摘要
针对AMT换挡机械同步过程中换挡执行机构的控制,提出一种自适应智能控制策略。目的是通过双闭环控制(换挡电机电流和同步器位移)使同步器位移紧密跟随目标位移轨迹。考虑到换挡执行机构参数不确定性和动态干扰,加入补偿器控制以接近实际换挡电机特性,补偿器增益由神经网络经由自学习算法训练得到,自学习算法输入为换挡电机实际电流与神经网络预测电流之差。同时,利用模糊自适应控制对同步器位移闭环PI控制器参数进行调整。目标同步器位移轨迹由离线和在线自学习策略及时更新。仿真结果表明:相比于常规PID控制,本文策略跟随目标轨迹精度更高,稳定性更好,响应速度更快。
For the shift actuator control during AMT engagement,an adaptive intelligent control strategy was proposed. The goal is to make the synchronizer displacement follow the target displacement trajectory closely by dual closed-loop control(shift motor current and synchronizer displacement). Considering the shift actuator parameter uncertainty and dynamic disturbance,a compensator control was added to get access to the actual shift motor feature. The compensator gains were obtained by the self-learning algorithm in neuron network,and the input of the self-learning algorithm is the error between shift motor actual current and predicted current by neuron network. Meanwhile,fuzzy adaptive control was utilized to regulate the parameters of PI control in the closed-loop of synchronizer displacement. The target synchronizer displacement trajectory was updated in time by offline and online self-learning strategy. The simulation results verify that compared to normal PID control,the proposed strategy has higher accuracy,better stability and faster response in following target trajectory.
作者
鄢挺
杨林
陈亮
YAN Ting;YANG Lin;CHEN Liang(School of Mechanical Engineerings Shanghai Jiaotong University,Shanghai 200240,China;Shanghai 01 Power Technology Co.Ltd.,Shanghai 200240,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2019年第5期1441-1450,共10页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(51275291)
关键词
车辆工程
换挡执行机构
轨迹跟随
自适应
驾驶性
神经网络
模糊算法
vehicle engineering
shift actuator
trajectory tracking
self-adaptive
drivability
neuron network
fuzzy algorithm
