Open Access
Mechanics & Industry
Volume 22, 2021
Article Number 17
Number of page(s) 11
Published online 09 March 2021
  1. M.C. Walz, Trends in the static stability factor of passenger cars, light trucks, and vans, (2005) [Google Scholar]
  2. F. Farroni, M. Russo, R. Russo, M. Terzo, F. Timpone, A combined use of phase plane and handling diagram method to study the influence of tyre and vehicle characteristics on stability, Vehicle System Dynamics 51 , 1265–1285 (2013) [Google Scholar]
  3. Y. Hisaoka, M. Yamamoto, A. Okada, Closed-loop analysis of vehicle behavior during braking in a turn, JSAE review 20 , 537–542 (1999) [Google Scholar]
  4. J. Lenasi, G. Danon, S. Žežclj, Lateral stability of a braking vehicle on the friction limit, Vehicle System Dynamics 29 , 711–716 (1998) [Google Scholar]
  5. Q. Qu, Y. Liu, On lateral dynamics of vehicles based on nonlinear characteristics of tires, Vehicle system dynamics 34 , 131–141 (2000) [Google Scholar]
  6. B. Olson, S. Shaw, G. Stépán, Stability and bifurcation of longitudinal vehicle braking, Nonlinear Dynamics 40 , 339–365 (2005) [Google Scholar]
  7. M. Ahmadian, Integrating electromechanical systems in commercial vehicles for improved handling, stability, and comfort, SAE International Journal of Commercial Vehicles 7 , 535–587 (2014) [Google Scholar]
  8. A.H. Kazemian, M. Fooladi, H. Darijani, Rollover index for the diagnosis of tripped and untripped rollovers, Latin American Journal of Solids and Structures 14 , 1979–1999 (2017) [Google Scholar]
  9. A.H. Kazemian, M. Fooladi, H. Darijani, Non-linear control of vehicle's rollover using sliding mode controller for new 8 degrees of freedom suspension model, International Journal of Heavy Vehicle Systems 26 , 707–726 (2019) [Google Scholar]
  10. E. Joa, K. Yi, Y. Hyun, Estimation of the tire slip angle under various road conditions without tire–road information for vehicle stability control, Control Engineering Practice 86 , 129–143 (2019) [Google Scholar]
  11. S. Cheng, L. Li, B. Yan, C. Liu, X. Wang, J. Fang, Simultaneous estimation of tire side-slip angle and lateral tire force for vehicle lateral stability control, Mechanical Systems and Signal Processing 132 , 168–182 (2019) [Google Scholar]
  12. M. Reiter, J. Wagner, Automated automotive tire inflation system–effect of tire pressure on vehicle handling, IFAC Proceedings Volumes 43 , 638–643 (2010) [Google Scholar]
  13. Y. Zhang, A. Khajepour, E. Hashemi, Y. Qin, Y. Huang, Reconfigurable model predictive control for articulated vehicle stability with experimental validation, IEEE Transactions on Transportation Electrification 6 , 308–317 (2020) [Google Scholar]
  14. V. Rezaei, A.M. Shafei, Dynamic analysis of flexible robotic manipulators constructed of functionally graded materials, Iranian Journal of Science and Technology, Transactions of Mechanical Engineering 43 , 327–342 (2019) [Google Scholar]
  15. A. Janot, P.M. Wensing, Sequential semidefinite optimization for physically and statistically consistent robot identification, Control Engineering Practice 107 , 104699 (2021) [Google Scholar]
  16. Y. Zhang, H. Liu, T. Ma, L. Hao, Z. Li, A comprehensive dynamic model for pneumatic artificial muscles considering different input frequencies and mechanical loads, Mechanical Systems and Signal Processing 148 , 107133 (2021) [Google Scholar]
  17. C.-Y. Lu, M.-C. Shih, Application of the pacejka magic formula tyre model on a study of a hydraulic anti-lock braking system for a light motorcycle, Vehicle System Dynamics 41 , 431–448 (2004) [Google Scholar]
  18. T.D. Gillespie, Fundamentals of vehicle dynamics, Society of automotive engineers Warrendale, PA, 1992 [Google Scholar]
  19. H. Pacejka, Tire and vehicle dynamics, Elsevier, 2005 [Google Scholar]
  20. E. Bakker, H.B. Pacejka, L. Lidner, A new tire model with an application in vehicle dynamics studies, SAE Transactions 98, 101–113 (1989) [Google Scholar]
  21. Y. Lee, S.H. Zak, Designing a genetic neural fuzzy antilock-brake-system controller, IEEE Transactions on Evolutionary Computation 6 , 198–211 (2002) [Google Scholar]
  22. J.R. Layne, K.M. Passino, S. Yurkovich, Fuzzy learning control for antiskid braking systems, IEEE Transactions on Control Systems Technology 1 , 122–129 (1993) [Google Scholar]
  23. D. Madau, F. Yuan, L. Davis, L. Feldkamp, Fuzzy logic anti-lock brake system for a limited range coefficient of friction surface, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, IEEE, 1993, pp. 883–888 [Google Scholar]
  24. S. Germann, M. Wurtenberger, A. Daiss, Monitoring of the friction coefficient between tyre and road surface, Proceedings of the third IEEE Conference on Control Applications, 1994, pp. 613–618 [Google Scholar]
  25. A. Mirzaei, M. Moallem, B. Mirzaeian, B. Fahimi, Design of an optimal fuzzy controller for antilock braking systems, 2005 IEEE Vehicle Power and Propulsion Conference, IEEE, 2005, pp. 823–828 [Google Scholar]
  26. H. Mirzaeinejad, M. Mirzaei, A new approach for modelling and control of two-wheel anti-lock brake systems, Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 225 , 179–192 (2011) [Google Scholar]
  27. E. Šabanovič, V. Žuraulis, O. Prentkovskis, V. Skrickij, Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation, Sensors 20 , 612 (2020) [Google Scholar]
  28. A. Aksjonov, V. Ricciardi, K. Augsburg, V. Vodovozov, E. Petlenkov, Hardware-in-the-loop test of an open loop fuzzy control method for decoupled electro-hydraulic antilock braking System, IEEE Transactions on Fuzzy Systems (2020) [Google Scholar]
  29. J. Zhang, S. Zhou, J. Zhao, Nonlinear robust wheel slip rate tracking control for autonomous vehicle with actuator dynamics, Advances in Mechanical Engineering 12 , 1687814020925222 (2020) [Google Scholar]
  30. P. Wellstead, N. Pettit, Analysis and redesign of an antilock brake system controller, IEE Proceedings-Control Theory and Applications 144 , 413–426 (1997) [Google Scholar]
  31. S. Jagannathan, M. Vandegrift, F.L. Lewis, Adaptive fuzzy logic control of discrete-time dynamical systems, Automatica 36 , 229–241 (2000) [Google Scholar]
  32. H. Li, Y. Wu, M. Chen, Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm, IEEE Transactions on Cybernetics 51, 1163–1174 (2020) [Google Scholar]
  33. K. Sakai, H. Shibasaki, R. Tanaka, T. Murakami, Y. Ishida, A design of a robust discrete-time controller, ISA Transactions 56 , 155–164 (2015) [PubMed] [Google Scholar]
  34. S. Zheng, H. Tang, Z. Han, Y. Zhang, Controller design for vehicle stability enhancement, Control Engineering Practice 14 , 1413–1421 (2006) [Google Scholar]
  35. E. Mamdani, Applications of fuzzy set theory to control systems: a survey, Fuzzy Automata and Decision Processes, 77–88 (1977) [Google Scholar]
  36. B.P. Graham, R.B. Newell, Fuzzy adaptive control of a first-order process, Fuzzy Sets and Systems 31 , 47–65 (1989) [Google Scholar]
  37. Z. Liu, S. Lu, R.-h. Du, A genetic-fuzzy control method for regenerative braking in electric vehicle, International Journal of Computing Science and Mathematics 11 , 263–277 (2020) [Google Scholar]
  38. G.F. Mauer, A fuzzy logic controller for an ABS braking system, IEEE Transactions on Fuzzy Systems 3 , 381–388 (1995) [Google Scholar]
  39. S. Latreche, S. Benaggoune, Robust wheel slip for vehicle anti-lock braking system with Fuzzy Sliding Mode Controller (FSMC), Engineering, Technology & Applied Science Research 10 , 6368–6373 (2020) [Google Scholar]
  40. W. Li, H. Du, W. Li, A modified extreme seeking-based adaptive fuzzy sliding mode control scheme for vehicle anti-lock braking, International Journal of Vehicle Autonomous Systems 15 , 1–25 (2020) [Google Scholar]
  41. C. Acosta-Lú, S.D. Gennaro, M.E. Sánchez-Morales, An adaptive controller applied to an anti-lock braking system laboratory, Dyna 83 , 69–77 (2016) [Google Scholar]
  42. M. Massaro, V. Cossalter, G. Cusimano, The effect of the inflation pressure on the tyre properties and the motorcycle stability, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 227 , 1480–1488 (2013) [Google Scholar]

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