NMPC-based drift control for vehicle trajectory tracking considering rear wheel steering and driving

  • Taihu Zuo
  • , Litong Zhang
  • , Congzhi Liu*
  • , Meng Yang
  • , Yehui Shi
  • , Tongzhan Li
  • , Yue Ma
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Drift control is an extremely difficult problem, which poses a huge challenge due to the involvement of the vehicle model’s nonlinearity. Numerous studies have confirmed the existence of drift equilibrium points and validated that tracking these points enables straightforward drift control. However, a significant discrepancy remains between the actual and theoretical drift trajectories, primarily due to the inherent differences between the theoretical and actual vehicle models. Consequently, drift equilibrium point tracking-based methods are incapable of integrating drift control with trajectory tracking. Additionally, substantial research has been dedicated to rear-wheel steering. However, there has been no research that has explored the aspect of jointly using the rear wheel steering angle and slip ratio to achieve saturation of the rear tire force. This study aims to address the challenge of integrating transient drift control with trajectory tracking, enabling the vehicle to achieve trajectory tracking while in sustained drifting. To address the aforementioned challenges, a tire model incorporating the longitudinal slip ratio is employed and a straightforward longitudinal slip ratio controller is designed to decrease model discrepancies. For lateral control, an NMPC controller is designed using the expected transient drifting tire forces and the Fiala tire model with slip consideration. For longitudinal control, a straightforward controller incorporating the desired slip ratio and longitudinal speed is employed. Finally, a hardware-in-the-loop (HIL) experiment is conducted. The results demonstrate that the proposed NMPC controller enables the vehicle to achieve a drift state while maintaining trajectory tracking accuracy, and the controller exhibits robustness, safety, real-time performance, and practical application potential.

Keywords

  • autonomous vehicles
  • drift control
  • nonlinear model predictive control
  • rear-wheel steering
  • tire model
  • trajectory tracking

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