Adaptive Neural Network-Based Sliding Mode Backstepping Control for Near-Space Morphing Vehicle

Abstract

In order to obtain good flight performance in the near-space morphing vehicle (NMV) cruise phase, this paper proposes an adaptive sliding mode backstepping control scheme based on a neural network, aiming at the reduction of elevator control efficiency and issues of uncertainties. Firstly, this paper analyzes the aerodynamic parameters of NMV in the states of winglet stretching and retracting during the cruise phase. Based on the above, the flight efficiency of NMV can be improved by retracting winglets in the level flight mode and stretching winglets in the altitude climbing mode. Secondly, an enhanced triple power reaching law (ETPRL) is proposed to ensure that the sliding mode control system can converge quickly and reduce chattering. Then, the sliding mode control based on ETPRL and backstepping control are combined to ensure the stability of the system, and adaptive control laws are developed to estimate and compensate for uncertainties. In addition, in face of the problem of reduced elevator control efficiency, the adaptive neural network is used to estimate and compensate for interference on the control channel to improve tracking accuracy and robustness of NMV. Finally, three sets of simulations verified the effectiveness of the proposed method.