Open Access
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 01001
Number of page(s) 9
Section Automation
Published online 09 August 2021
  1. J. Wang and L. Sun, “Design of semi-active air suspension system based on backstepping sliding mode control,” 2017 Chinese Automation Congress (CAC), 2017, pp. 4378–4382, doi: 10.1109/CAC.2017.8243550. [Google Scholar]
  2. E. Enders, P. Karle, G. Bonelli, D. Killian and G. Burkhard, “Modal Vertical Vehicle Dynamics Control for Semi-Active and Active Suspension Systems,” 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020, pp. 1–9, doi: 10.1109/EVER48776.2020.9242985. [Google Scholar]
  3. J. Wu, H. Zhou, Z. Liu and M. Gu, “Ride Comfort Optimization via Speed Planning and Preview Semi-Active Suspension Control for Autonomous Vehicles on Uneven Roads,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8343–8355, Aug.2020, doi: 10.1109/TVT.2020.2996681. [Google Scholar]
  4. Gowda, Dr.Dankan and Chakrasali, Sadashiva. (2014). Comparative Analysis of Passive and Semi-Active Suspension System for Quarter Car Model using PID Controller. [Google Scholar]
  5. Qazi, Abroon & Khan, Dr. Afzal & Khan, Muhammad & Noor, Sahar. (2013). A Parametric Study on Performance of Semi-active Suspension System with Variable Damping Coefficient Limit. AASRI Procedia. 4. 154–159. 10.1016/j.aasri.2013.10.024. [Google Scholar]
  6. Karthik Murali Madhavan Rathai. Experimental Implementation of Model Predictive Control Scheme for Control of Semi-active Suspension System. ScienceDirect, 2019. [Google Scholar]
  7. Wen, Jinyu & Liu, Ju & Long, Yao & Yao, Wei. (2016). Solution to short-term frequency response of wind farms by using energy storage systems. IET Renewable Power Generation.10. 669–678. 10.1049/iet-rpg.2015.0164 [Google Scholar]
  8. Abroon Jamal Qazi Afzal Khan, M. Tahir Khan, Sahar Noor. A Parametric Study on Performance of Semi-Active SuspensionSystem with Variable Damping Coefficient Limit,2014. [Google Scholar]
  9. Kazima, Sosthene and Josee, Musabyimana and Hui, Xiong. (2018). Fuzzy Logic Controller for Semi Active Suspension Based on MagnetoRheological Damper. International Journal of Automotive Engineering and Technologies. Volume 7. [Google Scholar]
  10. Hung, N.C., Nhung, N.T.B., Vu, L.T.Y., Khai, V.Q., Son, T.A., Thanh, T.Q., 2020. Apply a Fuzzy Algorithm to Control an Active Suspension in a Quarter Car by Matlab’s Simulink. AMM 902, 23–32. [Google Scholar]
  11. Abellan-Nebot, Jose. (2010). A review of artificial intelligent approaches applied to part accuracy prediction. International Journal of Machining and Machinability of Materials. 8. [Google Scholar]
  12. Yao, G. Z. et al. “MR damper and its application for semi-active control of vehicle suspension system.” Mechatronics 12 (2002): 963–973. [Google Scholar]
  13. Pang, Hui Liu, Fan Xu, Zeren. (2018). Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization. Neurocomputing. 306. 10.1016/j.neucom.2018.04.055. [Google Scholar]
  14. Durieux, Olivier Abramov, Sergey Mannan, Samjid. (2009). Semi-active suspension system simulation using Simulink. International Journal of Engineering Systems Modelling and Simulation. 1. 101–114. 10.1504/IJESMS.2009.027573. [Google Scholar]
  15. Juan C. Tudon-Martınez, Rubén Morales-Menéndez, Ricardo Ramirez-Mendoza, Olivier Sename, Luc Dugard. A Comparison between a Model-free and Model-based Controller of an Automotive Semi-active Suspension System: Independent Wheelstations. IFAC Joint conference SSSC - 5th Symposium on System Structure and Control, Feb 2013, Grenoble, France. pp.869–874, [Google Scholar]

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