Gearshift Sensorless Control for Direct-Drive-Type AMT Based on Improved GA-BP Neural Network Algorithm

Bo Li, Wenqing Ge, Qiang Li, Yujiao Li and Cao Tan

Mathematical Problems in Engineering, 2020, vol. 2020, 1-13

Abstract:

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6456410

DOI: 10.1155/2020/6456410

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