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Juan I. Yuz

Bio: 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
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.
Abstract: The use of an inverter with an output LC filter allows for generation of output sinusoidal voltages with low harmonic distortion, suitable for uninterruptible power supply systems. However, the controller design becomes more difficult. This paper presents a new and simple control scheme using predictive control for a two-level converter. The controller uses the model of the system to predict, on each sampling interval, the behavior of the output voltage for each possible switching state. Then, a cost function is used as a criterion for selecting the switching state that will be applied during the next sampling interval. In addition, an observer is used for load-current estimation, enhancing the behavior of the proposed controller without increasing the number of current sensors. Experimental results under linear and nonlinear load conditions, with a 5.5-kW prototype, are presented, verifying the feasibility and good performance of the proposed control scheme.

578 citations

Journal ArticleDOI
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.
Abstract: In this paper, we present a predictive control algorithm that uses a state-space model. Based on classical control theory, an exact discrete-time model of an induction machine with time-varying components is developed improving the accuracy of state prediction. A torque and stator flux magnitude control algorithm evaluates a cost function for each switching state available in a two-level inverter. The voltage vector with the lowest torque and stator flux magnitude errors is selected to be applied in the next sampling interval. A high degree of flexibility is obtained with the proposed control technique due to the online optimization algorithm, where system nonlinearities and restrictions can be included. Experimental results for a 4-kW induction machine are presented to validate the proposed state-space model and control algorithm.

432 citations

Journal ArticleDOI
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.
Abstract: This paper presents a predictive strategy for the speed control of a two-mass system driven by a permanent magnet synchronous motor (PMSM). The proposed approach allows to manipulate all the system variables simultaneously, including mechanical and electrical variables in a single control law. The state feedback is achieved with a reduced order extended Kalman filter, which observes the non-measured variables as well as reduces the impact of measurement noise. The performance of the control strategy is shown through simulation and experimental results in a 4 [kW] laboratory prototype.

142 citations

Journal ArticleDOI
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.
Abstract: Models for deterministic continuous-time nonlinear systems typically take the form of ordinary differential equations. To utilize these models in practice invariably requires discretization. In this paper, we show 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 /spl Delta/ is the sampling period and r is the system relative degree. The resulting model includes extra zero dynamics which have no counterpart in the underlying continuous-time system. The ideas presented here generalize well-known results for the linear case. We also explore the implications of these results in nonlinear system identification.

131 citations

Journal ArticleDOI
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.
Abstract: The goal of this paper is to contribute to the understanding of fundamental performance limits for feedback control systems. In the literature to date on this topic, all available results assume that the designer has an exact model of the plant. Heuristically, however, one would expect that plant uncertainty should play a significant role in determining the best achievable performance. The goal of this paper is to investigate performance limitations for linear feedback control systems in the presence of plant uncertainty. We formulate the problem by utilizing stochastic embedding of the uncertainty. The results allow one to evaluate the best average performance in the presence of uncertainty. They also allow one to judge whether uncertainty or other properties, e.g., nonminimum phase behavior, are dominant limiting factors.

76 citations


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Posted Content
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

4,252 citations

Book ChapterDOI
01 Jan 1998
TL;DR: In this paper, the authors explore questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties, using diffusion processes as a model of a Markov process with continuous sample paths.
Abstract: We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

2,446 citations

Book ChapterDOI
11 Dec 2012

1,704 citations

Journal ArticleDOI
TL;DR: The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics.
Abstract: This paper addresses to some of the latest contributions on the application of Finite Control Set Model Predictive Control (FCS-MPC) in Power Electronics. In FCS-MPC , the switching states are directly applied to the power converter, without the need of an additional modulation stage. The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics. Some applications of this technology in drives, active filters, power conditioning, distributed generation and renewable energy are covered. Finally, attention is paid to the discussion of new trends in this technology and to the identification of open questions and future research topics.

1,331 citations

Journal ArticleDOI
TL;DR: The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm.
Abstract: Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities.

1,283 citations