LAUSR

Abstract This paper investigates a composite learning controller for hypersonic longitudinal flight dynamics in presence of unknown dynamics. Different from previous designs, the controller is proposed without back-stepping through model transformation. This strategy simplifies the process of controller design and reduces the computation burden of parameter updating. For unknown dynamics, the radial basis function neural network (RBF NN) is used to approximate the lumped uncertainty. Base on the serial–parallel estimate model, the composite learning control scheme constructs a modeling error to evaluate the quality of learning. The highlight is that the composite learning includes the index of NN approximation performance which provides additional tuning of the system learning. Simulation results are presented and the effectiveness of the control strategy is verified.