基于仿人操控的无人摩托自适应定车控制OA

When an unmanned motorcycle is at zero velocity,the centrifugal effect becomes ineffective,making bal-ance control extremely challenging and lacking a robust and efficient control mechanism.Inspired by track stand skills of human riders,this paper proposes a human-inspired control method for the stationary balance of un-manned motorcycles.The principle of track stand is elucidated and applied to stationary balance of rear-wheel-driv-en unmanned motorcycles.By deriving the dynamic equations and conducting rider experiments,the mechanism of track stand is analyzed from both model-based and data-driven perspectives,revealing the principles by which riders maintain balance and adapt to varying terrains.Building upon this insight,a robust equilibrium-adaptation track stand control method is proposed.The method estimates the disturbed equilibrium point by using a disturb-ance observer,and adopts nonlinear model predictive control to achieve balance control under disturbances.The proposed method is shown to ensure zero steady-state tracking error,and its effectiveness is validated through ex-periments.Experimental results show that the proposed method improves tolerable disturbances to about 3.1 times and 2.4 times those of the conventional model predictive control in track stand tasks on lateral and longitudinal slopes,respectively,and reduces tracking errors by one order of magnitude in rear wheel position tracking tasks.