This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different approaches such as optimization methods for input/output LPV models Identification, set membership methods, optimization methods and subspace methods for state-space LPV models identification and orthonormal basis functions methods. Since there is a strong connection between LPV systems, hybrid switching systems and piecewise affine models, identification of hybrid switching systems and piecewise affine systems will be considered as well.
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This review volume reports the state-of-the-art in Linear Parameter Varying (Lpv) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent Lpv identification methods for both discrete-time and continuous-time models, using different approaches such as optimization methods for input/output Lpv models Identification, set membership methods, optimization methods and subspace methods for state-space Lpv models identification and orthonormal basis functions methods. Since there is a strong connection between Lpv systems, hybrid switching systems and piecewise affine models, identification of hybrid switching systems and piecewise affine systems will be considered as well.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : Near Fine. 1st Edition. Hardcover, xv + 385 pages, NOT ex-library. Book looks unread, clean and bright throughout with unmarked text, free of inscriptions and stamps, firmly bound. Issued without a dust jacket. -- This book provides an overview of the Linear Parameter-Varying (LPV) framework, a sophisticated approach to modelling and controlling systems that change their behaviour based on external variables. It acts as a bridge between simple linear systems and complex non-linear ones, offering tools to identify these systems from experimental data. The LPV approach assumes that a system's dynamics are linear, but its parameters (such as gain or time constants) vary as a function of an independent, measurable signal called the scheduling variable. This allows engineers to model non-linear systems - such as aircraft with changing altitudes or chemical reactors with varying temperatures - using extended linear techniques. The volume highlights several modern advancements in the field: - Subspace Identification Methods: A significant portion of the text focuses on subspace algorithms, which are particularly effective for identifying multi-variable (MIMO) LPV systems directly from data without needing complex initial guesses; - Input-Output vs. State-Space Models: The authors discuss the trade-offs between identifying input-output representations (simpler to calculate) versus state-space models (more useful for advanced control design); - Frequency Domain Techniques: It explores how frequency response data can be used to identify LPV models, a traditional method in aerospace engineering; - Basis Function Selection: The book addresses the critical challenge of choosing the right mathematical "basis functions" to describe how parameters vary, ensuring the model remains accurate across the entire operating range. Practical Applications: The methods described are essential for industries dealing with "varying-regime" operations: - Aerospace: Modelling flight dynamics across different Mach numbers and altitudes; - Automotive: Designing active suspension or engine control systems that adapt to vehicle speed; - Process Control: Managing industrial plants where the chemical properties of a product change during the manufacturing cycle. The book is a collaborative effort by international experts, including Paulo Lopes dos Santos and Daniel E. Rivera, combining academic rigour with insights into practical implementation. It serves as a high-level reference for postgraduate students and research engineers in automatic control and signal processing. N° de réf. du vendeur 012706
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