Novel auxiliary error compensation design for the adaptive neural control of a constrained flexible air-breathing hypersonic vehicle

Abstract

This paper investigates the design of auxiliary error compensation for adaptive neural control of the longitudinal dynamics of a flexible air-breathing hypersonic vehicle (FAHV) with magnitude constraints on actuators. The control objective pursued is to steer velocity and altitude to follow their respective reference trajectories in the presence of actuator saturation and system uncertainties. To guarantee the exploited controller’s robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of FAHV model. Different from the traditional parameter updating technique, in this paper, the minimal-learning-parameter (MLP) scheme is introduced to estimate the norm rather than the elements of NN’s weight vector while the computational load is reduced. The special contribution is that novel auxiliary systems are developed to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, numerical simulations are performed to illustrate the command tracking performance of the proposed strategy.