Regular Papers

International Journal of Control, Automation and Systems 2022; 20(2): 678-690

Published online February 4, 2022

https://doi.org/10.1007/s12555-020-0694-0

© The International Journal of Control, Automation, and Systems

Adaptive Back-stepping Neural Control for an Embedded and Tiltable Vtail Morphing Aircraft

Fuxiang Qiao, Jingping Shi*, Xiaobo Qu, and Yongxi Lyu

Northwestern Polytechnical University

Abstract

This paper presents an adaptive back-stepping neural control (ABNC) method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft. Based on a large number of aerodynamic data for different V-tail configurations, the longitudinal and lateral aerodynamic characteristics of the aircraft are analyzed, and a nonlinear model with six degrees-of-freedom is established. To avoid the problem of “differential explosion,” the controller is designed using the traditional back-stepping control (TBC) method with a first-order filter. Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model, and a controller based on the ABNC method is designed. The stability of the proposed ABNC controller is proved using Lyapunov theory, and it is shown that the tracking error of the closed-loop system converges uniformly within specified bounds. Simulation results show that the ABNC controller works well, with better tracking performance and robustness than the TBC controller. "

Keywords Adaptive control, back-stepping control, morphing aircraft, neural networks, radial basis function.

Article

Regular Papers

International Journal of Control, Automation and Systems 2022; 20(2): 678-690

Published online February 1, 2022 https://doi.org/10.1007/s12555-020-0694-0

Copyright © The International Journal of Control, Automation, and Systems.

Adaptive Back-stepping Neural Control for an Embedded and Tiltable Vtail Morphing Aircraft

Fuxiang Qiao, Jingping Shi*, Xiaobo Qu, and Yongxi Lyu

Northwestern Polytechnical University

Abstract

This paper presents an adaptive back-stepping neural control (ABNC) method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft. Based on a large number of aerodynamic data for different V-tail configurations, the longitudinal and lateral aerodynamic characteristics of the aircraft are analyzed, and a nonlinear model with six degrees-of-freedom is established. To avoid the problem of “differential explosion,” the controller is designed using the traditional back-stepping control (TBC) method with a first-order filter. Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model, and a controller based on the ABNC method is designed. The stability of the proposed ABNC controller is proved using Lyapunov theory, and it is shown that the tracking error of the closed-loop system converges uniformly within specified bounds. Simulation results show that the ABNC controller works well, with better tracking performance and robustness than the TBC controller. "

Keywords: Adaptive control, back-stepping control, morphing aircraft, neural networks, radial basis function.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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