Adaptive Track Stand Control for Unmanned Motorcycles Based on a Human-inspired Control Strategy
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摘要: 无人摩托车静止时离心力效应失效, 因而其平衡控制具有极大的挑战性, 缺乏一种鲁棒且高效的控制机制. 受摩托车手定车技巧启发, 提出一种基于仿人操控的无人摩托静止平衡控制方法, 阐明定车原理并基于其实现后驱无人摩托静止平衡控制. 通过建立动力学模型并结合骑手实验, 从模型和数据两个维度分析定车机理, 揭示骑手在定车中保持平衡与地形适应的原理, 在此基础上提出平衡点自适应鲁棒定车控制方法. 该方法利用扰动观测器估计的扰动计算受扰平衡点, 进而使用非线性模型预测控制实现扰动下的平衡控制. 本文证明了所提方法的无静差跟踪特性, 通过实验验证方法的有效性, 该方法在侧向/纵向斜面定车任务中将可容忍扰动分别提升至普通模型预测控制的约3.1倍和2.4倍, 在后轮位置跟踪任务中将跟踪误差降低一个数量级.Abstract: When an unmanned motorcycle is at zero velocity, the centrifugal effect becomes ineffective, making balance 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 unmanned motorcycles. The principle of track stand is elucidated and applied to stationary balance of rear-wheel-driven 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 disturbance 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 experiments. Experimental results show that the proposed method improves tolerable disturbances to 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.
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Key words:
- unmanned motorcycle /
- track stand /
- bio-inspired control /
- disturbance rejection control
1)2 1为下文表述简洁, 此处略有符号滥用, 下文中的状态$ {\boldsymbol{x}} $ 均是此扩展后的状态.2)1 2此处$ |{\boldsymbol{d}}(t)| $表示对向量或矩阵逐元素取绝对值, 符号$ \preceq $表示对向量或矩阵逐元素比较的小于等于. -
图 7 定车过程滚转角速度分别与踏板力矩和纵向加速度的互相关
Fig. 7 Cross-correlations of roll angular velocity with pedal torque and longitudinal acceleration during track stand
图 8 骑手定车控制增益递归最小二乘估计
Fig. 8 Recursive least square estimation of control gains for track stand control of a human rider
图 10 用于无人摩托车定车控制的EAMPC框图
Fig. 10 EAMPC block diagram for track stand control of unmanned motorcycle
图 12 斜面定车实验(上: 侧向斜面; 下: 纵向斜面)
Fig. 12 Track stand experiments on inclined planes (upper: laterally inclined plane; lower: longitudinally inclined plane)
图 13 侧向倾斜平面定车实验结果
Fig. 13 Experimental results of track stand on the laterally inclined plane
图 14 纵向倾斜平面定车实验结果
Fig. 14 Experimental results of track stand on the longitudinally inclined plane
图 15 最大可容许滚转/俯仰扰动及最大后轮位置跟踪误差
Fig. 15 Maximum tolerable roll/pitch disturbances and maximum rear wheel position tracking errors
图 17 后轮位置跟踪实验结果对比
Fig. 17 Comparison of rear wheel position tracking experimental results
图 19 后轮位置和转向角跟踪误差的RMSE和MAE
Fig. 19 RMSE and MAE of the rear wheel position tracking error and the steering tracking error
表 1 无人摩托车参数含义及取值
Table 1 Parameter definitions and values of the unmanned motorcycle
符号 含义 取值 $ a $ 质心后轮距离 0.14 $ {\rm{m}} $ $ b $ 轮轴距 0.408 $ {\rm{m}} $ $ c $ 拖曳距 0.024 $ {\rm{m}} $ $ h $ 质心高度 0.2 $ {\rm{m}} $ $ r $ 轮径 0.1 $ {\rm{m}} $ $ \lambda $ 前叉角 25° $ m $ 整体质量 7.4 $ {\rm{kg}} $ $ I_t $ 整体滚转惯量 0.356 $ {\rm{kg}}\cdot {\rm{m}}^2 $ $ I_r $ 车轮自转惯量 0.0039 $ {\rm{kg}}\cdot {\rm{m}}^2 $$ \varphi $ 滚转角 - $ \delta $ 转向角 - $ \delta_p $ 在水平面上的转向角投影 - $ P_1/P_2 $ 后轮/前轮触地点 - $ P_3 $ 转向轴和地面的交点 - $ P_4 $ 质心在轮轴线上的投影点 - $ \tau_r $ 后轮力矩 - $ \gamma $ 转向导致的偏航角偏移 - $ \omega $ 偏航角速度 - $ c_f $ 转向角非零时的拖曳距 - $ R_r $ 后轮转弯半径 - $ R_c $ 质心转弯半径 - $ v_r $ 后轮速度 - $ {\boldsymbol{a}}_{o} $ 坐标系$ o{\text{-}}xyz $的平动加速度 - 
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表 2 在不同地形上定车的平衡点
Table 2 Equilibriums of track stand on different terrains
实验组别 斜坡1 斜坡2 平地 平均滚转角(°) $ -0.688\;4 $ $ -2.180\;4 $ $ -0.302\;97 $
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