| 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) | | 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) | 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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