In the realm of motorcycle extreme sports, wheel-ground friction significantly influences vehicle safety. This study presents a robust and precise method for identifying motorcycle tire friction by leveraging an advanced observation scheme. The proposed observer combines cascaded flatness-based observer and extended Kalman filter, employs a robust fixed-time exact differentiator to estimate the first- and second-order derivatives of the signal. This approach ensures adaptability to environmental parameter variations while effectively attenuating measurement noise and external disturbances. The robustness and accuracy of the proposed method are validated through simulations on the BikeSim platform, incorporating external shock disturbances and varying road conditions.

Single-Track two-wheeled (STTW) robots, due to their unique kinematic structure, offer superior dynamic performance, making them highly suitable for real-world applications. However, research on drift control for STTW robots is limited due to complex dynamics, narrow feasible region and difficulty in collecting demonstration data for adopting data-driven solutions. In this paper, we explore the feasibility and effectiveness of applying model-free deep reinforcement learning (DRL) to two drift tasks: steady-state drifting and drift trajectory tracking. We trained a reinforcement learning controller based on novel reward functions particularly designed for STTW drifting control, which does not require demonstration data. Furthermore, we propose a sampling and verification mechanism that excludes drift targets violating action constraints and thus improve training performance. Simulation results show that the DRL-based solution can achieve consistent and generalizable drifting behaviors for STTW robots, paving the way toward their practical deployment in dynamic environments.

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.

Keeping balance is one of the most important tasks of a motorcycle. The steady-state manifold is proposed in this paper to explore the inherent dynamics and the balance properties of a riderless motorcycle. The dynamic and kinematic characteristics are analyzed based on the manifold and are validated by simulation. Comparing to traditional control method, the usefulness of the manifold in control is shown through the design of a novel control strategy. Furthermore, based on the analysis and the simulation, the potential applications of the manifold for control and planning are summarized.