Article
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Robust Combined Adaptive Passivity-Based Control for Induction Motors
Version 1
: Received: 18 March 2024 / Approved: 19 March 2024 / Online: 22 March 2024 (07:29:52 CET)
A peer-reviewed article of this Preprint also exists.
Travieso-Torres, J.C.; Ricaldi-Morales, A.J.; Aguila-Camacho, N. Robust Combined Adaptive Passivity-Based Control for Induction Motors. Machines 2024, 12, 272. Travieso-Torres, J.C.; Ricaldi-Morales, A.J.; Aguila-Camacho, N. Robust Combined Adaptive Passivity-Based Control for Induction Motors. Machines 2024, 12, 272.
Abstract
The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions
Keywords
Robust adaptive systems; Combined adaptive passivity-based control; field-oriented control; variable speed drives
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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