Research on parameter identification method for the power transmission auxiliary system of tracked vehicles based on MOGA-LSTM

Zhengwei Yang; Ke Bao; Yantao Chu; Mengwei Li

doi:10.1117/12.3026221

1 April 2024 Research on parameter identification method for the power transmission auxiliary system of tracked vehicles based on MOGA-LSTM

Zhengwei Yang, Ke Bao, Yantao Chu, Mengwei Li

Author Affiliations +

Proceedings Volume 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023); 130822V (2024) https://doi.org/10.1117/12.3026221
Event: 2023 4th International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology (MEMAT 2023), 2023, Guilin, China

Abstract

This paper focuses on the controller in the power transmission auxiliary system of tracked vehicles. Through the process of simplifying the model, initial stiffness calculation, sample set establishment, network training, and parameter identification, a parameter identification model for the power transmission auxiliary system based on the MOGA-LSTM model is established. The analysis results show that the stiffness identification results of the installation structure of the MOGA-LSTM model are reliable. It provides a foundation for further research on the coupling vibration modeling of the dynamic transmission body and the auxiliary system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengwei Yang, Ke Bao, Yantao Chu, and Mengwei Li "Research on parameter identification method for the power transmission auxiliary system of tracked vehicles based on MOGA-LSTM", Proc. SPIE 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023), 130822V (1 April 2024); https://doi.org/10.1117/12.3026221

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available