This paper presents a Chebyshev neural network-based adaptive control system for the stabilization of a multi-input multi-output prototypical aeroelastic wing section. The two degree-of-freedom aeroelastic model is equipped with a trailing-edge and a leading-edge control surface. This aeroelastic system describes the plunge and pitch motion of a wing section. The model includes unmodeled structural plunge and pitch axis nonlinearities, parameter uncertainties and gust loads. The uncontrolled aeroelastic model exhibits limit cycle oscillations beyond a critical free-stream velocity. The objective is to stabilize the oscillatory plunge and pitch angle trajectories. A nonlinear adaptive control law is designed for the stabilization of the oscillatory state trajectories. For the derivation of the control law, Chebyshev neural networks are used to represent the unmodelled structural plunge and pitch axis nonlinearities, and SDU decomposition of the high-frequency gain matrix is considered for avoiding singularity in the control law. By the Lyapunov stability analysis, it is shown that the complete state vector is uniformly ultimately bounded. Simulation results are presented which show that the control system suppresses the oscillatory responses of the system, despite large parameter uncertainties, unmodeled structural nonlinearities and gust loads.
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