Adaptive Neural Network-Based Nonlinear Control of Quadcopters

Hiển thị đơn giản
creativework.keywordsQuadcopter
creativework.keywordsAdaptive Control
creativework.keywordsRBF Neural Network
creativework.keywordsEstimating Disturbance
dc.contributor.authorDang Xuan Ba
dc.contributor.authorLe Manh Thang
dc.contributor.authorDoan Van Dong
dc.date.accessioned2025-11-28T02:59:24Z
dc.date.available2025-11-28T02:59:24Z
dc.date.issued2025-07
dc.description.abstractThis paper presents a novel adaptive neural network control strategy for trajectory tracking control of quadcopters in the presence of external disturbances and model uncertainties. The proposed method utilizes a hierarchical control structure, where an outer position loop is structured from a basic sliding mode control (SMC), and the inner-loop attitude control is comprised of a backstepping approach and an adaptive Radial Basis Function (RBF) neural network. The RBF neural network is designed to approximate lumped disturbances in real time through Gaussian basis functions and an online weight adaptation law, eliminating the need for detailed disturbance modeling. To evaluate the performance of the proposed approach, we conduct comparative simulations against a SMC controller and a Robust Feedback Linearization (RFBL) control method. Results obtained demonstrate that the RBF-based controller achieves superior tracking accuracy, faster convergence, and improved disturbance rejection, particularly under time-varying and uncertain conditions. These findings highlight the potential of adaptive learning-based controllers for robust and model-free UAV applications.
dc.identifier.issn1859-4263
dc.identifier.urihttps://thuvienso.ut.edu.vn/handle/123456789/4270
dc.language.isoTiếng Việt
dc.publisherThis work was supported by Ho Chi Minh City University of Technology and Education, Vietnam, under Grant T2024-01ĐH
dc.relation.ispartofseriesVolume 14; Issue 4
dc.titleAdaptive Neural Network-Based Nonlinear Control of Quadcopters
dc.typeArticle
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