Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions

Abstract

In this article, we examine the system control design of a flexible hypersonic vehicle with an unknown direction control. Prescribed performance controls, backstepping controls, and radial basis function neural network are used to design the controllers. Different from a traditional radial basis function neural network, the fully tuned dynamic radial basis function neural network has a better approximation ability; and the weight vector, respective centers, and width of the Gaussian function of neural network are regulated by adaptive laws designed in the controller. The proof and analysis of stability are taken in this article for the fully tuned dynamic neural network introduced to control the system. Furthermore, prescribed performance control can guarantee the tracking errors satisfy the specified conditions. The unknown control direction is solved with the Nussbaum function in the controller. Finally, the simulations demonstrate the effectiveness and corrective measures of the control strategy.