 414 434
  33. Redmon , J. and Farhadia , A. YOLO9000: Better, Faster, Stronger 2016

Cited By