J
Juan I. Yuz
Researcher at Federico Santa María Technical University
Publications - 112
Citations - 2360
Juan I. Yuz is an academic researcher from Federico Santa María Technical University. The author has contributed to research in topics: Linear system & Nonlinear system. The author has an hindex of 19, co-authored 108 publications receiving 2109 citations. Previous affiliations of Juan I. Yuz include Valparaiso University & University of Newcastle.
Papers
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Journal ArticleDOI
Model Predictive Control of an Inverter With Output $LC$ Filter for UPS Applications
Patricio Cortes,Gonzalo Costales Ortiz,Juan I. Yuz,Jose Rodriguez,Sergio Vazquez,Leopoldo G. Franquelo +5 more
TL;DR: A new and simple control scheme using predictive control for a two-level converter using a model of the system to predict the behavior of the output voltage for each possible switching state is presented.
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Predictive Torque Control of Induction Machines Based on State-Space Models
TL;DR: A predictive control algorithm that uses a state-space model of an induction machine with time-varying components improving the accuracy of state prediction and a high degree of flexibility is obtained with the proposed control technique due to the online optimization algorithm.
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Predictive Speed Control of a Two-Mass System Driven by a Permanent Magnet Synchronous Motor
TL;DR: This paper presents a predictive strategy for the speed control of a two-mass system driven by a permanent magnet synchronous motor that allows to manipulate all the system variables simultaneously, including mechanical and electrical variables in a single control law.
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On sampled-data models for nonlinear systems
Juan I. Yuz,Graham C. Goodwin +1 more
TL;DR: This paper shows how an approximate sampled-data model can be obtained for deterministic nonlinear systems such that the local truncation error between the output of this model and the true system is of order /spl Delta//sup r+1/, where /splDelta/ is the sampling period and r is the system relative degree.
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Performance limitations for linear feedback systems in the presence of plant uncertainty
TL;DR: Stochastic embedding of the uncertainty allows one to evaluate the best average performance in the presence of uncertainty and allow one to judge whether uncertainty or other properties, e.g., nonminimum phase behavior, are dominant limiting factors.