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Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence ToolsIn this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.
Document ID
20010012157
Acquisition Source
Glenn Research Center
Document Type
Conference Paper
Authors
Rubaai, Ahmed
(Howard Univ. Washington, DC United States)
Ricketts, Daniel
(Howard Univ. Washington, DC United States)
Kotaru, Raj
(Howard Univ. Washington, DC United States)
Thomas, Robert
(Howard Univ. Washington, DC United States)
Noga, Donald F.
Kankam, Mark D.
Date Acquired
August 20, 2013
Publication Date
August 1, 2000
Publication Information
Publication: HBCUs/OMUs Research Conference Agenda and Abstracts
Subject Category
Electronics And Electrical Engineering
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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