ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2504.09134
31
0

Steady-State Drifting Equilibrium Analysis of Single-Track Two-Wheeled Robots for Controller Design

12 April 2025
Feilong Jing
Yang Deng
Boyi Wang
Xudong Zheng
Yifan Sun
Zhang Chen
Bin Liang
ArXivPDFHTML
Abstract

Drifting is an advanced driving technique where the wheeled robot's tire-ground interaction breaks the common non-holonomic pure rolling constraint. This allows high-maneuverability tasks like quick cornering, and steady-state drifting control enhances motion stability under lateral slip conditions. While drifting has been successfully achieved in four-wheeled robot systems, its application to single-track two-wheeled (STTW) robots, such as unmanned motorcycles or bicycles, has not been thoroughly studied. To bridge this gap, this paper extends the drifting equilibrium theory to STTW robots and reveals the mechanism behind the steady-state drifting maneuver. Notably, the counter-steering drifting technique used by skilled motorcyclists is explained through this theory. In addition, an analytical algorithm based on intrinsic geometry and kinematics relationships is proposed, reducing the computation time by four orders of magnitude while maintaining less than 6% error compared to numerical methods. Based on equilibrium analysis, a model predictive controller (MPC) is designed to achieve steady-state drifting and equilibrium points transition, with its effectiveness and robustness validated through simulations.

View on arXiv
@article{jing2025_2504.09134,
  title={ Steady-State Drifting Equilibrium Analysis of Single-Track Two-Wheeled Robots for Controller Design },
  author={ Feilong Jing and Yang Deng and Boyi Wang and Xudong Zheng and Yifan Sun and Zhang Chen and Bin Liang },
  journal={arXiv preprint arXiv:2504.09134},
  year={ 2025 }
}
Comments on this paper