电助力自行车模型参考自适应扭矩控制研究  

Model reference adaptive torque control of power-assist electric bicycle

作　　者：张懿[1] 陆腾飞 魏海峰[1] 李垣江[1] 徐松 ZHANG Yi;LU Teng-fei;WEI Hai-feng;LI Yuan-jiang;XU Song(School of Electronics and Information,Jiangsu University of Science and Technology,Zhenjiang 212000,China)

机构地区：[1]江苏科技大学电子信息学院,江苏镇江212000

出　　处：《电机与控制学报》2022年第5期115-124,共10页Electric Machines and Control

基　　金：国家自然科学基金(51977101);江苏省重点研发计划产业前瞻性与共性关键技术重点项目(BE2018007)。

摘　　要：针对当前电动助力车在遇到车辆载重变化和道路坡度变化时,电机的助力效果较差的问题,提出一种基于模型参考自适应的电动助力车扭矩控制算法,根据李雅普诺夫稳定性理论,使用李雅普诺夫第二法推导模型参考自适应控制系统的自适应控制率,自适应机构根据自适应控制律逐步改变调节器参数,使被控对象的输出或状态逐步趋向于参考模型的输出或状态,最终获得期望的可调系统输出,之后建立被控对象数学模型,通过仿真结果对比,验证了在载重改变和坡度改变情况下基于模型参考自适应控制算法的有效性,最后搭建实车路况实验平台,通过电动助力车实时速度、相电流对比证明自适应控制算法在电动助力车起步速度和上坡能力等方面都有显著提高。Aiming at the problem that the current electric-assisted bicycles have poor power-assisting effect when the vehicle load changes and road gradients change,a model-reference adaptive torque control algorithm for power-assist electric bicycle was proposed.According to the Lyapunov stability theory,the Lyapunov second method was used to derive the adaptive control rate of the model reference adaptive control system.The adaptive mechanism gradually changes the regulator parameters according to the adaptive control law,so that the output or state of the controlled object gradually tends to the output or state of the reference model.Finally,the desired output of the adjustable system was obtained,and then a mathematical model of the controlled object was established.Through the comparison of simulation results,effectiveness of the model-based adaptive control algorithm under the conditions of load changes and slope changes was verified.Finally,a real vehicle road condition experiment was built.On the platform,the comparison of the real-time speed and phase current of the electric bicycle proves that the adaptive control algorithm has significantly improved the starting speed and the uphill ability of the electric bicycle.

关 键 词：电动助力车 永磁同步电机 力矩传感器 助力扭矩 模型参考自适应 李雅普诺夫第二法 

分 类 号：TM351[电气工程—电机] TP273[自动化与计算机技术—检测技术与自动化装置]