Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1405-1413.doi: 10.13229/j.cnki.jdxbgxb20200280

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Real-time torque tracking control based on BPNN online learning prediction model

Yan-hua DONG1(),Jing-wei LIU1,2,Jing-hua ZHAO1,3(),Liang LI1,Fang-xi XIE3   

  1. 1.College of Computer，Jilin Normal University，Siping 136000，China
    2.College of Information Technology，Changchun Finance College，Changchun 130028，China
    3.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2020-04-28 Online:2021-07-01 Published:2021-07-14
  • Contact: Jing-hua ZHAO E-mail:computerdyp@jlnu.edu.cn;zhaojh08@mails.jlu.edu.cn

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Abstract:

An online learning prediction model for fuel injection based on a large amount of experimental data is designed by utilizing back propagation neural network (BPNN), and a real-time torque tracking controller consisting of the BPNN predictive feedforward and a PID feedback is proposed. A simplified and discretized real-time model is adopted in the BPNN prediction model. The threshold value of the BPNN prediction model with parameter adaptability can be learned and updated online. Several experimental results show that the BPNN prediction model with online variable threshold proposed in this paper has higher prediction accuracy comparing with the model with fixed threshold, and that the torque tracking controller proposed in this paper can satisfy the real-time control requirement and has smaller error rate under the transient condition comparing with the PID controller.

Key words: back-propagation neural network （BPNN）, artificial intelligence, parameter adaptation, torque tracking control

CLC Number: 

  • TP399

Fig.1

BPNN prediction model structure of fuel injection"

Fig.2

Data set of speed， torque， and fuel injection"

Fig.3

Error sensitivity analysis for neurons number of hidden layer"

Fig.4

Linear regression analysis for the model"

Fig.5

Part of working conditions of FTP75"

Fig.6

Prediction effect of fuel injection BPNN model under FTP75 condition"

Fig.7

Tracking effect comparison of torque step target"

Fig.8

Zoom in of Fig.7"

Fig.9

Tracking effect comparison of torque sine target"

Fig.10

Zoom in of Fig.9"

Table 1

Error analysis of torque tracking"

误差指标阶跃扭矩目标工况正弦扭矩目标工况
MADRMSEMADRMSE
PID2.15184.60004.39725.3276
BPNN?PID1.68854.01382.62583.5417

Fig.11

VTBPNN-PID torque tracking control system"

Fig.12

Prediction effect of fuel injection VTBPNN model under FTP75 condition"

Fig.13

Tracking effect comparison of torque transient target"

Fig.14

Zoom in of Fig.13"

Fig.15

Error comparison of torque tracking"

Fig.16

Zoom in of Fig.15"

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