No abstract

In this article, the disturbance observer-based control is designed for a flexible hypersonic flight vehicle with the external disturbance. The aircraft structure is easy to cause elastic vibration, thus leading to serious change in structural configuration or even disintegration. Therefore, the impact of elasticity modal for altitude subsystem is described as a system disturbance, and then, the equivalent model is established. On this basis, considering the model structure and composition of nonlinear systems, the disturbance observer is used to eliminate the information of the disturbance. Finally, the simulation results make an offer to show that the disturbance observer-based control can provide a good tracking performance.

No abstract

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.