Robust Adaptive Neural Network Control of Miniature Unmanned Helicopter

Abstract

This paper presents a simple and time efficient robust adaptive neural network controller (RANNC) for a miniature unmanned helicopter with the presence of wind disturbance and uncertainties. In the proposed controlling method, firstly the control law is designed, using sliding mode control approach. Then, for overcoming unwanted effects of uncertainties, a robust adaptive neural network sliding mode controller is proposed. Helicopter model is approximated by radial basis function (RBF) neural network, and Lyapunov stability theory concept is used to design adaptation laws. The mathematical proof shows that the closed loop system is asymptotically stable in the presence of this controller. To verify the competences of the proposed controller, it is compared with traditional sliding mode controller. The chattering phenomenon is attenuated completely and the tracking error is also alleviated. The simulation results confirm the desirable performance of proposed robust adaptive neural network controller.