Prescribed Performance Fine Attitude Control for a Flexible Hypersonic Vehicle with Unknown Initial Errors

Abstract

AbstractThis is a study of a fine attitude control system design for a flexible hypersonic vehicle with unknown initial errors. A prescribed performance control method, backstepping control method, and RBF neural network method are used to design the controllers. A newly defined error transformation function is used to solve the unknown initial error issue in prescribed performance control. Different from traditional a RBF neural network, the fully‐tuned dynamic RBF neural network has better approximation ability, and the weight vector, respective centers, and width of the Gaussian function of the neural network are regulated by adaptive laws designed in the controller. The stability of the system is proved and analyzed in this paper for a fully‐tuned dynamic neural network introduced to the control system. Furthermore, prescribed performance control can guarantee the tracking errors satisfy the specified conditions and the fine attitude control can be implemented through the prescribed performance method. Finally, the simulations demonstrate the effectiveness and corrective ability of the control strategy.