Neural network modeling-based anti-disturbance tracking control for hypersonic flight vehicle models

This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle (HFV) models by using neural network (NN) identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbance-observer-based-control (DOBC) algorithm with adaptive regulation laws is thus presented to estimate the nonlinear disturbances. By integrating the estimated value of disturbances with the PI feedback control input, a composite controller based on convex optimization theory is generated to ensure the satisfactory stability and dynamical tacking convergence of HFV models. Finally, a numerical example for HFV models is included to illustrate the effectiveness of the theoretical results.