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Adaptive Neural Network Based Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances

This paper proposes an adaptive neural network based intelligent controller to stabilize the Unmanned Aircraft Systems (UAS) under complex environment including system uncertainties, unknown noise and/or disturbance. The proposed adaptive neural network controller is based on a class of artificial neural network, named Radial Basis Function (RBF) networks. Firstly, we develop a neural network based identifier that can handle the unknown dynamics and uncertainties in the system. Then, a neural network based controller is generated based on both the identified model of the system and the linear or nonlinear controller. The linear or nonlinear controller is utilized to ensure the stability of the system during its online training phase. The learning capability of the proposed intelligent controller makes it a promising approach to take system uncertainties, noises and/or disturbances into account. The satisfactory performance of the proposed intelligent controller is validated based on the computer based simulation results of a benchmark UAS with system uncertainties and disturbances, such as wind gusts disturbance.