Abstract
Taking into account the nonlinearity of vehicle dynamics and the variations of vehicle parameters, the integrated control strategy for active front steering (AFS) and direct yaw control (DYC) that can maintain the performance and robustness is a key issue to be researched. Currently, the H ∞ method is widely applied to the integrated control of chassis dynamics, but it always sacrifices the performance in order to enhance the stability. The modified structure internal model robust control (MSIMC) obtained by modifying internal model control (IMC) structure is proposed for the integrated control of AFS and DYC to surmount the conflict between performance and robustness. Double lane change (DLC) simulation is developed to compare the performance and the stability of the MSIMC strategy, the PID controller based on the reference vehicle model and the H ∞ controller. Simulation results show that the PID controller may oscillate and go into instability in severe driving conditions because of large variations of tire parameters, the H ∞ controller sacrifices the performance in order to enhance the stability, and only the MSIMC controller can both ensure the robustness and the high performance of the integrated control of AFS and DYC.
Similar content being viewed by others
References
Yang X, Wang Z, Peng W. Coordinated control of AFS and DYC for vehicle handling and stability based on optimal guaranteed cost theory. Vehicle Syst Dyn, 2009, 47: 57–79
He J, Crolla D A, Levesley M C, et al. Coordination of active steering, driveline, and braking for integrated vehicle dynamics control. P I Mech Eng D-J Aut, 2006, 220: 1401–1420
Ding N, Taheri S. An adaptive integrated algorithm for active front steering and direct yaw moment control based on direct Lyapunov method. Vehicle Syst Dyn, 2010, 48: 1193–1213
Li H Z, Li L, He L, et al. PID plus fuzzy logic method for torque control in traction control system. Int J Automotive Tech, 2012, 13: 441–450
Nagai M, Shino M, Gao F. Study on integrated control of active front steer angle and direct yaw moment. JSAE Review, 2002, 23: 309–315
Acarman T. Nonlinear optimal integrated vehicle control using individual braking torque and steering angle with on-line control allocation by using state-dependent Riccati equation technique. Vehicle Syst Dyn, 2009, 47: 155–177
Ono E, Hattori Y, Muragishi Y, et al. Vehicle dynamics integrated control for four-wheel-distributed steering and four-wheel-distributed traction/braking systems. Vehicle Syst Dyn, 2006, 44: 139–151
Zong C F, Liang H Q, Tian C W, et al. Vehicle chassis coordinated control strategy based on model predictive control method. In: 2010 IEEE International Conference on Information and Automation (ICIA). IEEE, 2010. 655–659
Adireddy G, Shim T, Rhode D, et al. Combined wheel torque and steering control based on model predictive controller using a simplified tyre model. Int J Vehicle Des, 2014, 65: 31–51
Men J L, Wu B F, Chen J. Comparisons of 4WS and Brake-FAS based on IMC for vehicle stability control. J Mech Sci Tech, 2011, 25: 1265–1277
Zhu H, Li L, Jin M, et al. Real-time yaw rate prediction based on a non-linear model and feedback compensation for vehicle dynamics control. P I Mech Eng D-J Aut, 2013, 227: 1431–1445
Li L, Song J, Li H Z, et al. Comprehensive prediction method of road friction for vehicle dynamics control. P I Mech Eng D-J Aut, 2009, 223: 987–1002
He Z, Ji X. Nonlinear robust control of integrated vehicle dynamics. Vehicle Syst Dyn, 2012, 50: 247–280
Ono E, Takanami K, Iwama N, et al. Vehicle integrated control for steering and traction systems by μ-synthesis. Automatica, 1994, 30: 1639–1647
Wu J Y, Tang H J, Li S Y, et al. Integrated control system design of active front wheel steering and four wheel torque to improve vehicle handling and stability. Int J Automotive Tech, 2007, 8: 299–308
Doumiati M, Sename O, Dugard L, et al. Integrated vehicle dynamics control via coordination of active front steering and rear braking. Eur J Control, 2013, 19: 121–143
Mizutani A, Yubai K, Hirai J. A direct design from input output data of the youla parameter for compensating plant perturbation on GIMC structure. In: IECON’09. 35th Annual Conference of IEEE Industrial Electronics. IEEE, 2009. 3047–3052
Zhou K, Ren Z. A new controller architecture for high performance, robust, and fault-tolerant control. IEEE T Automat Contr, 2001, 46: 1613–1618
Rajamani R. Vehicle Dynamics and Control. New York: Springer, 2011. 201–235
Zhou K, Doyle JC, Glover K. Robust and Optimal Control. Prentice-Hall: Englewood cliffs, NJ, 1996
Vidyasagar M. Control System Synthesis: A Factorization Approach. Cambridge: The MIT Press, 1985
Guo K, Pan F, Cheng Y, et al. Driver model based on the preview optimal artificial neural network. In: AVEC Proceedings, 2002
Li H Z, Li L, Song J, et al. Comprehensive lateral driver model for critical maneuvering conditions. Int J Automotive Tech, 2011, 12: 679–686
Pacejka H B. Tire and Vehicle Dynamics. Warrendale, PA: Society of Automotive Engineers. Inc., 2002
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, J., Zhao, Y., Ji, X. et al. A modified structure internal model robust control method for the integration of active front steering and direct yaw moment control. Sci. China Technol. Sci. 58, 75–85 (2015). https://doi.org/10.1007/s11431-014-5680-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11431-014-5680-4