Lan Jianglin (28 results)

Condition: New. In.

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Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control is a systematic examination of methods used to overcome the inevitable system uncertainties arising when a fault estimation (FE) function and a fault-tolerant controller interact as they are employed together to compensate for system faults and maintain robustly acceptable system performance. It covers the important subject of robust integration of FE and FTC with the aim of guaranteeing closed-loop stability. The reader's understanding of the theory is supported by the extensive use of tutorial examples, including some MATLAB®-based material available from the Springer website and by industrial-applications-based material.The text is structured into three parts:Part I examines the basic concepts of FE and FTC, providing extensive insight into the importance of and challenges involved in their integration;Part II describes five effective strategiesfor the integration of FE and FTC: sequential, iterative, simultaneous, adaptive-decoupling, and robust decoupling; andPart III begins to extend the proposed strategies to nonlinear and large-scale systems and covers their application in the fields of renewable energy, robotics and networked systems. The strategies presented are applicable to a broad range of control problems, because in the absence of faults the FE-based FTC naturally reverts to conventional observer-based control.The book is a useful resource for researchers and engineers working in the area of fault-tolerant control systems, and supplementary material for a graduate- or postgraduate-level course on fault diagnosis and FTC.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control is a systematic examination of methods used to overcome the inevitable system uncertainties arising when a fault estimation (FE) function and a fault-tolerant controller interact as they are employed together to compensate for system faults and maintain robustly acceptable system performance. It covers the important subject of robust integration of FE and FTC with the aim of guaranteeing closed-loop stability. The reader's understanding of the theory is supported by the extensive use of tutorial examples, including some MATLAB®-based material available from the Springer website and by industrial-applications-based material.The text is structured into three parts:Part I examines the basic concepts of FE and FTC, providing extensive insight into the importance of and challenges involved in their integration;Part II describes five effective strategiesfor the integration of FE and FTC: sequential, iterative, simultaneous, adaptive-decoupling, and robust decoupling; andPart III begins to extend the proposed strategies to nonlinear and large-scale systems and covers their application in the fields of renewable energy, robotics and networked systems. The strategies presented are applicable to a broad range of control problems, because in the absence of faults the FE-based FTC naturally reverts to conventional observer-based control.The book is a useful resource for researchers and engineers working in the area of fault-tolerant control systems, and supplementary material for a graduate- or postgraduate-level course on fault diagnosis and FTC.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.…

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Taschenbuch. Condition: Neu. Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control | Jianglin Lan (u. a.) | Taschenbuch | Advances in Industrial Control | xvi | Englisch | 2021 | Springer | EAN 9783030587628 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Condition: New. Dr. Jianglin Lan is a Lecturer in Autonomous Systems and Leverhulme Early Career Fellow at the James Watt School of Engineering, University of Glasgow, and an Honorary Research Fellow at Imperial College London. He received his PhD in Control and Intelligen.

Buch. Condition: Neu. Neuware - This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application.…

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Hardcover. Condition: new. Hardcover. This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application. This book bridges control theory and data-driven learning for designing adaptive, safe, and reliable autonomous systems. It covers advanced control strategies and runtime monitoring with case studies connecting theory to practice. Ideal for students, researchers, and practitioners in control systems, robotics, AI, and engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control is a systematic examination of methods used to overcome the inevitable system uncertainties arising when a fault estimation (FE) function and a fault-tolerant controller.…

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. MATLAB examples on authors website|Provides a systematic guide to the robust integration of model-based fault estimation and fault-tolerant controlOffers extensive insight into problem formulation and strategy designShowcases useful mathem.…

Hardcover. Condition: new. Hardcover. This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application. This book bridges control theory and data-driven learning for designing adaptive, safe, and reliable autonomous systems. It covers advanced control strategies and runtime monitoring with case studies connecting theory to practice. Ideal for students, researchers, and practitioners in control systems, robotics, AI, and engineering. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control is a systematic examination of methods used to overcome the inevitable system uncertainties arising when a fault estimation (FE) function and a fault-tolerant controller interact as they are employed together to compensate for system faults and maintain robustly acceptable system performance. It covers the important subject of robust integration of FE and FTC with the aim of guaranteeing closed-loop stability. The reader¿s understanding of the theory is supported by the extensive use of tutorial examples, including some MATLAB®-based material available from the Springer website and by industrial-applications-based material.The text is structured into three parts:Part I examines the basic concepts of FE and FTC, providing extensive insight into the importance of and challenges involved in their integration;Part II describes five effective strategiesfor the integration of FE and FTC: sequential, iterative, simultaneous, adaptive-decoupling, and robust decoupling; andPart III begins to extend the proposed strategies to nonlinear and large-scale systems and covers their application in the fields of renewable energy, robotics and networked systems.The strategies presented are applicable to a broad range of control problems, because in the absence of faults the FE-based FTC naturally reverts to conventional observer-based control.The book is a useful resource for researchers and engineers working in the area of fault-tolerant control systems, and supplementary material for a graduate- or postgraduate-level course on fault diagnosis and FTC.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 284 pp. Englisch. …

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control is a systematic examination of methods used to overcome the inevitable system uncertainties arising when a fault estimation (FE) function and a fault-tolerant controller interact as they are employed together to compensate for system faults and maintain robustly acceptable system performance. It covers the important subject of robust integration of FE and FTC with the aim of guaranteeing closed-loop stability. The reader¿s understanding of the theory is supported by the extensive use of tutorial examples, including some MATLAB®-based material available from the Springer website and by industrial-applications-based material.The text is structured into three parts:Part I examines the basic concepts of FE and FTC, providing extensive insight into the importance of and challenges involved in their integration;Part II describes five effective strategiesfor the integration of FE and FTC: sequential, iterative, simultaneous, adaptive-decoupling, and robust decoupling; andPart III begins to extend the proposed strategies to nonlinear and large-scale systems and covers their application in the fields of renewable energy, robotics and networked systems.The strategies presented are applicable to a broad range of control problems, because in the absence of faults the FE-based FTC naturally reverts to conventional observer-based control.The book is a useful resource for researchers and engineers working in the area of fault-tolerant control systems, and supplementary material for a graduate- or postgraduate-level course on fault diagnosis and FTC.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 284 pp. Englisch. …

Hardcover. Condition: new. Hardcover. This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application. This book bridges control theory and data-driven learning for designing adaptive, safe, and reliable autonomous systems. It covers advanced control strategies and runtime monitoring with case studies connecting theory to practice. Ideal for students, researchers, and practitioners in control systems, robotics, AI, and engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…