Solving Fault Diagnosis Problems / Studies in Systems, Decision and Control Bd.482 (PDF)
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The model-based approach to fault detection and diagnosis has been the subject of continuing research for several decades. While the theoretical aspects of the fault diagnosis relying on linear models are well understood, most of the proposed computational methods for the synthesis of fault detection and isolation filters are not satisfactory from a numerical point of view. Therefore, the aim of this book to address the fault detection and isolation topics from a computational perspective, contrasts with most existing literature. This book is an attempt to close the gap between the existing well developed theoretical results and the realm of reliable computational synthesis procedures.
Several features make this book unique in the fault detection literature:
· solution of standard synthesis problems in the most general setting, for both continuous- and discrete-time systems, regardless they are proper or not; consequently, the proposed synthesis procedures can solvea specific problem whenever a solution exists
· emphasis on the best numerical algorithms to solve the synthesis problems for linear systems in generalized state-space form (also known as descriptor systems).
· development of general synthesis procedures relying on new computational paradigms, as factorization-based design based on filter updating techniques and nullspace-based synthesis
· availability of a comprehensive set of free supporting software tools implemented in the Julia language, which ensures the reproducibility of the results of all presented synthesis examples.
This book is primarily aimed at researchers and advanced graduate students in the areas of fault diagnosis and fault tolerant control. It will also appeal to mathematicians with interests in control oriented numerics.
- Autor: Andreas Varga
- 2024, 2nd ed. 2023, 442 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3031357671
- ISBN-13: 9783031357671
- Erscheinungsdatum: 01.02.2024
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- Größe: 5.72 MB
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