吉林大学学报(工学版) ›› 2019, Vol. 49 ›› Issue (5): 1441-1450.doi: 10.13229/j.cnki.jdxbgxb20180465

• • 上一篇    

AMT换挡执行机构自适应智能控制策略

鄢挺1(),杨林1(),陈亮2   

  1. 1. 上海交通大学 机械与动力工程学院,上海 200240
    2. 上海凌翼动力科技有限公司,上海 200240
  • 收稿日期:2018-05-10 出版日期:2019-09-01 发布日期:2019-09-11
  • 通讯作者: 杨林 E-mail:janus137@126.com;yanglin@sjtu.edu.cn
  • 作者简介:鄢挺(1989-),男,博士研究生.研究方向:新能源汽车控制.E-mail:janus137@126.com
  • 基金资助:
    国家自然科学基金项目(51275291)

AMT shift actuator adaptive intelligent control strategy

Ting YAN1(),Lin YANG1(),Liang CHEN2   

  1. 1. School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
    2. Shanghai 01 Power Technology Co. Ltd. , Shanghai 200240, China
  • Received:2018-05-10 Online:2019-09-01 Published:2019-09-11
  • Contact: Lin YANG E-mail:janus137@126.com;yanglin@sjtu.edu.cn

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。

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

Abstract:

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.

Key words: vehicle engineering, shift actuator, trajectory tracking, self-adaptive, drivability, neuron network, fuzzy algorithm

中图分类号: 

  • U461.4

图1

换挡执行机构原理示意图"

图2

同步器摩擦力矩示意图"

图3

挂入目标挡齿圈受力示意图"

图4

换挡执行机构自适应智能控制策略"

图5

神经网络结构"

图6

输入输出量隶属度函数"

表1

比例系数修正量 Δ K P 模糊规则"

E EC
NB NM NS Z PS PM PB
NB Z Z NM NM NM NB NB
NM PS Z NS NM NM NM NB
NS PS PS Z NS NS NM NM
Z PM PM PS Z NS NM NM
PS PM PM PM PS Z NS NS
PM PB PB PM PS PS Z NS
PB PB PB PM PM PS Z Z

表2

积分系数修正量 Δ K I 模糊规则"

E EC
NB NM NS Z PS PM PB
NB NB NB NM NM NS Z Z
NM NB NB NM NS NS Z Z
NS NB NM NS NS Z PS PS
Z NM NM NS Z PS PM PM
PS NM NS Z PS PS PM PB
PM Z Z PS PS PM PB PB
PB Z Z PS PM PM PB PB

图7

同步器目标轨迹曲线"

图8

离线自学习框架图"

图9

在线自学习框架图"

表3

同步器仿真模型参数"

参 数 数值
摩擦锥面半锥角 φ h /(°) 8
摩擦锥面平均半径 R h f /mm 26.5
锥面间摩擦系数 μ h 0.15
同步器输出轴转速 ω s /(r·min) 600
接合齿圈等效转动惯量 J c h /(kg·m2) 0.016
啮合套等效转动惯量 J s h /(kg·m2) 0.004

图10

同步器AMEsim建模图"

图11

AMEsim仿真曲线"

图12

执行器部分建模图"

图13

优化计算部分建模图"

图14

仿真结果对照示意图"

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