Research on data-driven control algorithm of automotive electronic control opening and closing system

Jianguo Liu; Zitong Chen; Fuwu Yan; Youhua Wu; Yunfei Sun; Dafeng Hu; Nuo Chen

doi:10.1117/12.2661027

16 December 2022 Research on data-driven control algorithm of automotive electronic control opening and closing system

Jianguo Liu, Zitong Chen, Fuwu Yan, Youhua Wu, Yunfei Sun, Dafeng Hu, Nuo Chen

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 125006G (2022) https://doi.org/10.1117/12.2661027
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

This article presents a data-driven control algorithm for automotive electronic control opening and closing system. By collecting the data of various mixed complex working conditions, the automatic decision-making model is formed on the basis of data learning, which can reduce the design and research of the algorithm itself and improve the intelligence degree of ECU. The untrained data can be collected at any time to learn to get the new model, and the original model can be quickly replaced. According to a large number of data collected in complex mixed conditions, the control method has better adaptability, simplifies the electronic control algorithm, and has certain generality in the field of automotive electronics. This article takes the anti-pinch control of power lift door as an example, proposes a data-driven BP neural network anti-pinch force classification method, selects good data for network training, and the recognition accuracy reaches 99.7%. Based on DSPACE, the rapid prototype bench test results show that the anti-pinch forces are in the range of 70N-100N, which verifies the feasibility of the control method.

Citation Download Citation

Jianguo Liu, Zitong Chen, Fuwu Yan, Youhua Wu, Yunfei Sun, Dafeng Hu, and Nuo Chen "Research on data-driven control algorithm of automotive electronic control opening and closing system", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 125006G (16 December 2022); https://doi.org/10.1117/12.2661027

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Control systems

Neural networks

Automotive electronic systems

Detection and tracking algorithms

Algorithm development

Evolutionary algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years