Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller

This contribution proposes a novel neural-network-based control approach to stabilize a nonlinear aeroelastic wing section. With the prerequisite that all the states of the system are available, the proposed controller requires no comprehensive information about structural nonlinearity of the wing section. Furthermore, the proposed control approach requires no human intervention of designing goal dynamics and formulating control input function, which is difficult to be realized by the typical neural-network-based control following an inverse control scheme. Simulation results show that the proposed controller can stabilize the aeroelastic system with different nonlinearities.