Nonlinear adaptive tracking control for a small-scale unmanned helicopter using a learning algorithm with the least parameters

Abstract

This paper puts forward a new nonlinear adaptive controller for a small-scale unmanned helicopter with unknown mass. The controller is developed under the framework of backstepping technique, with the unknown mass estimated by a novel identifier and the internal and external uncertainties approximated by radial basis function neural networks (RBFNNs). The overall closed-loop system, which consists of three parts: longitudinal–lateral subsystem, heave subsystem, and heading subsystem, is proved to be semi-globally uniformly ultimately bounded by the strict Lyapunov stability theory. Furthermore, the proposed method is more practical in actual applications with an improved online learning algorithm of the least parameters used in the RBFNNs. Finally, the effectiveness and the robustness of the proposed strategy are exhibited through two simulations compared with the classic PID method.