无人自行车质量偏心识别的认知学习方法研究

doi:10.3901/JME.2024.23.140

机械工程学报 ›› 2024, Vol. 60 ›› Issue (23): 140-151.doi: 10.3901/JME.2024.23.140

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无人自行车质量偏心识别的认知学习方法研究

黄用华, 梁子彦, 庄未, 杨海洋

桂林电子科技大学机电工程学院桂林 541004

收稿日期:2023-12-25 修回日期:2024-03-05 出版日期:2024-12-05 发布日期:2025-01-23
作者简介:黄用华，男，1977年出生，博士，研究员，硕士研究生导师。主要研究方向为机器人动力学与运动控制技术。E-mail：huangyonghuaxj@sina.com;庄未(通信作者)，女，1977年出生，博士，研究员，硕士研究生导师。主要研究方向为机器人动力学及运动控制技术。E-mail：zhuangweibupt@sohu.com
基金资助:
国家自然科学基金(52165001，51865005，51765011)和广西自然科学基金(2018GXNSFAA281297，2018GXNSFAA281301)资助项目。

Cognitive Learning Methods for Mass Eccentricity Identification of Unmanned Bicycles

HUANG Yonghua, LIANG Ziyan, ZHUANG Wei, YANG Haiyang

School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004

Received:2023-12-25 Revised:2024-03-05 Online:2024-12-05 Published:2025-01-23

摘要/Abstract

摘要： 车体质量偏心对无人自行车航向轨迹跟踪性能有着重要影响，为实现车体质量偏心的在线识别，提出一种无人自行车质量偏心识别的认知学习方法。该方法考虑了车体质量偏心对直线轨迹的影响，并借助车体偏航轨迹和质量偏心之间的逆向映射关系，以车体航向角及航向角积分构造状态评价函数，引入反应式认知学习算法，构造正态分布的学习机制，对学习自动机的期望值和标准差进行迭代更新，并将迭代学习得到的结果补偿到平衡控制器中，最终实现无人自行车质量偏心的在线识别。数值仿真和样机实验结果表明：该方法在多种不同负载状态的场景下经过2～3轮迭代学习后(每轮学习时长为20 s)，车体质量偏心识别的绝对误差维持在0.01 rad内，相对误差不超过10%，并具备一定的抗干扰能力。研究结果可为后续无人自行车航向轨迹跟踪研究提供一定的理论支撑和技术参考。

关键词: 无人自行车, 质量偏心, 在线识别, 认知学习, 航向轨迹

Abstract: Body mass eccentricity plays an important role in course tracking performance of unmanned bicycle. In order to realize online recognition of body mass eccentricity, a cognitive learning method for mass eccentricity recognition of unmanned bicycle is proposed. In this method, the influence of vehicle mass eccentricity on the linear trajectory is considered, and the inverse mapping relationship between the vehicle body yaw trajectory and mass eccentricity is used to construct the state evaluation function based on the vehicle body heading angle and heading angle integral. A reactive cognitive learning algorithm is introduced to construct a normal distribution learning mechanism, and the expected value and standard deviation of the learning automaton are iteratively updated. The results of iterative learning are compensated into the balance controller to realize the online identification of mass eccentricity of the unmanned bicycle. The results of numerical simulation and prototype experiment show that after 2 to 3 rounds of iterative learning (each round learning time is 20 s) under different load conditions, the absolute error of body mass eccentricity identification is maintained within 0.01 rad, the relative error is less than 10%, and the method has certain anti-interference ability. The research results can provide some theoretical support and technical reference for the follow-up research on course and trajectory tracking of unmanned bicycles.

Key words: unmanned bicycle, mass eccentricity, online recognition, cognitive learning, heading trajectory

中图分类号:

TP242

黄用华, 梁子彦, 庄未, 杨海洋. 无人自行车质量偏心识别的认知学习方法研究[J]. 机械工程学报, 2024, 60(23): 140-151.

HUANG Yonghua, LIANG Ziyan, ZHUANG Wei, YANG Haiyang. Cognitive Learning Methods for Mass Eccentricity Identification of Unmanned Bicycles[J]. Journal of Mechanical Engineering, 2024, 60(23): 140-151.

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无人自行车质量偏心识别的认知学习方法研究

Cognitive Learning Methods for Mass Eccentricity Identification of Unmanned Bicycles

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