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Study on the Handling Qualities Enhancement of Fixed-wing Aircraft Using Adaptive Neural Network

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Abstract

This paper intends to propose the integrated design framework of a model-following controller (MFC) combined with both an adaptive neural network (ANN) and a linear observer to enhance the handling qualities (HQ) of a fixed-wing aircraft. To achieve this objective, the HQ requirements (HQRs) applicable to a light sports aircraft and the controller structure have been defined. And, the controller design parameters are optimized to meet HQRs using the CONDUIT®. Thereby, the integrated design environment can determine the control gains and ANN’s tuning parameters in an optimum manner and the level of HQ achievable with the designed parameters can be automatically evaluated. The paper tries to clarify the effect of the ANN with the linear observer on the HQ enhancement. To achieve this, the control laws in the attitude command response types have been designed and their tracking performances are investigated with and without ANN elements to identify the improved HQ with the ANN. In addition, a series of comparative simulation studies on the effects of external disturbances on the attitude tracking performance are carried out to show the enhanced robustness with the proposed controller structure. The results show that the ANN control can dramatically improve the level of HQ and provide the favorable robustness to external disturbances. Therefore, the results of the present study can provide the efficient ways of the HQ enhancement by adopting the ANN in the conventional MFC structure and by designing the controller parameters with the integrated design processes of CONDUIT®.

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Authors and Affiliations

  1. Department of Aerospace Information Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul, 05029, Korea

    Do Hyeon Lee, Chang-Joo Kim, Sung Wook Hur & Seong Han Lee

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  1. Do Hyeon Lee
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  2. Chang-Joo Kim
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  3. Sung Wook Hur
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  4. Seong Han Lee
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Correspondence to Chang-Joo Kim.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Ding Zhai under the direction of Editor Guang-Hong Yang. This work was conducted at High-Speed Compound Unmanned Rotorcraft (HCUR) research laboratory with the support of Agency for Defense Development (ADD).

Do Hyeon Lee received his B.S. and M.S. degrees in Aerospace Engineering from University of Ulsan, Ulsan, Korea, in 2011 and 2013, respectively. Presently, he is a Ph.D. student with Department of Aerospace Information Engineering, Konkuk University. His research interests include aircraft flight dynamics, adaptive control, and trajectory generation.

Chang-Joo Kim is a Professor of the Department of Aerospace Engineering at Konkuk University, Korea. He received his Ph.D. degree in Aeronautical Engineering from Seoul National University in 1991. His research interests include nonlinear optimal control, helicopter flight mechanics, and helicopter system design.

Sung Wook Hur received his B.S. and M.S. degrees in Aerospace Information Engineering from Konkuk University, Seoul, Korea, in 2013 and 2015, respectively. Currently, he is a Ph.D. student with Department of Aerospace Information Engineering, Konkuk University. His research interests include nonlinear optimal control, spacecraft trajectory generation, and flight dynamics.

Seong Han Lee received his B.S. degree in Aerospace Engineering from University of Ulsan, Ulsan, Korea, in 2015 and M.S. degree in Aerospace Information Engineering, Konkuk University, Seoul, Korea, in 2017. Presently, he is a Ph.D. student with Department of Aerospace Information Engineering, Konkuk University. His research interests include aircraft flight dynamics, optimal control.

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Lee, D.H., Kim, CJ., Hur, S.W. et al. Study on the Handling Qualities Enhancement of Fixed-wing Aircraft Using Adaptive Neural Network. Int. J. Control Autom. Syst. 18, 1061–1074 (2020). https://doi.org/10.1007/s12555-018-9403-7

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  • DOI: https://doi.org/10.1007/s12555-018-9403-7

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