Adaptive dynamic programing design for the neural control of hypersonic flight vehicles

Abstract An adaptive dynamic programming controller based on backstepping method is designed for the optimal tracking control of hypersonic flight vehicles. The control input is divided into two parts namely stable control and optimal control. First, the back-stepping method is exploited via neural networks (NNs) to estimate unknown functions. Then, the computational load is reduced by the minimal-learning-parameter (MLP) scheme. To avoid the problem of “explosion of terms”, a first-order filter is adopted. Next, the optimal controller is designed based on the adaptive dynamic programming. In order to solve the Hamiltonian equation, NNs estimators are introduced to approximate performance indicators, achieving the approximate optimal control of hypersonic flight vehicles. Finally, the effectiveness and advantages of the control method are verified by simulation results.