Article: Observer-based adaptive neural network robust H∞ control of quadrotor aerial robot Journal: International Journal of Automation and Control (IJAAC) 2025 Vol.19 No.4 pp.412 - 436 Abstract: This paper presents an approach utilising observer-based adaptive neural networks to achieve robust H∞ control in an autonomous quadrotor system. The methodology addresses parameter uncertainties and wind disturbances for enhanced control performance. By integrating a state observer design, our method eliminates the need for direct access to state variables. We employ adaptive neural networks, specifically radial basis function neural networks, to approximate unknown functions through state estimates. The proposed methodology ensures H∞ tracking performance, addressing cumulative uncertainties from unmodelled dynamics, approximation errors, and external disturbances. Leveraging Lyapunov stability theory, we rigorously establish the uniform ultimate boundedness of both the observer-based controller and the closed-loop system. Simulation results are provided to validate and underscore the effectiveness of this approach. Inderscience Publishers - linking academia, business and industry through research

Title: Observer-based adaptive neural network robust H_∞ control of quadrotor aerial robot

Authors: Zakaria Bellahcene; Abdelmalek Laidani; Mohamed Bouhamida

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

Abstract: This paper presents an approach utilising observer-based adaptive neural networks to achieve robust H_∞ control in an autonomous quadrotor system. The methodology addresses parameter uncertainties and wind disturbances for enhanced control performance. By integrating a state observer design, our method eliminates the need for direct access to state variables. We employ adaptive neural networks, specifically radial basis function neural networks, to approximate unknown functions through state estimates. The proposed methodology ensures H_∞ tracking performance, addressing cumulative uncertainties from unmodelled dynamics, approximation errors, and external disturbances. Leveraging Lyapunov stability theory, we rigorously establish the uniform ultimate boundedness of both the observer-based controller and the closed-loop system. Simulation results are provided to validate and underscore the effectiveness of this approach.

Keywords: H_∞ control; observer; UAV; unmanned aerial vehicle; neural networks RBF NNs; adaptive tracking.

DOI: 10.1504/IJAAC.2025.147201

International Journal of Automation and Control, 2025 Vol.19 No.4, pp.412 - 436

Received: 20 Mar 2024
Accepted: 17 Jul 2024
Published online: 11 Jul 2025 *

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Title: Observer-based adaptive neural network robust H∞ control of quadrotor aerial robot

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Title: Observer-based adaptive neural network robust H_∞ control of quadrotor aerial robot