J
Jose Rodriguez
Researcher at Andrés Bello National University
Publications - 1039
Citations - 68908
Jose Rodriguez is an academic researcher from Andrés Bello National University. The author has contributed to research in topics: Model predictive control & Converters. The author has an hindex of 93, co-authored 803 publications receiving 58176 citations. Previous affiliations of Jose Rodriguez include University of Miami & April.
Papers
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Journal ArticleDOI
Simplified Finite Control Set-Model Predictive Control for Matrix Converter-Fed PMSM Drives
TL;DR: In this article, the authors proposed an effective method to simplify the FCS-MPC and to reduce its calculation efforts for its application in matrix converter-fed permanent magnet synchronous motors.
Journal ArticleDOI
Cascade-Free Predictive Speed Control for Electrical Drives
TL;DR: The experimental results obtained with a permanent-magnet synchronous motor and an induction machine, fed by a two-level three-phase inverter, confirm that the design of a predictive speed controller for electrical drives reaches a very good dynamic performance without a high computational effort.
Journal ArticleDOI
Predictive Current Control With Input Filter Resonance Mitigation for a Direct Matrix Converter
TL;DR: In this article, the authors proposed a control method for direct matrix converters that combines the advantages of predictive control with active damping, which is used to suppress the resonance of the input filter.
Proceedings ArticleDOI
Power Distribution in Hybrid Multi-cell Converter with Nearest Level Modulation
TL;DR: In this article, the modulation index rates of regenerative operation were found for a three-cell hybrid multi-cell converter for two different asymmetries, as well as a comparison algorithm to allow a mathematical analysis.
Journal ArticleDOI
Torque Ripple Reduction of Predictive Torque Control for PMSM Drives With Parameter Mismatch
TL;DR: In this article, a modified PTC for a permanent magnet synchronous motor was proposed to reduce the torque ripple at the presence of parametric uncertainty by improving the prediction accurateness.