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9783642138119 364213811X Intends to bridge the gap between Linear Parameter-Varying (LPV) modeling and control by investigating fundamental questions of modeling and identification. This book explores missing details of LPV system theory that have hindered the formulation of a well established identification framework., This book aims to bridge the gap between Linear Parameter-Varying (LPV) modeling and control by investigating fundamental questions of modeling and identification. It explores missing details of LPV system theory that have hindered the formulation of a well established identification framework. By proposing a unified LPV system theory, based on a behavioral approach, the concepts of representations, equivalence transformations and means to compare model structures are re-established, giving a solid basis for an identification theory. It is also explored when and how first-principle nonlinear models can be efficiently converted to LPV descriptions detailing possible pitfalls. Building on well-founded system theoretical concepts, the classical LTI prediction-error framework is extended to the LPV case via the use of series-expansion representations. The book is written as a research monograph with a broad scope, trying to cover the key issues from system theory to modeling and identification. It is meant to be interesting for both researchers and engineers but also for graduate students in systems and control who would like to learn about the LPV framework., This book aims to bridge the gap between modeling and control by investigating the fundamental questions of LPV modeling and identification. It explores the missing details of the LPV system theory that have hindered the formulation of a well established identification framework. By proposing an unified LPV system theory that is based on a behavioral approach, the concepts of representations, equivalence transformations, and means to compare model structures are re-established, giving a solid basis for an identification theory. It is also explored when and how first-principle nonlinear models can be efficiently converted to LPV descriptions and what are the pitfalls that must be avoided. Building on well founded system theoretical concepts, the classical LTI prediction-error framework is extended to the LPV case via the use of series-expansion representations.The book is written as a research monograph with a broad scope, trying to cover the key issues from system theory to modeling and identification. It is meant to be interesting for both researchers and engineers but also for graduate students in systems and control who would like to learn about the LPV framework. It also offers an easy to use guide for engineers about the off-shelf solutions in LPV modeling and identification., This series aims to report new developments in the fields of control and information sciences - quickly, informally and at a high level. The type of material considered for publication includes: 1. Preliminary drafts of monographs and advanced textbooks 2.Lectures on a new field, or presenting a new angle on a classical field 3. Research reports 4. Reports of meetings, provided they are a) of exceptional interest and b) devoted to a specific topic. The timeliness of subject material is very important. Publication in LNCIS is free of charge. Springer-Verlag retains copyright. However, all authors are free to use the material in other publications, with acknowledgment to Springer-Verlag. Manuscripts should be written in English and be no less than 100, preferably no more than 500 pages. The manuscript in its final and approved version must be submitted in camera-ready form. You are strongly encouraged to use LATEX together with the corresponding Springer LATEX macro packages. The corresponding electronic files are also required for the production process, in particular the online version. Detailed instructions for authors can be found on the engineering site of our homepage: springer.com/series/642. Manuscripts should be sent to one of the series editors, Professor Dr.-Ing. M. Thoma, Institut für Regelungstechnik, Technische Universität, Appelstra'e 11, 30167 Hannover, Germany, Professor Frank Allgöwer, Universität Stuttgart, Inst. Systemtheorie Technischer Prozesse (IST), Pfaffenwaldring 9, 70550 Stuttgart, Germany or Professor M. Morari, Institut für Automatik, ETH/ETL I 29, Physikstra'e 3,8092 Zürich, Switzerland, or directly to the Engineering Editor, Springer-Verlag, Tiergartenstra'e 17, 69121 Heidelberg, Germany. Book jacket., Through the past 20 years, the framework of Linear Parameter-Varying (LPV) systems has become a promising system theoretical approach to h- dle the controlof mildly nonlinear and especially position dependent systems which are common in mechatronic applications and in the process ind- try. The birth of this system class was initiated by the need of engineers to achieve better performance for nonlinear and time-varying dynamics, c- mon in many industrial applications, than what the classical framework of Linear Time-Invariant (LTI) control can provide. However, it was also a p- mary goal to preserve simplicity and re-use the powerful LTI results by extending them to the LPV case. The progress continued according to this philosophy and LPV control has become a well established ?eld with many promising applications. Unfortunately, modeling of LPV systems, especially based on measured data (which is called system identi'cation) has seen a limited development sincethebirthoftheframework. Currentlythisbottleneck oftheLPVfra- work is halting the transfer of the LPV theory into industrial use. Without good models that ful'll the expectations of the users and without the und- standing how these models correspond to the dynamics of the application, it is di'cult to design high performance LPV control solutions. This book aims to bridge the gap between modeling and control by investigating the fundamental questions of LPV modeling and identi'cation. It explores the missing details of the LPV system theory that have hindered the formu- tion of a well established identi'cation framework."
9783642138119 364213811X Intends to bridge the gap between Linear Parameter-Varying (LPV) modeling and control by investigating fundamental questions of modeling and identification. This book explores missing details of LPV system theory that have hindered the formulation of a well established identification framework., This book aims to bridge the gap between Linear Parameter-Varying (LPV) modeling and control by investigating fundamental questions of modeling and identification. It explores missing details of LPV system theory that have hindered the formulation of a well established identification framework. By proposing a unified LPV system theory, based on a behavioral approach, the concepts of representations, equivalence transformations and means to compare model structures are re-established, giving a solid basis for an identification theory. It is also explored when and how first-principle nonlinear models can be efficiently converted to LPV descriptions detailing possible pitfalls. Building on well-founded system theoretical concepts, the classical LTI prediction-error framework is extended to the LPV case via the use of series-expansion representations. The book is written as a research monograph with a broad scope, trying to cover the key issues from system theory to modeling and identification. It is meant to be interesting for both researchers and engineers but also for graduate students in systems and control who would like to learn about the LPV framework., This book aims to bridge the gap between modeling and control by investigating the fundamental questions of LPV modeling and identification. It explores the missing details of the LPV system theory that have hindered the formulation of a well established identification framework. By proposing an unified LPV system theory that is based on a behavioral approach, the concepts of representations, equivalence transformations, and means to compare model structures are re-established, giving a solid basis for an identification theory. It is also explored when and how first-principle nonlinear models can be efficiently converted to LPV descriptions and what are the pitfalls that must be avoided. Building on well founded system theoretical concepts, the classical LTI prediction-error framework is extended to the LPV case via the use of series-expansion representations.The book is written as a research monograph with a broad scope, trying to cover the key issues from system theory to modeling and identification. It is meant to be interesting for both researchers and engineers but also for graduate students in systems and control who would like to learn about the LPV framework. It also offers an easy to use guide for engineers about the off-shelf solutions in LPV modeling and identification., This series aims to report new developments in the fields of control and information sciences - quickly, informally and at a high level. The type of material considered for publication includes: 1. Preliminary drafts of monographs and advanced textbooks 2.Lectures on a new field, or presenting a new angle on a classical field 3. Research reports 4. Reports of meetings, provided they are a) of exceptional interest and b) devoted to a specific topic. The timeliness of subject material is very important. Publication in LNCIS is free of charge. Springer-Verlag retains copyright. However, all authors are free to use the material in other publications, with acknowledgment to Springer-Verlag. Manuscripts should be written in English and be no less than 100, preferably no more than 500 pages. The manuscript in its final and approved version must be submitted in camera-ready form. You are strongly encouraged to use LATEX together with the corresponding Springer LATEX macro packages. The corresponding electronic files are also required for the production process, in particular the online version. Detailed instructions for authors can be found on the engineering site of our homepage: springer.com/series/642. Manuscripts should be sent to one of the series editors, Professor Dr.-Ing. M. Thoma, Institut für Regelungstechnik, Technische Universität, Appelstra'e 11, 30167 Hannover, Germany, Professor Frank Allgöwer, Universität Stuttgart, Inst. Systemtheorie Technischer Prozesse (IST), Pfaffenwaldring 9, 70550 Stuttgart, Germany or Professor M. Morari, Institut für Automatik, ETH/ETL I 29, Physikstra'e 3,8092 Zürich, Switzerland, or directly to the Engineering Editor, Springer-Verlag, Tiergartenstra'e 17, 69121 Heidelberg, Germany. Book jacket., Through the past 20 years, the framework of Linear Parameter-Varying (LPV) systems has become a promising system theoretical approach to h- dle the controlof mildly nonlinear and especially position dependent systems which are common in mechatronic applications and in the process ind- try. The birth of this system class was initiated by the need of engineers to achieve better performance for nonlinear and time-varying dynamics, c- mon in many industrial applications, than what the classical framework of Linear Time-Invariant (LTI) control can provide. However, it was also a p- mary goal to preserve simplicity and re-use the powerful LTI results by extending them to the LPV case. The progress continued according to this philosophy and LPV control has become a well established ?eld with many promising applications. Unfortunately, modeling of LPV systems, especially based on measured data (which is called system identi'cation) has seen a limited development sincethebirthoftheframework. Currentlythisbottleneck oftheLPVfra- work is halting the transfer of the LPV theory into industrial use. Without good models that ful'll the expectations of the users and without the und- standing how these models correspond to the dynamics of the application, it is di'cult to design high performance LPV control solutions. This book aims to bridge the gap between modeling and control by investigating the fundamental questions of LPV modeling and identi'cation. It explores the missing details of the LPV system theory that have hindered the formu- tion of a well established identi'cation framework."