Quadrotor trajectory tracking based on backstepping control and radial basis function neural networks
Aristotelis Kapnopoulos, Charalampos Kazakidis, Alex Alexandridis
Abstract
In this paper we introduce a novel method for solving the trajectory tracking problem for a quadrotor system based on backstepping control and radial basis function (RBF) neural networks. Backstepping controllers in quadrotor control are designed so as to guarantee Lyapunov stability of the closed-loop system based on a dynamic model of the quadrotor, derived using first principles equations. Such a model is prone to modelling errors due to various factors, including uncertainties, time varying quadrotor parameters and external disturbances acting on the vehicle, thus compromising the controller performance. To remedy this situation, we couple the controller with a radial basis function (RBF) neural network trained with the fuzzy means (FM) algorithm, which allows the proposed RBF – backstepping framework to take into account unmodelled dynamics with increased accuracy, thus improving the tracking performance. The resulting method is evaluated on a detailed quadrotor model over trajectories with distinct geometrical characteristics, and compared with a backstepping controller using a different neural network architecture and a standard backstepping control scheme. The proposed control method produces very competitive results, outperforming its rival in terms of trajectory tracking performance.