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Applications of adaptive neural network control to an unmanned airship

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Abstract

This paper represents an application of a neural network-based adaptive control to the Stability and Control Augmentation System(SCAS) of an unmanned airship whose maneuvers consist of diverse flight phases at low speeds. The neural network (NN) based adaptive SCAS is based on the inversion of a linear model of the airship at a nominal operating point and the adaptation of neural networks to unmodeled dynamics, parameter variations, and uncertain environments. This paper also presents an evaluation of the adaptive SCAS with flight test results and simulation results. In this evaluation, an outer-loop control is used. The autopilot is designed using a classical PID control algorithm for trajectory line tracking and altitude hold modes. Moreover, the adaptive SCAS approach showed superiority over the classical PID design approach in terms of the gain tuning process during a flight test.

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Authors and Affiliations

  1. Dodaam Systems Ltd., 461-31 Jeonmin-dong, Yuseong-gu, Daejeon, 306-230, Korea

    Chun-Han Hong

  2. Gyeongsang National University, 900 Gaja-dong, Jinju, 660-701, Korea

    Kwang-Chan Choi

  3. ReCAPT, Gyeongsang National University, 900 Gaja-dong, Jinju, 660-701, Korea

    Byoung-Soo Kim

Authors
  1. Chun-Han Hong
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  2. Kwang-Chan Choi
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  3. Byoung-Soo Kim
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Corresponding author

Correspondence to Byoung-Soo Kim.

Additional information

Recommended by Editorial Board member Sung-Kwun Oh under the direction of Editor-in-Chief Jin Bae Park. This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2009-(C1090-0904-0001)).

Chun-Han Hong received the B.E. at the School of Mechanical and Aerospace Engineering of Gyeongsang National University in 2002 and received the Master’s degree from the Aerospace Engineering Graduate School of Gyeongsang National University in 2006. He is now working in the Control Department of Dodaam Systems Ltd.

Kwang-Chan Choi received the B.E. at the School of Mechanical and Aerospace Engineering of Gyeongsang National University in 2006. He is now working toward his Master in Aerospace Engineering at the Graduate School of Gyeongsang National University.

Byoung-Soo Kim received the Ph.D. from the School of Aerospace Engineering at Georgia Tech in the U.S.A. in 1994. His research area is flight-control-system design for manned/unmanned aircraft and algorithm development for neural network-based adaptive control systems.

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Hong, CH., Choi, KC. & Kim, BS. Applications of adaptive neural network control to an unmanned airship. Int. J. Control Autom. Syst. 7, 911–917 (2009). https://doi.org/10.1007/s12555-009-0606-9

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  • DOI: https://doi.org/10.1007/s12555-009-0606-9

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