Gearshift Sensorless Control for Direct-Drive-Type AMT Based on Improved GA-BP Neural Network Algorithm
Joint Authors
Tan, Cao
Ge, Wenqing
Li, Bo
Li, Qiang
Li, Yujiao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-19
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The automated mechanical transmission (AMT) based on the electromagnetic linear driving device (EMLDD) has good potential for shift performance.
However, the direct-drive shifting mechanism based on the displacement sensor is difficult to meet the compactness of the structure and control robustness in complex environment.
Through analyzing the working principle of the electromagnetic linear driving device and features of sensorless control strategy, a new displacement prediction method based on the improved GA-BP neural network is proposed to replace the displacement sensor.
With current, voltage, and input shaft speed of the electromagnetic linear driving device as input, displacement prediction is obtained by the GA-BP neural network with improved selection factor.
Finally, the experiment validated the effectiveness of displacement prediction based on the improved GA-BP neural network of shift control.
The results showed that prediction accuracy of the improved GA-BP neural network was greater than 96% under all shift working conditions.
The average RMSE was reduced by 21.8%, absolute error of displacement prediction was controlled within ±0.5 mm, and average shift time was less than 0.18 s.
In this paper, the BP neural network is applied to complex linear displacement prediction, which has important application and popularization value.
American Psychological Association (APA)
Li, Bo& Ge, Wenqing& Li, Qiang& Li, Yujiao& Tan, Cao. 2020. Gearshift Sensorless Control for Direct-Drive-Type AMT Based on Improved GA-BP Neural Network Algorithm. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1196798
Modern Language Association (MLA)
Li, Bo…[et al.]. Gearshift Sensorless Control for Direct-Drive-Type AMT Based on Improved GA-BP Neural Network Algorithm. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1196798
American Medical Association (AMA)
Li, Bo& Ge, Wenqing& Li, Qiang& Li, Yujiao& Tan, Cao. Gearshift Sensorless Control for Direct-Drive-Type AMT Based on Improved GA-BP Neural Network Algorithm. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1196798
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196798