This thesis investigates adaptive control systems for a special class of multi-rotor unmanned aerial vehicles (UAVs), namely X8-copters, tasked with transporting heavy, unsteady, and unknown payloads. Carefully designed dynamical models allow exploiting the vehicle’s nonlinearities and structure of the adaptive control mechanisms and, hence, attain superior performance over classical linear controllers currently available in commercial off-the-shelf autopilots for UAVs. Four main classes of adaptive control systems are employed to design the UAV’s control system, namely classical model reference adaptive control (MRAC), the recently developed Two-Layer MRAC, and robustifications of these two techniques. These results are unprecedented in the literature on X8-copters. An additional element of novelty of this thesis lies in the high-fidelity simulations performed employing PyChrono, which was originally developed for multi-body simulations and terramechanics applications. The use of this piece of software allows the user to obtain high-quality results without having to model, and, hence, approximate, the complex dynamics of unknown, unsteady payloads.
Questa tesi investiga sistemi di controllo adattivo per una classe speciale di veicoli aerei senza pilota a multirotore (UAV), in particolare X8-copter, incaricati di trasportare carichi pesanti, instabili e ignoti. Modelli dinamici attentamente progettati permettono di sfruttare sia le non linearità del veicolo che la struttura dei meccanismi di controllo adattivo e, quindi, ottenere prestazioni superiori rispetto ai controllori lineari classici attualmente disponibili negli autopiloti commerciali per UAV. Quattro principali classi di sistemi di controllo adattivo vengono impiegate per progettare il sistema di controllo dell’UAV: Model Reference Adaptive Control (MRAC), il recentemente sviluppato Two- Layer MRAC e le robustizzazioni di queste due tecniche. Questi risultati sono senza precedenti nella letteratura sugli X8-copter. Un ulteriore elemento di novità di questa tesi risiede nelle simulazioni ad alta fedeltà eseguite utilizzando PyChrono, originariamente sviluppato per simulazioni multibody e applicazioni di terrameccanica. L’uso di questo software consente all’utente di ottenere risultati di alta qualità senza dover modellare e, quindi, approssimare le complesse dinamiche dei carichi sconosciuti e instabili.
Design and high-fidelity simulations of an adaptive control system for X8-copters employed for payload delivery
Gramuglia, Mattia
2022/2023
Abstract
This thesis investigates adaptive control systems for a special class of multi-rotor unmanned aerial vehicles (UAVs), namely X8-copters, tasked with transporting heavy, unsteady, and unknown payloads. Carefully designed dynamical models allow exploiting the vehicle’s nonlinearities and structure of the adaptive control mechanisms and, hence, attain superior performance over classical linear controllers currently available in commercial off-the-shelf autopilots for UAVs. Four main classes of adaptive control systems are employed to design the UAV’s control system, namely classical model reference adaptive control (MRAC), the recently developed Two-Layer MRAC, and robustifications of these two techniques. These results are unprecedented in the literature on X8-copters. An additional element of novelty of this thesis lies in the high-fidelity simulations performed employing PyChrono, which was originally developed for multi-body simulations and terramechanics applications. The use of this piece of software allows the user to obtain high-quality results without having to model, and, hence, approximate, the complex dynamics of unknown, unsteady payloads.File | Dimensione | Formato | |
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2023_12_Gramuglia_Executive_Summary_02.pdf
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Descrizione: Executive Summary
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2023_12_Gramuglia_Thesis_01.pdf
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Descrizione: Thesis
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https://hdl.handle.net/10589/214350