Discrete Adaptive Control of Hypersonic Vehicles Based on Characteristic Models with Fuzzy Neural Network Observer

A novel discrete adaptive control scheme is developed based on the characteristic modeling theory, for the attitude control of a hypersonic vehicle with strong nonlinearities and uncertainties. A new structure of the characteristic model is proposed firstly, in which an extended state is added to describe the unmolded dynamics and disturbance. Then a fuzzy neural network observer is constructed to approximate the extended state, and the updated law of the characteristic parameters and the net parameters are designed simultaneously based on the structure of the characteristic model. Further, to reject the undesired influence of the time-variation of characteristic parameters and the approximation error of the neural network, a nonlinear term containing a saturation function is added to the parameter adaption law. Then an error-surface based control law is proposed on the basis of the characteristic model. Using the Lyapunov stability theory, the closed-loop system is proved stable and convergence properties of the system are assured. Finally, the effectiveness of the proposed adaptive control scheme is illustrated by several representative simulations.