<p>This study examines the stability of wing rock phenomena, which occur in a delta wing or high-sweptback aircraft at a high angle of attack, resulting in lateral instability during flight. To improve the fast convergence and reduce the transient response of roll angle, a second order sliding mode control (SOSMC) based on global fast terminal (GFT) attractor is presented using the backstepping control technique. Moreover, a reduced-order proportional integral observer based on the equivalent control concept is also offered in order to overcome the chattering problem of the reduced-order sliding mode observer and to estimate the roll rate in wing rock dynamics. Additionally, to estimate the bound of external disturbance, an adaptive protocol based on the radial basis function neural network (RBFNN) is proposed. The asymptotic stability of the closed-loop system is guaranteed by the Lyapunov stability theory. To extract the performance of the neural adaptive backstepping GFT SOSMC (NABGFTSOSMC), simulations are performed in comparison with neural adaptive backstepping SOSMC (NABSOSMC) and neural adaptive backstepping fast terminal SOSMC (NABFTSOSMC). The outcomes reveal that the suggested control approach outperforms the other two control approaches in terms of stabilization performance for wing rock phenomena and adaptation for the bound of external disturbance.</p>

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Neural Network-based Adaptive Backstepping Global Fast Terminal Second-order Sliding Mode Control for Wing Rock Suppression

  • Ahmad Mahmood

摘要

This study examines the stability of wing rock phenomena, which occur in a delta wing or high-sweptback aircraft at a high angle of attack, resulting in lateral instability during flight. To improve the fast convergence and reduce the transient response of roll angle, a second order sliding mode control (SOSMC) based on global fast terminal (GFT) attractor is presented using the backstepping control technique. Moreover, a reduced-order proportional integral observer based on the equivalent control concept is also offered in order to overcome the chattering problem of the reduced-order sliding mode observer and to estimate the roll rate in wing rock dynamics. Additionally, to estimate the bound of external disturbance, an adaptive protocol based on the radial basis function neural network (RBFNN) is proposed. The asymptotic stability of the closed-loop system is guaranteed by the Lyapunov stability theory. To extract the performance of the neural adaptive backstepping GFT SOSMC (NABGFTSOSMC), simulations are performed in comparison with neural adaptive backstepping SOSMC (NABSOSMC) and neural adaptive backstepping fast terminal SOSMC (NABFTSOSMC). The outcomes reveal that the suggested control approach outperforms the other two control approaches in terms of stabilization performance for wing rock phenomena and adaptation for the bound of external disturbance.