Adaptive Neural Network Control of a Self-Balancing Two-Wheeled Scooter

Abstract

This paper presents an adaptive control using radialbasis-function neural networks (RBFNNs) for a two-wheeled selfbalancing scooter. A mechatronic system structure of the scooter driven by two dc motors is briefly described, and its mathematical modeling incorporating two frictions between the wheels and the motion surface is derived. By decomposing the overall system into two subsystems (yaw motion and mobile inverted pendulum), one proposes two adaptive controllers using RBFNN to achieve self-balancing and yaw control. The performance and merit of the proposed adaptive controllers are exemplified by conducting several simulations and experiments on a two-wheeled self-balancing scooter.

DOI: 10.1109/TIE.2009.2039452

10 Figures and Tables

01020200920102011201220132014201520162017
Citations per Year

96 Citations

Semantic Scholar estimates that this publication has 96 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Tsai2010AdaptiveNN, title={Adaptive Neural Network Control of a Self-Balancing Two-Wheeled Scooter}, author={Ching-Chih Tsai and Hsu-Chih Huang and Shui-Chun Lin}, journal={IEEE Trans. Industrial Electronics}, year={2010}, volume={57}, pages={1420-1428} }