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Proceedings Paper

Adaptive time-delay neural control in large space structures
Author(s): Gary G. Yen; Victor M. Beazel
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Paper Abstract

The design of control algorithms for large space structures, possessing nonlinear dynamics which are often time-varying and likely ill-modeled, presents great challenges for all current methodologies. These limitations have led to the pursuit of a robust and fault tolerant structural controller. In the present paper, we propose the use of adaptive time-delay radial basis function (ATDRBF) networks as a learning controller in system identification and dynamic control of flexible structures. The ability of such neural networks to approximate arbitrary continuous functions offers an efficient means of vibration suppression and trajectory maneuvering for precision pointing capability. The ATDRBF network, which incorporates adaptive time-delays and interconnection weights, provides a feasible and flexible modeling technique to effectively capture all of the spatiotemporal interactions among the structure members. In the spirit of model reference adaptive control, we utilize the ATDRBF network as a building block to allow the neural network to function as a closed-loop controller. The controller regulate the dynamics of the nonlinear plant to follow a prespecified reference model asymptotically. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via specific examples.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169990
Show Author Affiliations
Gary G. Yen, Air Force Phillips Lab. (United States)
Victor M. Beazel, Air Force Phillips Lab. (United States)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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