Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1441-1450.doi: 10.13229/j.cnki.jdxbgxb20180465

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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

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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

CLC Number: 

  • U461.4

Fig.1

Shift actuator schematic diagram"

Fig.2

Diagram of synchronizer friction torque"

Fig.3

Force diagram of gear ring when engaging"

Fig.4

Shift actuator adaptive intelligent control scheme"

Fig.5

Neural network structure"

Fig.6

Input and output value membership function"

Table 1

Proportional coefficient correction ? Δ K P fuzzy rules"

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

Table 2

Integral coefficient correction Δ K I fuzzy rules"

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

Fig.7

Target synchronizer trajectory curve"

Fig.8

Offline self?learning frame diagram"

Fig.9

Online self?learning frame diagram"

Table 3

Synchronizer simulation parameters"

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

Fig.10

AMEsim synchronizer simulation model"

Fig.11

AMEsim simulation curves"

Fig.12

Actuator segment simulation model"

Fig.13

Optimal calculation segment simulation model"

Fig.14

Comparison diagram of simulation results"

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