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Neural Network-Based Adaptive Attitude Controller for a Micro-Unmanned Aerial Vehicle

Year: 2026

Type: article

Abstract: The deployment of micro-Unmanned Aerial Vehicles (UAVs) for civil applications has exponentially grown in the last decades due to their versatility, low cost and ease of use. However, their effective implementation requires an efficient attitude control system that ensures the tracking of a reference signal, even in the presence of uncertainties or external disturbances. The main aim of this paper is to present the design of a neural-network-augmented H∞ controller for a micro-UAV, to compensate for dynamic uncertainties and environmental perturbations. The effectiveness of the proposed architecture is validated through both numerical sensitivity analyses and experimental tests on a Crazyflie 2.1+ quadrotor mounted on a tilting test bench.

Source: Sensors

DOI: 10.3390/s26216797

Authors: Gennaro Raspaolo, Salvatore Rosario Bassolillo, Luciano Blasi, Egidio D’Amato, Immacolata Notaro

Institutions: University of Campania "Luigi Vanvitelli", Parthenope University of Naples

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

OpenAlex ID: W7223917839

FWCI:

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