A Fuzzy Model Predictive Control Based Upon Adaptive Neural Network Disturbance Observer for a Constrained Hypersonic Vehicle

Abstract

A fuzzy model predictive control scheme based upon adaptive neural network disturbance observer is proposed for the longitudinal dynamics of a constrained hypersonic vehicle (HV) in the presence of diverse disturbances. First, an equivalent disturbed fuzzy dynamic model with the varying parameters is constructed to approximate the nonlinear dynamics, where the inevitable lumped disturbances, including the fuzzy modeling error, extraneous disturbances, and model uncertainties caused by aerodynamic uncertainties, need to be suppressed. Subsequently, according to the parameter-dependent Lyapunov function, the proposed scheme taking the varying parameters into account is developed to explicitly handle the constraints of fuel equivalence ratio, elevator deflection, and angle of attack. Furthermore, based on the strong nonlinear approximation ability of neural network (NN), an adaptive neural network disturbance observer with the adaptive laws of NN's weight matrixes is established to estimate lumped disturbances, and then an additional compensator formulated by integrating the estimations of lumped disturbances and the corresponding compensation gain matrix is appended to the proposed method for suppressing the lumped disturbances directly. Finally, the comparative simulation results for tracking the reference commands of velocity and altitude demonstrate that the proposed method provides a satisfactory tracking performance even when HV is in the presence of lumped disturbances and constraints.