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.

Forecasting, Structural Time Series Models and the Kalman Filter

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.

Stochastic Differential Equations

System Identification I

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.

State of the Art of Finite Control Set Model Predictive Control in Power Electronics

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.

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Model Predictive Control for Power Converters and Drives: Advances and Trends

Dissipative port-Hamiltonian Formulation of Maxwell Viscoelastic Fluids

When identifying a dynamic system the model has to be validated as well. For an errors-in-variables situation where both input and output measurements are noise corrupted, this is a nontrivial task, seldom treated in the literature. Some different approaches for model validation are introduced and evaluated by theoretical analysis as well as application to simulated data.

Model validation methods for errors-in-variables estimation

Dealing with the control design problem for a given system, the presence of pure time-delays in the plant imposes interesting challenges from the control point of view In fact, different techniques can be applied among them the well-known Smith predictor In this paper, the use of the Smith predictor is analyzed and approximated in the frequency domain by means of a Taylor expansion Additionally, this approximation can be interpreted as the introduction of a prediction in the loop to modify the Nyquist plot, such that stability margins are improved As an alternative, a notch filter to reduce the gain at the crossover frequency is also considered A control design example for an unstable time-delayed plant illustrates this approach Some advantages and drawbacks of this control design approach are also outlined

Frequency domain interpretation of the Smith predictor

In this paper we consider the problem of obtaining a general port-Hamiltonian formulation of Newtonian fluids. We propose the port-Hamiltonian models to describe the energy flux of rotational three-dimensional isentropic and non-isentropic fluids, whose boundary flows and efforts can be used for control purposes or for power-preserving interconnection with other physical systems. In case of two-dimensional flows, we include the considerations about the operators associated with fluid vorticity, preserving the port-Hamiltonian structure of the models proposed.

Juan I. Yuz

Papers

Dissipative port-Hamiltonian Formulation of Maxwell Viscoelastic Fluids

Model validation methods for errors-in-variables estimation

Frequency domain interpretation of the Smith predictor

About Dissipative and Pseudo Port-Hamiltonian Formulations of Irreversible Newtonian Compressible Flows

EM-based identification of sparse FIR systems having quantized data1