NDLI: Comparison of RBF and SHL Neural Network Based Adaptive Control

Content Provider	Springer Nature Link
Author	Anderson, Ryan T. Chowdhary, Girish Johnson, Eric N.
Copyright Year	2008
Abstract	Modern unmanned aerial vehicles (UAVs) are required to perform complex maneuvers while operating in increasingly uncertain environments. To meet these demands and model the system dynamics with a high degree of precision, a control system design known as neural network based model reference adaptive control (MRAC) is employed. There are currently two neural network architectures used by industry and academia as the adaptive element for MRAC; the radial basis function and single hidden layer neural network. While mathematical derivations can identify differences between the two neural networks, there have been no comparative analyses conducted on the performance characteristics for the flight controller to justify the selection of one neural network over the other. While the architecture of both neural networks contain similarities, there are several key distinctions which exhibit a noticeable impact on the control system’s overall performance. In this paper, a detailed comparison of the performance characteristics between both neural network based adaptive control approaches has been conducted in an application highly relevant to UAVs. The results and conclusions drawn from this paper will provide engineers with tangible justification for the selection of the better neural network adaptive element and thus a controller with better performance characteristics.
Starting Page	183
Ending Page	199
Page Count	17
File Format	PDF
ISSN	09210296
Journal	Journal of Intelligent & Robotic Systems
Volume Number	54
Issue Number	1-3
e-ISSN	15730409
Language	English
Publisher	Springer Netherlands
Publisher Date	2008-09-23
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Neural network SHL RBF MRAC Adaptive control Comparison Mechanical Engineering Artificial Intelligence (incl. Robotics) Electronic and Computer Engineering Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Control and Systems Engineering Mechanical Engineering Electrical and Electronic Engineering Software

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