Nonlinear system identification and predictive control of a heat exchanger based on local linear fuzzy models

Hecker, Oliver; Nelles, Oliver; Moseler, Olaf

doi:10.1109/acc.1997.612074

Cited by 24 publications

(9 citation statements)

References 13 publications

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“…• A nonlinear dynamic model has been built for a truck Diesel engine turbocharger for a hardware-in-the-Ioop simulation [268,288,356]. • Concepts have been developed for nonlinear system identification and nonlinear predictive control of a tubular heat exchanger [144,281,283]. • Neural networks with internal and external dynamics are compared theoretically in [274].…”

Section: Discussionmentioning

confidence: 99%

Nonlinear System Identification

Nelles

2001

1,091

465

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PrefaceThe goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications.Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure optimization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same approach for a broad variety of systems. Certainly, a more problem-specific procedure typically promises superior performance, but from an industrial point of view a good tradeoff between development effort and performance is the decisive criterion for success. This book presents neural networks and fuzzy models together with major classical approaches in a unified framework. The strict distinction between the model architectures on the one hand (Part II) and the techniques for fitting these models to data on the other hand (Part I) tries to overcome the confusing mixture between both that is frequently encountered in the neuro and fuzzy literature. Nonlinear system identification is currently a field of very active research; many new methods will be developed and old methods will be refined. Nevertheless, I am confident that the underlying principles will continue to be valuable in the future.This book offers enough material for a two-semester course on optimization and nonlinear system identification. A higher level one-semester graduate course can focus on Chap. 7, the complete Part II, and Chaps. 17 to 21 in VI Preface Part III. For a more elementary one-semester course without prerequisites in optimization and linear system identification Chaps. 1 to 4 and 16 might be covered, while Chaps. 10, 14, 18, and 21 can be skipped. Alternatively, a course might omit the dynamic systems in Part III and instead emphasize the optimization techniques and nonlinear static modeling treated in Parts I and II. The applications presented in Part IV focus on a certain model architecture, and will convince th...

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Section: Discussionmentioning

confidence: 99%

Nonlinear System Identification

Nelles

2001

1,091

465

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“…The model predictive controller utilizes a linear plant model which is updated each sampling instant. The linear model is obtained from a model constructed via the local linear model tree (LoLiMoT) algorithm [9][10][11]. This model will be referred to as LoLiMoT-model in the following and its generation will be illustrated in the next section.…”

Section: Control Conceptmentioning

confidence: 99%

“…The predicted outputŷ k+i|k is corrected by the difference between measurement and model output at time k, i.e.ŷ k − y k . Constraints on the actuating signal u are given by (11). The rate of change of the actuating signal ∆u is limited by ∆u min and ∆u max , see (12)-(13).…”

Section: Model Predictive Controllermentioning

confidence: 99%

Model Predictive Control of Temperature and Humidity in Heating, Ventilating and Air Conditioning Systems

Rehrl

Schwingshackl

Horn

2014

IFIP Advances in Information and Communication Technology

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International audienceThe major application of heating, ventilating and air-conditioning (HVAC) systems is the simultaneous control of air temperature and air humidity. Therefore, in a typical industrial HVAC plant the following actuators are available: A cooling coil is used to decrease the air temperature and relative humidity by cooling below the dew point temperature. A steam humidifier is installed to increase the air humidity whereas the air temperature is influenced via a heating coil. Additionally, air temperature and humidity are affected by disturbances acting on the system. These disturbances include outer air temperature and humidity as well as the temperatures of hot water and cool water supply. Consequently, in the setup at hand, a plant with three manipulated inputs, four measurable disturbances and two controlled outputs has to be considered. A predictive control scheme based on a discrete time plant model is presented. The proposed controller computes the manipulated variables by solving an optimization problem at each time step. Simulation and measurement results obtained from an industrial HVAC system are shown

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“…Basierend auf dem dort entwickelten Modell wurde in [25] eine Regelung entworfen. Die Anwendung von LOLIMOT zur Identifikation und nichtlinearen, prädiktiven Regelung eines Wärmetauschers wird in [26] demonstriert.…”

Section: Ergänzungen Und Erweiterungenunclassified

LOLIMOT - Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme

Nelles¹

1997

at - Automatisierungstechnik

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Dipl.-Ing. Oliver Nelles ist wissenschaftlicher Mitarbeiter am Fachgebiet Regelsystemtechnik und Prozeßautomatisierung (Prof. Isermann) des Instituts für Regelungstechnik an der Technischen Hochschule Darmstadt. Sein Hauptarbeitsfeld ist die Identifikation von nichtlinearen, dynamischen und statischen Prozessen mittels neuronaler Netze und Fuzzy-Modellen. Adresse: In diesem Beitrag wird ein neues Verfahren namens LOLIMOT (local linear model tree) zur Identifikation nichtlinearer, dynamischer Systeme vorgestellt. Dieser Algorithmus basiert auf lokalen, linearen Modellen, die durch ein Basisfunktionen-Netz interpoliert werden. Ein Konstruktionsverfahren baut schrittweise eine Baumstruktur auf die sowohl Anzahl der Neuronen als auch Position und Breite der Basisfunktionen optimiert. Dabei wird der Eingangsraum durch Achsen-orthogonale Schnitte unterteilt. Die sich ergebende Struktur läßt sich alternativ als Baum, als lokales Basisfunktionen-Netz und als Sugeno-Takagi Fuzzy-System interpretieren. Das LOLIMOT-Verfahren ist einfach, leicht zu implementieren und trainiert sehr schnell Des weiteren weist es folgende wichtige Vorteile auf: es umgeht den sogenannten "Fluch der Dimensionalität'\ zeigt irrelevante und linear beeinflussende Eingänge auf und verwendet ein robustes, lokales, lineares Parameterschätzverfahren. Nachteile können sich aus der Beschränkung auf Achsen-orthogonale Schnitte und durch das Entstehen von Interpolationsfehlem infolge einer lokalen Schätzung ergeben.at -Automatisierungstechnik 45 (1997) 4 © R. Oldenbourg Verlag

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Nonlinear system identification and predictive control of a heat exchanger based on local linear fuzzy models

Cited by 24 publications

References 13 publications

Nonlinear System Identification

Nonlinear System Identification

Model Predictive Control of Temperature and Humidity in Heating, Ventilating and Air Conditioning Systems

LOLIMOT - Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme

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