Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters
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Archivos
Fecha
2021-04
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
MDPI
Nombre de Curso
Licencia CC
Attribution 4.0 International (CC BY 4.0)
Licencia CC
https://creativecommons.org/licenses/by/4.0/
Resumen
An accurate definition of a system model significantly affects the performance of modelbased control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.
Notas
Indexación: Scopus.
Palabras clave
Model predictive control (MPC), Model-free predictive control, Power converter, Robust performance, State-space neural network with particle swarm optimization (ssNN-PSO)
Citación
Energies Open Access Volume 14, Issue 82 April 2021 Article number 2325
DOI
10.3390/en14082325