Adaptive neural tracking control for near-space vehicles with stochastic disturbances

Abstract

In this article, an adaptive neural tracking controller is designed for near-space vehicles with stochastic disturbances and unknown parametric uncertainties. Based on the great nonlinear function approximation capability of neural networks, the unknown system uncertainties are tackled using the radial basis function neural networks. Furthermore, on the basis of stochastic Lyapunov stability theory, an adaptive tracking control scheme is developed for near-space vehicle which can guarantee the closed-loop system stability. Under the developed adaptive neural control scheme, all closed-loop system signals are bounded in the sense of probability, and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are provided to illustrate the proposed adaptive neural control scheme that can guarantee the satisfactory tracking performance for the attitude motion of the near-space vehicle with stochastic disturbances.