NDLI: Experimental implementation of an adaptive neural network tracking controller for motion control of step motors

Content Provider	IEEE Xplore Digital Library
Author	Rubaai, A. Castro, M.
Copyright Year	2005
Description	Author affiliation: Electr. & Comput. Eng. Dept., Howard Univ., Washington, DC, USA (Rubaai, A.; Castro, M.)
Abstract	Two distinct multilayer perception neural networks (NNs) are implemented via laboratory experiment to simultaneously identify and adaptively control the trajectory tracking of a hybrid step motor assumed to operate in a high-performance drives environment. A neural network identifier (NNI) which captures the nonlinear dynamics of the stepper motor drive system (SMDS) over any arbitrary time interval in its range of operation, and a neural network controller (NNC) to provide the necessary control actions as to achieve trajectory tracking of the rotor speed. The NNC is constructed as a feedback signal depending on the current state of the drive system supplies by the NNI and the reference trajectory we wish the outputs to follow. The two NNs are on-line trained using dynamic back-propagation algorithm. The composite structure is used as a speed controller for the SMDS. Performance of the composite controller is evaluated through a laboratory experiment. Experimental results show the effectiveness of this approach, and demonstrate the usefulness of the proposed controller in high performance drives.
Starting Page	693
Ending Page	699
File Size	296641
Page Count	7
File Format	PDF
ISBN	0780392086
ISSN	01972618
DOI	10.1109/IAS.2005.1518383
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Programmable control Adaptive systems Tracking Neural networks Nonlinear control systems Control systems Trajectory Motion control Adaptive control Multi-layer neural network
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Electrical and Electronic Engineering

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