Optimal Measurement Methods For Distributed Parameter System Identification


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Optimal Measurement Methods for Distributed Parameter System Identification


Optimal Measurement Methods for Distributed Parameter System Identification

Author: Dariusz Ucinski

language: en

Publisher: CRC Press

Release Date: 2004-08-27


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For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software systems. With the expense of scanning and moving sensors, optimal placement presents a critical problem.

Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems


Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems

Author: Marcin Witczak

language: en

Publisher: Springer Science & Business Media

Release Date: 2007-04-05


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This monograph presents a variety of techniques that can be used for designing robust fault diagnosis schemes for non-linear systems. The introductory part of the book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. Subsequently, advanced robust observer structures are presented. Parameter estimation based techniques are discussed as well. A particular attention is drawn to experimental design for fault diagnosis. The book also presents a number of robust soft computing approaches utilizing evolutionary algorithms and neural networks. All approaches described in this book are illustrated by practical applications.

Iterative Learning Control Algorithms and Experimental Benchmarking


Iterative Learning Control Algorithms and Experimental Benchmarking

Author: Eric Rogers

language: en

Publisher: John Wiley & Sons

Release Date: 2023-01-12


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Iterative Learning CONTROL ALGORITHMS AND EXPERIMENTAL BENCHMARKING Iterative Learning Control Algorithms and Experimental Benchmarking Presents key cutting edge research into the use of iterative learning control The book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. The book provides integrated coverage of the major approaches to-date in terms of basic systems, theoretic properties, design algorithms, and experimentally measured performance, as well as the links with repetitive control and other related areas. Key features: Provides comprehensive coverage of the main approaches to ILC and their relative advantages and disadvantages. Presents the leading research in the field along with experimental benchmarking results. Demonstrates how this approach can extend out from engineering to other areas and, in particular, new research into its use in healthcare systems/rehabilitation robotics. The book is essential reading for researchers and graduate students in iterative learning control, repetitive control and, more generally, control systems theory and its applications.