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Direct data–driven strategy for closed-loop aircraft flutter test

Wang Jianhong (College of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China)
Ricardo A. Ramirez-Mendoza (College of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China)

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Article publication date: 12 December 2022

5

Abstract

Purpose

This new paper aims to extend the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. After feeding back the output signal to the input and introducing one feedback controller in the adding feedback loop, two parts, i.e. unknown aircraft flutter model and unknown feedback controller, exist in this closed-loop aircraft flutter system, simultaneously, whose input and output are all corrupted with external noise. Because of the relations between aircraft flutter model parameters and the unknown aircraft model, direct data–driven identification is proposed to identify that aircraft flutter model, then some identification algorithms and their statistical analysis are given through the authors’ own derivations. As the feedback controller can suppress the aircraft flutter or guarantee the flutter response converge to one desired constant value, the direct data–driven control is applied to design that feedback controller only through the observed data sequence directly. Numerical simulation results have demonstrated the efficiency of the proposed direct data–driven strategy. Generally, during our new information age, direct data–driven strategy is widely applied around our living life.

Design/methodology/approach

First, consider one more complex closed loop stochastic aircraft flutter model, whose input–output are all corrupted with external noise. Second, for the identification problem of closed-loop aircraft flutter model parameters, new identification algorithm and some considerations are given to the corresponding direct data–driven identification. Third, to design that feedback controller, existing in that closed-loop aircraft flutter model, direct data–driven control is proposed to design the feedback controller, which suppresses the flutter response actively.

Findings

A novel direct data–driven strategy is proposed to achieve the dual missions, i.e. identification and control for closed-loop aircraft flutter test. First, direct data–driven identification is applied to identify that unknown aircraft flutter model being related with aircraft flutter model parameters identification. Second, direct data–driven control is proposed to design that feedback controller.

Originality/value

To the best of the authors’ knowledge, this new paper extends the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. Consider the identification problem of aircraft flutter model parameters within the presented closed loop environment, direct data–driven identification algorithm is proposed to achieve the identification goal. Direct data–driven control is proposed to design the feedback controller, i.e. only using the observed data to design the feedback controller.

Keywords

Acknowledgements

This work is partially supported by Jiangxi Provinical National Science Foundation (No. 20202BAL202009). The authors are grateful to Professor Eduardo F Camacho for his warm invitation in his control lab at the University of Seville, Seville, Spain.

Conflict of interest: The authors declare that there is no conflict of interests regarding the publication of this paper.

Data availability: The data used to support the findings of this study are available from the corresponding author upon request.

Citation

Jianhong, W. and Ramirez-Mendoza, R.A. (2022), "Direct data–driven strategy for closed-loop aircraft flutter test", Aircraft Engineering and Aerospace Technology, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AEAT-09-2022-0264

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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