Adaptive Neural Network-Based Robust H∞ Tracking Control of a Quadrotor UAV Under Wind Disturbances

Abstract

The paper deals with the stabilisation and trajectory tracking control of an autonomous quadrotor helicopter system in the presence of wind disturbances. The proposed adaptive tracking controller uses radial basis function neural networks (RBF NNs) to approximate unknown nonlinear functions in the system. Two controllers are proposed in this paper to handle the modelling errors and external disturbances: H∞ adaptive neural controller H∞-ANC and H∞-based adaptive neural sliding mode controller H∞-ANSMC. The design approach combines the robustness of sliding mode control (SMC) with the ability of H∞ to deal with parameter uncertainties and bounded disturbances. Furthermore, the RBF models are derived using Lyapunov stability analysis. The simulation results show that H∞-ANSMC is able to eliminate the chattering phenomenon, reject perturbation mismatch and leads to a better performance than H∞-ANC. A comparative simulation study between the proposed controllers is presented and the results are discussed.