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Control of Fuel Cell Power Systems / Jay T. Pukrushpan

Public ISBD Titre : Control of Fuel Cell Power Systems : principles, modeling, analysis and feedback design Type de document : texte imprimé Auteurs : Jay T. Pukrushpan, Auteur ; Anna G. Stefanopoulou, Auteur ; Huei Peng, Auteur Editeur : London : Springer-Verlag Année de publication : 2005 Collection : Advances in Industrial Control Importance : 161 p. Présentation : couv. ill. en coul., ill. Format : 24,1 cm. ISBN/ISSN/EAN : 978-1-85233-816-9 Langues : Anglais (eng) Catégories : LES ÉNERGIES Index. décimale : 21-03 Le pétrole Résumé : The problem of greenhouse gas (particularly carbon dioxide) release during power generation in fixed and mobile systems is widely acknowledged. Fuel cells are electrochemical devices offering clean and efficient energy production by the direct conversion of gaseous fuel into electricity. As such, they are under active study for commercial stationary power generation, residential applications and in transportation. The control of fuel cell systems under a variety of environmental conditions and over a wide operating range is a crucial factor in making them viable for extensive use in every-day technology. - An overview of the underlying physical principles and the main control objectives and difficulties associated with the implementation of fuel cell systems. - System-level dynamic models derived from the physical principles of the processes involved - Formulation, in-depth analysis and detailed control design for two critical control problems, namely, the control of the cathode oxygen supply for a high-pressure direct hydrogen fuel cell system and control of the anode hydrogen supply from a natural gas fuel processor system. - Multivariable controllers that attenuate restraints resulting from lack of sensor fidelity or actuator authority. - Real-time observers for stack variables that confer redundancy in fault detection processes. - Examples of the assistance of control analysis in fuel cell redesign and performance improvement. - Downloadable SIMULINK(R) model of a fuel cell for immediate use supplemented by sample MATLAB(R) files with which to run it and reproduce some of the book plots Primarily intended for researchers and students with a control background looking to expand their knowledge of fuel cell technology, Control of Fuel Cell Power Systems will also appeal to practicing fuel cell engineers through the simplicity of its models and the application of control algorithms in concrete case studies. The thorough coverage of control design will be of benefit to scientists dealing with the electrochemical, materials and fluid-dynamic aspects of fuel cells. Note de contenu : Table of Contents 1 Background and Introduction. - 2 Fuel Cell System Model: Auxiliary Components. - 3 Fuel Cell System Model: Fuel Cell Stack. - 4 Fuel Cell System Model: Analysis and Simulation. - 5 Air Flow Control for Fuel Cell Cathode Oxygen Reactant. - 6 Natural Gas Fuel Processor System Model. - 7 Control of Natural Gas Fuel Processor. - 8 Closing Remarks. - A Miscellaneous Equations, Tables, and Figures. - A.1 FCS Air Flow Control Design. - A.2 FPS Control Design. - References. Control of Fuel Cell Power Systems : principles, modeling, analysis and feedback design [texte imprimé] / Jay T. Pukrushpan, Auteur ; Anna G. Stefanopoulou, Auteur ; Huei Peng, Auteur . - London : Springer-Verlag, 2005 . - 161 p. : couv. ill. en coul., ill. ; 24,1 cm.. - (Advances in Industrial Control) . ISBN : 978-1-85233-816-9 Langues : Anglais (eng) Catégories : LES ÉNERGIES Index. décimale : 21-03 Le pétrole Résumé : The problem of greenhouse gas (particularly carbon dioxide) release during power generation in fixed and mobile systems is widely acknowledged. Fuel cells are electrochemical devices offering clean and efficient energy production by the direct conversion of gaseous fuel into electricity. As such, they are under active study for commercial stationary power generation, residential applications and in transportation. The control of fuel cell systems under a variety of environmental conditions and over a wide operating range is a crucial factor in making them viable for extensive use in every-day technology. - An overview of the underlying physical principles and the main control objectives and difficulties associated with the implementation of fuel cell systems. - System-level dynamic models derived from the physical principles of the processes involved - Formulation, in-depth analysis and detailed control design for two critical control problems, namely, the control of the cathode oxygen supply for a high-pressure direct hydrogen fuel cell system and control of the anode hydrogen supply from a natural gas fuel processor system. - Multivariable controllers that attenuate restraints resulting from lack of sensor fidelity or actuator authority. - Real-time observers for stack variables that confer redundancy in fault detection processes. - Examples of the assistance of control analysis in fuel cell redesign and performance improvement. - Downloadable SIMULINK(R) model of a fuel cell for immediate use supplemented by sample MATLAB(R) files with which to run it and reproduce some of the book plots Primarily intended for researchers and students with a control background looking to expand their knowledge of fuel cell technology, Control of Fuel Cell Power Systems will also appeal to practicing fuel cell engineers through the simplicity of its models and the application of control algorithms in concrete case studies. The thorough coverage of control design will be of benefit to scientists dealing with the electrochemical, materials and fluid-dynamic aspects of fuel cells. Note de contenu : Table of Contents 1 Background and Introduction. - 2 Fuel Cell System Model: Auxiliary Components. - 3 Fuel Cell System Model: Fuel Cell Stack. - 4 Fuel Cell System Model: Analysis and Simulation. - 5 Air Flow Control for Fuel Cell Cathode Oxygen Reactant. - 6 Natural Gas Fuel Processor System Model. - 7 Control of Natural Gas Fuel Processor. - 8 Closing Remarks. - A Miscellaneous Equations, Tables, and Figures. - A.1 FCS Air Flow Control Design. - A.2 FPS Control Design. - References.

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	N.Inventaire
415	21-03-01	Livre	Bibliothèque de Génie Electrique- USTO	Documentaires	Exclu du prêt	415

Strategies for Feedback Linearisation / Freddy Garces

Public ISBD Titre : Strategies for Feedback Linearisation : a dynamic neural network approach Type de document : texte imprimé Auteurs : Freddy Garces, Auteur ; Victor M. Becerra, Auteur ; Chandrasekhar Kambhampati, Auteur Editeur : London : Springer-Verlag Année de publication : 2003 Collection : Advances in Industrial Control Importance : 171 p. Présentation : couv. ill. en coul., ill. Format : 23,9 cm. ISBN/ISSN/EAN : 978-1-85233-501-4 Langues : Anglais (eng) Catégories : AUTOMATISME Mots-clés : Extension  control  feedback  identification  linearisation  mimo system  neural networks  non-linear identification  nonlinear control  nonlinear system  system  system identification Index. décimale : 25-04 Théorie des systèmes:systèmes asservis Résumé : The last few decades have witnessed tremendous developments in nonlinear control theory. One of the most important of these is the model-based method of feedback linearisation in which a nonlinear system is transformed into a linear system by means of state feedback and nonlinear transformations. After feedback linearisation, a system can be dealt with by linear controller design. The extension of these techniques to include MIMO systems allows for the further simplification of controller design by decoupling the system. Strategies for Feedback Linearisation demonstrates this powerful technique in the light of research on neural networks which allow the identification of nonlinear models without the complicated and costly development of models based on physical laws. Dynamic or recurrent neural networks have inherent properties that allow them to approximate nonlinear dynamic systems. Strategies for the identification of nonlinear systems using such neural networks are presented in this monograph together with the use of such models for the design and application of input-output linearisation and decoupling methods. Strategies for Feedback Linearisation is written to serve academic and industrial researchers in non-linear control and system identification and practising control engineers interested in their application to real-world industrial systems. The reader will gain a balanced view of theoretical and practical issues: relevant mathematical proofs are provided as are case studies illustrating design and application issues. Note de contenu : Table of Contents Chapter 1 Introduction 1.1 The Need for Nonlinear Control in Industrial Processes 1.2 Nonlinear Control Strategies 1.3 Nonlinear System Models: a Key Issue 1.4 Neural Networks 1.5 System Identification 1.6 Static Neural Networks for Identification and Control 1.7 Dynamic Neural Networks for Nonlinear Identification 1.8 Input-Output Linearisation-Decoupling and the Use of Dynamic Neural Networks 1.9 Potential applications Chapter 2 Fundamental Concepts 2.1 Elementary Concepts of Geometric Theory 2.2 Stability of Nonlinear Systems 2.3 Summary Chapter 3 Introduction To Feedback Linearisation 3.1 Nonlinear Control Affine Systems 3.2 Review of Other Linearisation Techniques 3.3 General Nonlinear Systems 3.4 Symbolic Algebra Software 3.5 Remarks 3.6 Summary Chapter 4 Dynamic Neural Networks 4.1 Introduction 4.2 Origins of Neural Computation 4.3 Single Layer Neural Network Structure 4.4 Static Multilayer Feedforward Networks 4.5 Dynamic Neural Networks (DNNs) 4.6 Training Dynamic Neural Networks 4.7 Validating the Dynamic Neural Models 4.8 A Training Example 4.9 Summary Chapter 5 Nonlinear System Approximation Using Dynamic Neural Networks 5.1 The Universal Approximation Property of Static Multilayer Networks 5.2 Dynamic Neural Network Structure 5.3 Approximation Ability of Dynamic Neural Networks 5.4 Summary Chapter 6 Feedback Linearisation Using Dynamic Neural Networks 6.1 Approximate Input-Output Linearisation of Control Affine Systems 6.2 Approximate Input-Output Linearisation for General Nonlinear Systems 6.3 Related Work 6.4 Summary Chapter 7 Case Studies 7.1 The Pressure Pilot Plant 7.2 Single Link Manipulator 7.3 Evaporator System 7.4 Summary REFERENCES Strategies for Feedback Linearisation : a dynamic neural network approach [texte imprimé] / Freddy Garces, Auteur ; Victor M. Becerra, Auteur ; Chandrasekhar Kambhampati, Auteur . - London : Springer-Verlag, 2003 . - 171 p. : couv. ill. en coul., ill. ; 23,9 cm.. - (Advances in Industrial Control) . ISBN : 978-1-85233-501-4 Langues : Anglais (eng) Catégories : AUTOMATISME Mots-clés : Extension  control  feedback  identification  linearisation  mimo system  neural networks  non-linear identification  nonlinear control  nonlinear system  system  system identification Index. décimale : 25-04 Théorie des systèmes:systèmes asservis Résumé : The last few decades have witnessed tremendous developments in nonlinear control theory. One of the most important of these is the model-based method of feedback linearisation in which a nonlinear system is transformed into a linear system by means of state feedback and nonlinear transformations. After feedback linearisation, a system can be dealt with by linear controller design. The extension of these techniques to include MIMO systems allows for the further simplification of controller design by decoupling the system. Strategies for Feedback Linearisation demonstrates this powerful technique in the light of research on neural networks which allow the identification of nonlinear models without the complicated and costly development of models based on physical laws. Dynamic or recurrent neural networks have inherent properties that allow them to approximate nonlinear dynamic systems. Strategies for the identification of nonlinear systems using such neural networks are presented in this monograph together with the use of such models for the design and application of input-output linearisation and decoupling methods. Strategies for Feedback Linearisation is written to serve academic and industrial researchers in non-linear control and system identification and practising control engineers interested in their application to real-world industrial systems. The reader will gain a balanced view of theoretical and practical issues: relevant mathematical proofs are provided as are case studies illustrating design and application issues. Note de contenu : Table of Contents Chapter 1 Introduction 1.1 The Need for Nonlinear Control in Industrial Processes 1.2 Nonlinear Control Strategies 1.3 Nonlinear System Models: a Key Issue 1.4 Neural Networks 1.5 System Identification 1.6 Static Neural Networks for Identification and Control 1.7 Dynamic Neural Networks for Nonlinear Identification 1.8 Input-Output Linearisation-Decoupling and the Use of Dynamic Neural Networks 1.9 Potential applications Chapter 2 Fundamental Concepts 2.1 Elementary Concepts of Geometric Theory 2.2 Stability of Nonlinear Systems 2.3 Summary Chapter 3 Introduction To Feedback Linearisation 3.1 Nonlinear Control Affine Systems 3.2 Review of Other Linearisation Techniques 3.3 General Nonlinear Systems 3.4 Symbolic Algebra Software 3.5 Remarks 3.6 Summary Chapter 4 Dynamic Neural Networks 4.1 Introduction 4.2 Origins of Neural Computation 4.3 Single Layer Neural Network Structure 4.4 Static Multilayer Feedforward Networks 4.5 Dynamic Neural Networks (DNNs) 4.6 Training Dynamic Neural Networks 4.7 Validating the Dynamic Neural Models 4.8 A Training Example 4.9 Summary Chapter 5 Nonlinear System Approximation Using Dynamic Neural Networks 5.1 The Universal Approximation Property of Static Multilayer Networks 5.2 Dynamic Neural Network Structure 5.3 Approximation Ability of Dynamic Neural Networks 5.4 Summary Chapter 6 Feedback Linearisation Using Dynamic Neural Networks 6.1 Approximate Input-Output Linearisation of Control Affine Systems 6.2 Approximate Input-Output Linearisation for General Nonlinear Systems 6.3 Related Work 6.4 Summary Chapter 7 Case Studies 7.1 The Pressure Pilot Plant 7.2 Single Link Manipulator 7.3 Evaporator System 7.4 Summary REFERENCES

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	N.Inventaire
113	25-04-06	Livre	Bibliothèque de Génie Electrique- USTO	Documentaires	Exclu du prêt	113

Modelling and control of mini-flying machines / Pedro Castillo

Public ISBD Titre : Modelling and control of mini-flying machines Type de document : texte imprimé Auteurs : Pedro Castillo, Auteur ; Rogelio Lozano, Auteur ; Alejandro E. Dzul, Auteur Editeur : London : Springer-Verlag Année de publication : 2005 Collection : Advances in Industrial Control Importance : 251 p. Présentation : couv. ill. en coul., ill. Format : 24 cm. ISBN/ISSN/EAN : 978-1-85233-957-9 Langues : Anglais (eng) Catégories : AUTOMATISME Mots-clés : Aerospace Engineering,Control,Control Applications,Control Engineering,Control Theory,Helicopters,Kalman Filtering,Lyapunov Analysis,Nonlinear Control,Performance,Sensor,algorithms,microcontroller Index. décimale : 25-06 Identification et simulation des processus Résumé : Problems in the motion control of aircraft are of perennial interest to the control engineer as they tend to be of complex and nonlinear nature. Modelling and Control of Mini-Flying Machines is an exposition of models developed for various types of mini-aircraft: • planar Vertical Take-off and Landing aircraft; • helicopters; • quadrotor mini-rotorcraft; • other fixed-wing aircraft; • blimps. For each of these it propounds: • detailed models derived from Euler-Lagrange methods; • appropriate nonlinear control strategies and convergence properties; • real-time experimental comparisons of the performance of control algorithms; • review of the principal sensors, on-board electronics, real-time architecture and communications systems for mini-flying machine control, including discussion of their performance; • detailed explanation of the use of the Kalman filter to flying machine localization. To researchers and students in nonlinear control and its applications Modelling and Control of Mini-Flying Machines provides valuable insights to the application of real-time nonlinear techniques in an always challenging area. Advances in Industrial Control aims to report and encourage 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. Note de contenu : Contents 1 Introduction and Historical Background 2 The PVTOL Aircraft 3 The Quad-rotor Rotorcraft 4 Robust Prediction-based Control for Unstable Delay Systems 5 Modelling and Control of Mini-helicopters 6 Helicopter in a Vertical Flying Stand 7 Modelling and Control of a Tandem-Wing Tail-Sitter UAV 8 Modelling and Control of Small Autonomous Airships 9 Sensors, Modems and Microcontrollers for UAVs Index Modelling and control of mini-flying machines [texte imprimé] / Pedro Castillo, Auteur ; Rogelio Lozano, Auteur ; Alejandro E. Dzul, Auteur . - London : Springer-Verlag, 2005 . - 251 p. : couv. ill. en coul., ill. ; 24 cm.. - (Advances in Industrial Control) . ISBN : 978-1-85233-957-9 Langues : Anglais (eng) Catégories : AUTOMATISME Mots-clés : Aerospace Engineering,Control,Control Applications,Control Engineering,Control Theory,Helicopters,Kalman Filtering,Lyapunov Analysis,Nonlinear Control,Performance,Sensor,algorithms,microcontroller Index. décimale : 25-06 Identification et simulation des processus Résumé : Problems in the motion control of aircraft are of perennial interest to the control engineer as they tend to be of complex and nonlinear nature. Modelling and Control of Mini-Flying Machines is an exposition of models developed for various types of mini-aircraft: • planar Vertical Take-off and Landing aircraft; • helicopters; • quadrotor mini-rotorcraft; • other fixed-wing aircraft; • blimps. For each of these it propounds: • detailed models derived from Euler-Lagrange methods; • appropriate nonlinear control strategies and convergence properties; • real-time experimental comparisons of the performance of control algorithms; • review of the principal sensors, on-board electronics, real-time architecture and communications systems for mini-flying machine control, including discussion of their performance; • detailed explanation of the use of the Kalman filter to flying machine localization. To researchers and students in nonlinear control and its applications Modelling and Control of Mini-Flying Machines provides valuable insights to the application of real-time nonlinear techniques in an always challenging area. Advances in Industrial Control aims to report and encourage 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. Note de contenu : Contents 1 Introduction and Historical Background 2 The PVTOL Aircraft 3 The Quad-rotor Rotorcraft 4 Robust Prediction-based Control for Unstable Delay Systems 5 Modelling and Control of Mini-helicopters 6 Helicopter in a Vertical Flying Stand 7 Modelling and Control of a Tandem-Wing Tail-Sitter UAV 8 Modelling and Control of Small Autonomous Airships 9 Sensors, Modems and Microcontrollers for UAVs Index

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	N.Inventaire
976	25-06-18	Livre	Bibliothèque de Génie Electrique- USTO	Documentaires	Disponible	976