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Recently, model-based predictive current control (MB-PCC) has been presented as a good alternative to classical control algorithms in terms of simplicity and performance reliability. However, MB-PCC suffers from the high dependence on system parameters, which may deteriorate its performance under parameters variations. On the other hand, synchronous reluctance motors (SynRMs) are susceptible to suffer from inductances variations due to the magnetic saturation. Accordingly, in this article a new model-free predictive current control of SynRMs based on a recurrent neural network (RNN-PCC) is developed and proposed. The proposed RNN-PCC relies on the identification of the SynRM currents without considering any parameters. Simulation and experimental results show that both RNN-PCC and MB-PCC have similarly excellent dynamics, while better control performance and tracking errors can be achieved thanks to the proposed RNN-PCC. [ABSTRACT FROM AUTHOR]

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines due to their ability to operate under fault-tolerant conditions. However, the transition from pre- to post-fault control needs a reliable fault diagnostics algorithm. Accordingly, this article presents a reliable diagnostics approach that allows the detection and identification of multiple open-switches and open-phase faults in power converters fed six-phase induction motors, including adjacent and nonadjacent phases. The proposed method provides normalized numerical signatures from the measured output currents without requiring either tuning effort or detection thresholds. The reliability and robustness of the proposed algorithm are proven through experimental tests. [ABSTRACT FROM AUTHOR]

The paper presents a method for a direct-power control (DPC) based on Artificial Neural Networks (ANN) applied to a shunt active-power filter (SAPF). The aim is to improve the performance of conventional controls. SAPF is one of the most advanced pollution control solutions. DPC is a high-performance control for PWM converters based on the instantaneous-power theory. However, the presented control has some drawbacks, such as the presence of ripples in the current. To improve the performance of the system to be controlled, artificial neural networks are applied to the conventional control. To achieve the objective, DPC-ANN is combined with conventional DPC using MATLAB/Simulink, Simulation results show a very satisfactory performance. [ABSTRACT FROM AUTHOR]