In this paper, a novel model-reference adaptive control methodology is proposed for the regulation of two power converter topologies. The main controller objective is the asymptotic tracking of the reference trajectory provided as input. The tracking is achieved through the adaptive mechanism based on Torelli Control Box approach. The control methodology explicitly guarantees convergence and asymptotic stability of the system. The designed controller has been simulated on buck and boost power converters and its performance has been analyzed by subjecting the converters to varying load and voltage conditions. Under all the test conditions, the controller proposed performs better than a backstepping-based controller taken as a benchmark for both converters.

Application of a Novel Adaptive Control Approach for the Regulation of Power Converters / Qureshi, Ma; Musumeci, S; Torelli, F; Reatti, A; Mazza, A; Chicco, G. - ELETTRONICO. - (2022), pp. 1-6. (Intervento presentato al convegno 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 tenutosi a Istanbul, Turkey) [10.1109/UPEC55022.2022.9917619].

Application of a Novel Adaptive Control Approach for the Regulation of Power Converters

Qureshi, MA;Musumeci, S;Torelli, F;Mazza, A;Chicco, G
2022

Abstract

In this paper, a novel model-reference adaptive control methodology is proposed for the regulation of two power converter topologies. The main controller objective is the asymptotic tracking of the reference trajectory provided as input. The tracking is achieved through the adaptive mechanism based on Torelli Control Box approach. The control methodology explicitly guarantees convergence and asymptotic stability of the system. The designed controller has been simulated on buck and boost power converters and its performance has been analyzed by subjecting the converters to varying load and voltage conditions. Under all the test conditions, the controller proposed performs better than a backstepping-based controller taken as a benchmark for both converters.
2022
978-1-6654-5505-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2979993