提出单轨两轮机器人稳态漂移的解析方法，提升高机动性控制稳定性。
Steady-State Drifting Equilibrium Analysis of Single-Track Two-Wheeled Robots for Controller Design
- 基于几何与运动学关系构建解析算法，计算速度提升10000倍
- 理论揭示高手骑手反向打把漂移的力学机制，误差低于6%
- 设计模型预测控制器，实现漂移状态与平衡点间平滑切换
漂移是一种高级驾驶技术，通过打破轮式机器人轮胎与地面间的非完整纯滚动约束，实现快速转向等高机动任务。稳态漂移控制可增强侧滑条件下的运动稳定性。尽管四轮机器人已成功实现漂移，但对单轨两轮（STTW）机器人（如无人摩托车或自行车）的研究仍不充分。本文将漂移平衡理论拓展至STTW机器人，揭示了稳态漂移操作的机理。特别地，该理论解释了熟练骑手使用的反向打把漂移技术。此外，提出一种基于内在几何与运动学关系的解析算法，计算耗时降低四个数量级，且与数值方法相比误差小于6%。基于平衡分析，设计了模型预测控制器（MPC），实现稳态漂移及平衡点过渡，仿真验证了其有效性和鲁棒性。
原文摘要 · Abstract (English)
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.
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