Control-oriented System Identification: An H Approach (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) - Hardcover

Chen, Jie; Gu, Guoxiang

Hardcover

ISBN 10: 047132048X ISBN 13: 9780471320487

Verlag: John Wiley & Sons Inc, 2000

Inhaltsangabe

Identification in control theory is the process of obtaining a model of the object or process being controlled. It is used extensively in the area of robust control for the purpose of finding applicable models and, once identified, then validated or tested. Written by two of the leading researchers, this is the first book to cover this rapidly growing field.

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Über die Autorin bzw. den Autor

JIE CHEN, PhD, is Professor of Electrical Engineering at the University of California, Riverside. GUOXIANG GU, PhD, is Professor in the Department of Electrical and Computer Engineering at Louisiana State University, Baton Rouge.

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A comprehensive, one-stop reference for new system modeling and identification tools

The field of control-oriented identification has grown immensely over the past decade, spawning numerous results and modeling techniques and promising the potential to influence science and engineering for years to come. In this new work, Jie Chen and Guoxiang Gu, two leading authorities on worst-case identification, share their vision and walk readers through carefully selected topics from the vast literature, offering a much-needed, timely comprehensive introduction to the theory of H? identification and model validation.

Chen and Gu clearly demonstrate the pros and cons of the worst-case approach in comparison to traditional techniques and provide researchers in systems and control theory with ready access to many new and complementary identification tools. Through a rigorous yet logical and easy-to-follow treatment, supported by many deep insights, intuitions, and philosophical thinking, they:

• Survey and assess the current state of control and system identification research
• Develop both two-stage and interpolatory algorithms for system identification
• Show readers how to analyze the properties of linear algorithms
• Offer a unique emphasis on model uncertainty estimation and complexity, two of the central issues
• Develop both time-domain and frequency-domain identification algorithms
• Explain in detail uncertainty model validation concepts and techniques
• Devote a chapter to a review of the requisite mathematics

Provide a concise yet self-contained appendix on several key relevant notions

Details zur Ausgabe

• Verlag: John Wiley & Sons Inc
• Erscheinungsdatum: 2000
• ISBN 10: 047132048X
• ISBN 13: 9780471320487
• Einband: Tapa dura
• Sprache: Englisch
• Anzahl der Seiten: 444
• Kontakt zum Hersteller: Nicht verfügbar