Title

Longitudinal wheel slip regulation using nonlinear autoregressive-moving average (NARMA-L2) neural controller

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2019 7th International Conference on Robot Intelligence Technology and Applications, RiTA 2019

First Page

220

Last Page

225

Publication Date

11-1-2019

Abstract

In this study, the implementation of a nonlinear autoregressive-moving average model ( NARMA-L2) neural network controller to maximize the traction of tires during braking scenarios was explored. The proposed controller and system dynamics was done in Simulink. All in all, the neural network controller shows good stability and good response in following the reference trajectory or desired slip ratio. It has experienced the peak worst error of around 2%, its best performance was reached after 89 epochs and it can reach around 99.5% of the reference trajectory or desired slip ratio. Further research should focus on hardware implementation, integration with slip estimation techniques , and, better sets of training data to make the controller more adaptive to different environment and road surface characteristics.

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Digitial Object Identifier (DOI)

10.1109/RITAPP.2019.8932939

Disciplines

Electrical and Electronics

Keywords

Neural networks (Computer science); Tires—Traction

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