Adaptive Backstepping Control of Morphing Aircraft Based on RBF Neural Networks

To enhance the disturbance rejection of morphing aircraft, an adaptive backstepping control method based on the RBF neural network is proposed. Firstly, the aerodynamic parameters and model parameters are modeled as functions of sweep angle, and the latter is expressed as pure function of time. Then, the RBF neural network adaptive algorithm is combined with the backstepping control to estimate the uncertain disturbance during the wing transition process, so as to compensate for the control law output by the backstepping control. Thereafter, the weight update law of the RBF adaptive algorithm is obtained by Lyapunov stability theory, and the stability of the control system is proved at the same time. Finally, comparative simulation experiments are used to show that the proposed control strategy is preferable, and the error between actual values and the estimated of the RBF adaptive algorithm can be controlled within 0.05%.