Article: Adaptive neural network-based robust H∞ tracking control of a quadrotor UAV under wind disturbances Journal: International Journal of Automation and Control (IJAAC) 2021 Vol.15 No.1 pp.28 - 57 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. Inderscience Publishers - linking academia, business and industry through research

Title: Adaptive neural network-based robust H_∞ tracking control of a quadrotor UAV under wind disturbances

Authors: Zakaria Bellahcene; Mohamed Bouhamida; Mouloud Denai; Khaled Assali

Addresses: Laboratory of Automatics Vision and Intelligent Control Systems, Department of Automatic, University of Science and Technology of Oran, USTO-MB BP 1505 El M'naouer, 31000 Oran, Algeria ' Laboratory of Automatics Vision and Intelligent Control Systems, Department of Automatic, University of Science and Technology of Oran, USTO-MB BP 1505 El M'naouer, 31000 Oran, Algeria ' University of Hertfordshire, Hatfield, UK ' Laboratory of Automatics Vision and Intelligent Control Systems, Department of Automatic, University of Science and Technology of Oran, USTO-MB BP 1505 El M'naouer, 31000 Oran, Algeria

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.

Keywords: adaptive tracking; H_∞ control; quadrotor control; neural networks; sliding mode control; SMC; robust control; UAV.

DOI: 10.1504/IJAAC.2021.111747

International Journal of Automation and Control, 2021 Vol.15 No.1, pp.28 - 57

Received: 18 Aug 2018
Accepted: 04 Mar 2019
Published online: 14 Dec 2020 *

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Title: Adaptive neural network-based robust H∞ tracking control of a quadrotor UAV under wind disturbances

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Title: Adaptive neural network-based robust H_∞ tracking control of a quadrotor UAV under wind disturbances