Research on engine speed regulation based on PID neural network in AMT gear shift

Jian Xin Peng; Hui Jin; Hui Yan Chen; Jin Ling Tao

Research on engine speed regulation based on PID neural network in AMT gear shift

Jian Xin Peng, Hui Jin, Hui Yan Chen^*, Jin Ling Tao

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

A new method for regulating engine speed during automated mechanical transmission (AMT) gear shift is proposed using PID neural network (PIDNN). With the help of engine platform-test, Dongfeng Cummis EQB235-20 diesel engine's throttle experimental model was established. Matlab software was used to train PIDNN to approach the characteristics of throttle experimental model. Engine speed control experiment was conducted with PIDNN, which was compiled into ECU. The results prove that, compared with ordinary PID, PIDNN shows better response speed, robustness and convergence, which improves the adaptive ability of vehicles.

Original language	English
Pages (from-to)	1179-1183
Number of pages	5
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	31
Issue number	10
Publication status	Published - Oct 2011

Keywords

Automated manual transmission
Engine speed regulation
Neural network
Proportion integration differentiation (PID)

Cite this

@article{23b85801a7cd4cdb8d76f2d555b2ff92,

title = "Research on engine speed regulation based on PID neural network in AMT gear shift",

abstract = "A new method for regulating engine speed during automated mechanical transmission (AMT) gear shift is proposed using PID neural network (PIDNN). With the help of engine platform-test, Dongfeng Cummis EQB235-20 diesel engine's throttle experimental model was established. Matlab software was used to train PIDNN to approach the characteristics of throttle experimental model. Engine speed control experiment was conducted with PIDNN, which was compiled into ECU. The results prove that, compared with ordinary PID, PIDNN shows better response speed, robustness and convergence, which improves the adaptive ability of vehicles.",

keywords = "Automated manual transmission, Engine speed regulation, Neural network, Proportion integration differentiation (PID)",

author = "Peng, \{Jian Xin\} and Hui Jin and Chen, \{Hui Yan\} and Tao, \{Jin Ling\}",

year = "2011",

month = oct,

language = "English",

volume = "31",

pages = "1179--1183",

journal = "Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology",

issn = "1001-0645",

publisher = "Beijing Institute of Technology",

number = "10",

}

TY - JOUR

T1 - Research on engine speed regulation based on PID neural network in AMT gear shift

AU - Peng, Jian Xin

AU - Jin, Hui

AU - Chen, Hui Yan

AU - Tao, Jin Ling

PY - 2011/10

Y1 - 2011/10

N2 - A new method for regulating engine speed during automated mechanical transmission (AMT) gear shift is proposed using PID neural network (PIDNN). With the help of engine platform-test, Dongfeng Cummis EQB235-20 diesel engine's throttle experimental model was established. Matlab software was used to train PIDNN to approach the characteristics of throttle experimental model. Engine speed control experiment was conducted with PIDNN, which was compiled into ECU. The results prove that, compared with ordinary PID, PIDNN shows better response speed, robustness and convergence, which improves the adaptive ability of vehicles.

AB - A new method for regulating engine speed during automated mechanical transmission (AMT) gear shift is proposed using PID neural network (PIDNN). With the help of engine platform-test, Dongfeng Cummis EQB235-20 diesel engine's throttle experimental model was established. Matlab software was used to train PIDNN to approach the characteristics of throttle experimental model. Engine speed control experiment was conducted with PIDNN, which was compiled into ECU. The results prove that, compared with ordinary PID, PIDNN shows better response speed, robustness and convergence, which improves the adaptive ability of vehicles.

KW - Automated manual transmission

KW - Engine speed regulation

KW - Neural network

KW - Proportion integration differentiation (PID)

UR - https://www.scopus.com/pages/publications/82055207309

M3 - Article

AN - SCOPUS:82055207309

SN - 1001-0645

VL - 31

SP - 1179

EP - 1183

JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

IS - 10

ER -

Research on engine speed regulation based on PID neural network in AMT gear shift

Abstract

Keywords

Other files and links

Fingerprint

Cite this