Made available in DSpace on 2014-12-17T14:54:52Z (GMT). No. of bitstreams: 1 DanielleSSC.pdf: 1582089 bytes, checksum: 60ca15361e82c560b730f3d2c8f4b062 (MD5) Previous issue date: 2009-03-27 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Predictive Controller has been receiving plenty attention in the last decades, because the need to understand, to analyze, to predict and to control real systems has been quickly growing with the technological and industrial progress. The objective of this thesis is to present a contribution for the development and implementation of Nonlinear Predictive Controllers based on Hammerstein model, as well as to its make properties evaluation. In this case, in the Nonlinear Predictive Controller development the time-step linearization method is used and a compensation term is introduced in order to improve the controller performance. The main motivation of this thesis is the study and stability guarantee for the Nonlinear Predictive Controller based on Hammerstein model. In this case, was used the concepts of sections and Popov Theorem. Simulation results with literature models shows that the proposed approaches are able to control with good performance and to guarantee the systems stability / O Controle Preditivo tem recebido muita aten??o nas ?ltimas d?cadas, visto que a necessidade de compreender, analisar, predizer e controlar sistemas reais tem crescido rapidamente com o avan?o tecnol?gico e industrial. O objetivo desta tese ? apresentar uma contribui??o para o desenvolvimento e implementa??o de Controladores Preditivos N?o lineares baseado no modelo de Hammerstein, bem como fazer uma avalia??o de suas propriedades. Neste caso, no desenvolvimento do Controlador Preditivo N?o Linear utiliza-se o m?todo de lineariza??o por degrau de tempo e ? introduzido um termo de compensa??o a fim de melhorar o desempenho do mesmo. A principal motiva??o desta tese ? o estudo e a prova da estabilidade para o Controlador Preditivo N?o Linear baseado no modelo de Hammerstein. Para isso utilizou-se os conceitos de setores e Crit?rio de Popov. Testes de simula??o com modelos da literatura mostram que as abordagens propostas s?o capazes de controlar com um bom desempenho e garantir a estabilidade dos sistemas

Continuous-time system identification is an important subject with applications within many fields. Many physical processes are continuous in time. Therefore, when identifying a continuous-time model, we can use our insight into the system to decide the system structure and have a direct interpretation of the parameters. Furthermore, in systems such as network control systems and sensor networks, there is a common feature that the output data is quantized meaning we can only represent our data with a limited amount of distinct values. When performing continuous-time system identification of a system with quantized output data, we have errors from process and measurement noise and also a quantization error. This will make it more difficult to estimate the system parameters. This thesis aims to evaluate if it is possible to obtain accurate estimates of continuous-time systems with quantized output data using the indirect inference method. Indirect Inference is a simulation-based method that first estimates a misspecified auxiliary model to the observed data and in the second step, the parameters of the true system are estimated by simulations. Experiments are done both on one linear and two non-linear Hammerstein systems with quantized output data. The indirect inference estimator is shown to have the means to yield accurate estimates on both linear systems as well as non-linear Hammerstein systems with quantized output. The method performs better than the simplified refined instrumental variable method for continuous-time systems (SRIVC), which is commonly used for system identification of continuous-time systems, on a linear system. Furthermore, it performed significantly better compared to the Hammerstein Simplified Refined Instrumental Variable method for continuous-time systems (HSRIVC) for one of the non-linear systems and slightly better for the second one. The downside is that indirect inference is computationally expensive and time-consuming, hence not a good choice when computation time is a critical factor / Identifiering av Tidskontinuerlig system är ett viktigt ämne med användningsområde inom många områden. De flesta fysiska processer är tidskontinuerliga och när vi identifierar tidskontinuerliga modeller av dessa system kan vi använda vår insikt av systemet för att bestämma systemstrukturen och även direkt tolka dessa parametrar. I nätverkssystem och sensor-nätverk är det vanligt att vår utdata är kvantiserad, därav kan vi endast representera vår data med ett begränsat antal distinka värden. När vi identifierar tidskontinuerliga system med kvantiserad utdata, har vi därför både fel som ett resultat av process och mätbrus ovh ett kvantiseringsfel. Detta gör det svårare att identifiera parametrarna av systemet. I detta projekt var målet att utvärdera om det är möjligt att erhålla bra estimat för ett tidskontinuerligt system med kvantiserad utdata genom att använda metoden indrect inference. Indirect inference är en simuleringsbaserad metod som först estimerar en misspecificerad model från det observerade datat och i nästa steg, estimerar paramtrarna av det sanna systemet via simulering. Experiment utfördes både på ett linjärt och två olinära Hammerstein system med kvantiserad utdata. Indirect inference metoden visas ha potential att genere bra estimat på både linjära och icke-linära Hammerstein system med kvantiserad utdata. Metoden presterar bättre än SimplifiedRefined Instrumental Variable Method for continuous-time systems (SRIVC) på det linjära systemet och även mycket bättre än Hammerstein Simplified Refined InstrumentalVariable method for continuous-time systems (HSRIVC) för ett av det olinjära systemen och lite bättre för det andra. En nackdel med indirect inference är att det är beräkningstungt och att det tar lång tid att generera estimaten. Därav är denna metod inte att rekomendera när tid är en kritisk faktor.

La procédure d'identification consiste à rechercher un modèle mathématique adéquat pour un système dynamique donné à partir de données expérimentales. L'identification de systèmes naturels est cruciale pour une meilleure compréhension de notre environnement et cette thèse vise à apporter une solution au problème de modélisation de la relation pluie/débit dans un bassin versant rural. A cet effet, deux structures de modèles non-linéaires sont étudiées : les modèles Hammerstein et les modèles Linéaires à Paramètres variants (LPV). La contribution principale réside dans le développement de méthodes dédiées à l'estimation de ces modèles, à temps discret ou continu, opérant en boucle ouverte ou fermée, en se concentrant sur le cas réaliste Box--Jenkins (BJ). De plus, les méthodes proposées ont été conçues spécialement pour fournir des résultats utiles dans le cas réel où le modèle de bruit est inconnu ou mal évalué. Le premier chapitre constitue une introduction à la problématique de l'identification de systèmes naturels et motive les développements théoriques impliqués. Le deuxième chapitre présente une méthode sous optimale de variable instrumentale pour l'estimation des modèles Hammerstein BJ grâce à l'augmentation du modèle considéré. Le troisième chapitre se concentre sur l'identification de modèles LPV-BJ, soulève les problèmes rencontrés par les méthodes existantes, et propose une solution via une reformulation du modèle. Enfin, le dernier chapitre est dédié à l'application de ces méthodes sur des données réelles pour la modélisation de la relation pluie/débit / System identification is an established field in the aera of system analysis and control. It aims at determining mathematical models for dynamical systems based measured data. The identification of natural systems is crucial for a better understanding of our environment and this work aims at solving the modelling problem of the rainfall/flow relationship in rural catchments. In order to achieve this goal, two nonlinear model structures are studied: the Hammerstein and the Linear Parameter Varying (LPV) models. The contribution of this work lies in the development of identification methods dealing robustly with estimation problem of such models, both in discrete-time and continuous-time, in open-loop and closed-loop configuration, focusing on the realistic Box--Jenkins (BJ) case. Moreover, the methods are especially designed to result in relevant estimates in case the noise model is unknown, which is the case in most practical applications. The first chapter is an introduction defining the problems encountered with natural systems and motivating the theoretical work induced. The second chapter presents a suboptimal Refined Instrumental Variable based method for Hammerstein BJ models. The third chapter focuses on the identification of LPV-BJ models, highlights the problems encountered by the existing methods and proposes a solution via a reformulation of the model. Finally, the last chapter is dedicated to the application of the presented methods on some real rainfall/flow data set acquired from a rural catchment situated in Rouffach, Alsace, France for the identification of the rainfall/runoff relationship

This thesis explores the American liberal Protestant religious influences on Oscar Hammerstein II, and investigates how they are manifested in his musical plays written with Richard Rodgers in the period 1943-1959. Identifying these influences, which stem from Hammerstein's Protestant maternal family and from his attendance during his youth at the prominent Universalist church, The Church of the Divine Paternity, enable a widening of the theological engagement with popular culture to include the neglected realm of musical theatre. Having identified the Rodgers and Hammerstein musical play as a particularly powerful popular art form that explores the existential questions faced by human beings, I investigate the previously unexplored Unitarian and Universalist influences on Oscar Hammerstein II, refuting claims that he was part of the Jewish theatrical community on Broadway. Tracing these influences in Hammerstein's lyrics and libretti shows his response to these fundamental questions as human beings seek to create meaning and build identity in relation to that which is ‘other'. Within Hammerstein's personal philosophy I distinguish, the relationship between human beings and God, and the ethical relationships between human beings in community. I begin by exploring the Unitarian moral philosophy and belief in the fatherhood of God found in Carousel, The Sound of Music and Cinderella, and engaging with the Universalist depiction of the restoration period of the soul found in Carousel. Having revealed Hammerstein's liberal Protestant understanding of this relationship, I turn to his social and political activism connecting it to a social gospel understanding of the brotherhood of man and assertion of human unity. Engaging with his ‘American' musicals – Oklahoma!, Carousel, and The Sound of Music - and his ‘Asian' musicals – South Pacific, The King and I, and Flower Drum Song - separately, I question the theological implications of his late nineteenth- and early twentieth-century understanding of human unity have with regard to diversity. Throughout each of his musicals evidence is adduced of an unwavering belief in the progress of humankind onward and upward, as he reveals a significant liberal Protestant understanding of the nature of humanity, the brotherhood of man, and the possibility for human development and change.

L'objet de la thèse est l'étude, la modélisation et le traitement des distorsions non linéaires sonores, pour lesquelles les techniques actuelles s'avèrent impuissantes. L'approche retenue consiste à représenter, globalement, à la fois le signal audio à restaurer et le processus de distorsion, dans le cadre d'un modèle statistique. Cette approche présente un bon compromis entre une souhaitable généricité -possibilité de traiter à l'aide d'une méthode globale plusieurs types de distorsions- et l'utilisation de connaissances spécifiques, notamment concernant les sources de distorsions. La première étape de la thèse consiste en une analyse des mécanismes de la distorsion basée sur une série de mesures où plusieurs séquences audio sont enregistrées en entrée et en sortie d'appareils audiofréquences standards (amplificateurs de puissance, convertisseurs numérique-analogique, enregistreurs sur bandes magnétiques). Les éléments d'analyse retenus conduisent à la présentation des hypothèses principales du traitement. La méthode est basée sur un modèle de transmission non-linéaire choisi parmi ceux étudiés dans la littérature (modèles en cascades de Hammerstein simple), ainsi qu'un modèle des signaux à restaurer (modélisation autorégressive et modèle gaussien à écart-type variable). La seconde étape définit d'une part, la méthode d'identification ``autodidacte'' (à partir de la donnée seule du signal distordu) du modèle de distorsion et d'autre part, la technique de reconstruction de l'extrait sonore associée aux modèles de distorsion et de signal.

La procédure d'identification consiste à rechercher un modèle mathématique adéquat pour un système dynamique donné à partir de données expérimentales. Alors que l'identification de système est orientée majoritairement pour répondre aux problèmes de commande depuis les années 90, l'identification de systèmes naturels reste cruciale pour une meilleure compréhension de notre environnement. Cette thèse vise à apporter une solution au problème de modélisation de la relation pluie/débit dans un bassin versant rural. Un bassin versant est défini comme la portion de territoire délimitée par des lignes de crête, dont les eaux alimentent un exutoire commun : cours d'eau, lac, mer, etc. L'identification de la relation pluie/débit est un problème stimulant, de par la complexité à trouver une structure de modèle définissant le comportement du bassin dans son ensemble. De plus, dans les bassins ruraux, il y a une grande variabilité spatio-temporelle des propriétés du sol tant au niveau de la végétation, du type de sol ou de l'évapotranspiration et seulement une partie de la pluie totale ruisselle et contribue au débit à l'exutoire. Dans ce cas, les modèles linéaires ne sont pas adaptés et ne peuvent délivrer de modèle acceptable pour la relation pluie/débit. A cet effet, deux structures de modèles non-linéaires sont étudiées : les modèles Hammerstein et les modèles Linéaires à Paramètres variants (LPV). La contribution principale de cette thèse réside dans le développement de méthodes dédiées à l'estimation de ces modèles, à temps discret ou continu, opérant en boucle ouverte ou fermée, en se concentrant sur le cas réaliste où le bruit de sortie est coloré et indépendant du processus étudié : le cas Box--Jenkins (BJ). De plus, les méthodes proposées ont été conçues spécialement pour fournir des résultats utiles dans le cas réel où le modèle de bruit est inconnu ou mal évalué. Finalement, ces méthodes sont utilisées sur des données réelles, acquises sur un bassin versant rural situé à Rouffach, Alsace, France et un processus d'identification innovant est proposé pour la modélisation de la relation pluie/débit.

This thesis is about some issues in system modeling: The first is a parsimonious representation of MISO Hammerstein system, which is by projecting the multivariate linear function into a univariate input function space. This leads to the so-called semiparamtric Hammerstein model, which overcomes the commonly known “Curse of dimensionality” for nonparametric estimation on MISO systems. The second issue discussed in this thesis is orthogonal expansion analysis on a univariate Hammerstein model and hypothesis testing for the structure of the nonlinear subsystem. The generalization of this technique can be used to test the validity for parametric assumptions of the nonlinear function in Hammersteim models. It can also be applied to approximate a general nonlinear function by a certain class of parametric function in the Hammerstein models. These techniques can also be extended to other block-oriented systems, e.g, Wiener systems, with slight modification. The third issue in this thesis is applying machine learning and system modeling techniques to transient stability studies in power engineering. The simultaneous variable section and estimation lead to a substantially reduced complexity and yet possesses a stronger prediction power than techniques known in the power engineering literature so far.

This thesis is about some issues in system modeling: The first is a parsimonious representation of MISO Hammerstein system, which is by projecting the multivariate linear function into a univariate input function space. This leads to the so-called semiparamtric Hammerstein model, which overcomes the commonly known “Curse of dimensionality” for nonparametric estimation on MISO systems. The second issue discussed in this thesis is orthogonal expansion analysis on a univariate Hammerstein model and hypothesis testing for the structure of the nonlinear subsystem. The generalization of this technique can be used to test the validity for parametric assumptions of the nonlinear function in Hammersteim models. It can also be applied to approximate a general nonlinear function by a certain class of parametric function in the Hammerstein models. These techniques can also be extended to other block-oriented systems, e.g, Wiener systems, with slight modification. The third issue in this thesis is applying machine learning and system modeling techniques to transient stability studies in power engineering. The simultaneous variable section and estimation lead to a substantially reduced complexity and yet possesses a stronger prediction power than techniques known in the power engineering literature so far.

Thesis (M. Mus.)--University of Akron, School of Music, 2005. / "August, 2005." Title from electronic thesis title page (viewed 11/28/2005). Advisor, Brooks Toliver; Faculty Reader, Michele Tannenbaum; School Director, William Guegold; Dean of the College, Mark S. Auburn; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica / Made available in DSpace on 2012-10-23T07:46:46Z (GMT). No. of bitstreams: 1 238732.pdf: 2058587 bytes, checksum: b3f37188dd9f1fa6bb3a0f50c74d43f6 (MD5) / As pesquisas associadas às estratégias de controle preditivo não-linear têm apresentado grande crescimento ultimamente registrando, também, um número considerável de aplicações na indústria. A representação de um processo complexo através de um modelo não-linear, com o objetivo de melhorar seu desempenho dinâmico, tende a sacrificar a simplicidade de projeto do controlador preditivo. Visando aliar a capacidade de representação da não-linearidade de um processo com a simplicidade de projeto, torna-se interessante a utilização de controladores preditivos baseados no modelo de Hammerstein o qual é constituído de um bloco estático, não-linear, seguido de um bloco linear dinâmico. Esta tese apresenta um estudo de modelagem, identificação e controle preditivo não-linear baseado em modelos de Hammerstein. Algumas técnicas de seleção de estrutura e identificação do modelo de Hammerstein são apresentadas e algumas inovações são propostas. Estratégias de controle preditivo baseado no modelo de Hammerstein são discutidas e são propostas modificações num controlador para a inclusão de perturbações mensuráveis e uma técnica analítica para solucionar a multiplicidade do sinal de controle. Para avaliar as técnicas de identificação e controle estudadas, são apresentados resultados de simulação e experimentais em uma planta solar de climatização.