| Titre : | Intelligent Systems and Control : principles and applications | | Type de document : | texte imprimé | | Auteurs : | Laxmidhar Behera, Auteur ; Indrani Kar, Auteur | | Editeur : | India : Oxford University Press | | Année de publication : | 2009 | | Importance : | 373 p. | | Présentation : | couv. ill. en coul., ill. | | Format : | 24,1 cm. | | Accompagnement : | CD-ROM | | ISBN/ISSN/EAN : | 978-0-19-806315-5 | | Langues : | Anglais (eng) | | Catégories : | AUTOMATISME
| | Index. décimale : | 25-07 Théorie de la commande: commandes des processus | | Résumé : | Intelligent Systems and Control: Principles and Applications is a textbook for undergraduate level courses on intelligent control, intelligent systems, adaptive control, and non-linear control. The book covers primers in neural networks, fuzzy logic, and non-linear control so that readers can
easily follow intelligent control techniques. Design principles for fuzzy and neural control schemes have been enumerated with an easy understanding for readers. Stability analysis of control systems have been provided with rigour. Intelligent control systems have been simulated for benchmark
non-linear systems across disciplines such as electrical system, electro-mechanical systems, and process control systems. Details of real-time experiments for cart-pole inverted pendulum system and seven degrees of freedom (DOF) robot manipulator using intelligent control schemes have been included
in the book to illustrate efficacy of these advanced control schemes. A chapter on quantum neural networks and its application has been included to illustrate the importance of the emerging research in quantum computational intelligence in control. Many examples with Matlab codes have been provided
for readers to comprehend the subject matter provided in this book.
Each chapter includes a set of exercise problems for readers to get well-versed with the subject. C-codes for selected exercise problems have been included in the CD accompanying the book. Simulation results and experimental videos are also included in the CD. This book can be used as a reference
for courses such as Artificial Neural Networks and Fuzzy Logic, Artificial Intelligence, Instrumentation and Control, and Advanced Control Systems. Also practicing engineers in R&D sectors will be greatly benefitted from this book. | | Note de contenu : | Table of contents
Chapter 1. Non-linear Control: Primer
1.4 Continuous time state-Space model
1.5 Non-linear state-space model
1.6 Lyapunov stability theory
1.8 Modelling of different non-linear systems
Chapter 2. Neural Networks
2.1 Feed-forward networks
2.2 Multi-Layered neural networks
2.3 Radial basics function networks
2.4 Adaptive learning rate
Chapter 3. Fuzzy Logic
3.2 Fuzzy Sets
3.3 Fuzzy rule base and approximate reasoning
3.4 Fuzzy logic control
Chapter 4. Indirect Adaptive Control Using Neural Networks
4.1 Continuous time affine systems
4.2 Discrete time affine systems
4.3 Discrete time non-affine systems
Chapter 5. Direct Adaptive Control Using Neural Networks
5.1 Direct adaptive control
5.2 Single Input Single output affine systems
5.3 Multi-input multi-output systems
5.4 Single Input Single output discrete time affine systems
Chapter 6. Approximate Dynamic Programming
6.1 Linear quadratic regulator
6.2 The HJB formulation
6.3 HJB for affine systems
6.4 HDP and DHP
6.5 Single network adaptive critic
6.6 Continuous time adaptive critic
6.7 Adaptive critic using the T-S Fuzzy Model
Chapter 7. Fuzzy Logic Control
7.1 Construction of an FLC
7.2 fuzzy PD controller
7.3 fuzzy PI controller
7.4 fuzzy PI controller for a series DC motor
7.5 FLC using Lyapunov synthesis
7.6 Horizontal planar two link robot manipulator
Chapter 8. Takagi–Sugeno Fuzzy Model Based Control
8.1 T-S Fuzzy model
8.2 Linear matrix inequality technique
8.3 Fixed gain state feedback controller design technique
8.5 Variable gain controller design using each linear
8.6 Controller design using discrete T-S fuzzy system
Chapter 9. Intelligent Control of a Pendulum on a Cart
9.1 T-S fuzzy model representation
9.2 Control using the T-S fuzzy model
9.3 network inversion based control
9.5 Cart-pole system: simulation and experiment
Chapter 10. Visual Motor Control of a Redundant Manipulator
10.1 System model
10.2 Visual motor control using neural networks
10.3 Visual motor control using a fuzzy network
Appendix List of C programs on CD |
Intelligent Systems and Control : principles and applications [texte imprimé] / Laxmidhar Behera, Auteur ; Indrani Kar, Auteur . - India : Oxford University Press, 2009 . - 373 p. : couv. ill. en coul., ill. ; 24,1 cm. + CD-ROM. ISBN : 978-0-19-806315-5 Langues : Anglais ( eng) | Catégories : | AUTOMATISME
| | Index. décimale : | 25-07 Théorie de la commande: commandes des processus | | Résumé : | Intelligent Systems and Control: Principles and Applications is a textbook for undergraduate level courses on intelligent control, intelligent systems, adaptive control, and non-linear control. The book covers primers in neural networks, fuzzy logic, and non-linear control so that readers can
easily follow intelligent control techniques. Design principles for fuzzy and neural control schemes have been enumerated with an easy understanding for readers. Stability analysis of control systems have been provided with rigour. Intelligent control systems have been simulated for benchmark
non-linear systems across disciplines such as electrical system, electro-mechanical systems, and process control systems. Details of real-time experiments for cart-pole inverted pendulum system and seven degrees of freedom (DOF) robot manipulator using intelligent control schemes have been included
in the book to illustrate efficacy of these advanced control schemes. A chapter on quantum neural networks and its application has been included to illustrate the importance of the emerging research in quantum computational intelligence in control. Many examples with Matlab codes have been provided
for readers to comprehend the subject matter provided in this book.
Each chapter includes a set of exercise problems for readers to get well-versed with the subject. C-codes for selected exercise problems have been included in the CD accompanying the book. Simulation results and experimental videos are also included in the CD. This book can be used as a reference
for courses such as Artificial Neural Networks and Fuzzy Logic, Artificial Intelligence, Instrumentation and Control, and Advanced Control Systems. Also practicing engineers in R&D sectors will be greatly benefitted from this book. | | Note de contenu : | Table of contents
Chapter 1. Non-linear Control: Primer
1.4 Continuous time state-Space model
1.5 Non-linear state-space model
1.6 Lyapunov stability theory
1.8 Modelling of different non-linear systems
Chapter 2. Neural Networks
2.1 Feed-forward networks
2.2 Multi-Layered neural networks
2.3 Radial basics function networks
2.4 Adaptive learning rate
Chapter 3. Fuzzy Logic
3.2 Fuzzy Sets
3.3 Fuzzy rule base and approximate reasoning
3.4 Fuzzy logic control
Chapter 4. Indirect Adaptive Control Using Neural Networks
4.1 Continuous time affine systems
4.2 Discrete time affine systems
4.3 Discrete time non-affine systems
Chapter 5. Direct Adaptive Control Using Neural Networks
5.1 Direct adaptive control
5.2 Single Input Single output affine systems
5.3 Multi-input multi-output systems
5.4 Single Input Single output discrete time affine systems
Chapter 6. Approximate Dynamic Programming
6.1 Linear quadratic regulator
6.2 The HJB formulation
6.3 HJB for affine systems
6.4 HDP and DHP
6.5 Single network adaptive critic
6.6 Continuous time adaptive critic
6.7 Adaptive critic using the T-S Fuzzy Model
Chapter 7. Fuzzy Logic Control
7.1 Construction of an FLC
7.2 fuzzy PD controller
7.3 fuzzy PI controller
7.4 fuzzy PI controller for a series DC motor
7.5 FLC using Lyapunov synthesis
7.6 Horizontal planar two link robot manipulator
Chapter 8. Takagi–Sugeno Fuzzy Model Based Control
8.1 T-S Fuzzy model
8.2 Linear matrix inequality technique
8.3 Fixed gain state feedback controller design technique
8.5 Variable gain controller design using each linear
8.6 Controller design using discrete T-S fuzzy system
Chapter 9. Intelligent Control of a Pendulum on a Cart
9.1 T-S fuzzy model representation
9.2 Control using the T-S fuzzy model
9.3 network inversion based control
9.5 Cart-pole system: simulation and experiment
Chapter 10. Visual Motor Control of a Redundant Manipulator
10.1 System model
10.2 Visual motor control using neural networks
10.3 Visual motor control using a fuzzy network
Appendix List of C programs on CD |
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