Improved nonlinear trajectory tracking using RBFNN for a robotic helicopter

This paper presents a backstepping control method using radial-basis-function neural network (RBFNN) for improving trajectory tracking performance of a robotic helicopter. Many well-known nonlinear controllers for robotic helicopters have been constructed based on the approximate dynamic model in which the coupling effect is neglected; their qualitative behavior must be further analyzed to ensure that the unmodeled dynamics do not destroy the stability of the closed-loop system. In order to improve the controller design process, the proposed controller is developed based on the complete dynamic model of robotic helicopters by using an RBFNN function approximation to the neglected dynamic uncertainties, and then proving that all the trajectory tracking error variables are globally ultimately bounded and converge to a neighborhood of the origin. The merits of the proposal controller are exemplified by four numerical simulations, showing that the proposed controller outperforms a well-known controller in (J. Robust Nonlinear Control 2004; 14(12):1035-1059). Copyright (C) 2009 John Wiley & Sons, Ltd.