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

Content Provider	IEEE Xplore Digital Library
Author	Ching-Chih Tsai Hsu-Chih Huang Shui-Chun Lin
Copyright Year	1982
Abstract	This paper presents an adaptive control using radial-basis-function neural networks (RBFNNs) for a two-wheeled self-balancing 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.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	1420
Ending Page	1428
Page Count	9
File Size	896474
File Format	PDF
ISSN	02780046
Volume Number	57
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Programmable control Adaptive control Neural networks Motorcycles Motion control Control systems Mechatronics DC motors Mathematical model two-wheeled robot neural network radial basis function self-balancing
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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