Neural Network Based UAV Fault Tolerant Control with Aerodynamic Disturbance Estimation and Compensation

Manconi, Luigi (2022) Neural Network Based UAV Fault Tolerant Control with Aerodynamic Disturbance Estimation and Compensation. [Laurea magistrale], Università di Bologna, Corso di Studio in Aerospace engineering / ingegneria aerospaziale [LM-DM270] - Forli', Documento full-text non disponibile
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In this thesis, the problem of controlling a quadrotor UAV is considered. It is done by presenting an original control system, designed as a combination of Neural Networks and Disturbance Observer, using a composite learning approach for a system of the second order, which is a novel methodology in literature. After a brief introduction about the quadrotors, the concepts needed to understand the controller are presented, such as the main notions of advanced control, the basic structure and design of a Neural Network, the modeling of a quadrotor and its dynamics. The full simulator, developed on the MATLAB Simulink environment, used throughout the whole thesis, is also shown. For the guidance and control purposes, a Sliding Mode Controller, used as a reference, it is firstly introduced, and its theory and implementation on the simulator are illustrated. Finally the original controller is introduced, through its novel formulation, and implementation on the model. The effectiveness and robustness of the two controllers are then proven by extensive simulations in all different conditions of external disturbance and faults.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Manconi, Luigi
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Neural Networks, adaptive control, fault tolerant control, aerodynamic disturbance, quadrotor
Data di discussione della Tesi
14 Luglio 2022

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