Prädiktive Regelung eines Wärmetauschers auf der Basis on-line adaptierter Fuzzy-Modelle

Fischer, Martin; Fink, Alexander; Hecker, Oliver; Pfeiffer, Bernd‐Markus; Kofahl, Rüdiger; Schumann, R.

doi:10.1524/auto.1998.46.9.426

Cited by 5 publications

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“…A complete gray box modeling and identification approach with the utilization of first principles, expert knowledge, and data is realized in [89]. • Based on these models, various predictive control concepts have been developed for temperature control of a cross-flow heat exchanger [87,93]. Moreover, sophisticated online model adaptation strategies were designed in order to make the nonlinear predictive controller adaptive [84,92,94,96,97].…”

Section: Discussionmentioning

confidence: 99%

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Nonlinear System Identification

Nelles

2001

1,086

447

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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%

“…As an illustrative application example, the thermal plant presented in Sect. The adaptive predictive control of the cross-flow heat exchanger can be found in [87,94]. Section 24.1 discusses the design of a predictive controller based on a local linear neurofuzzy model.…”

Section: Applications Of Advanced Methodsmentioning

confidence: 99%