Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
Published on 30. April 2002
Book
Hardback
258 pages
978-0-89871-505-7 (ISBN)
Description
Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. This book brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems.
Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems.
Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
College/higher education
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 228 mm
Width: 152 mm
Thickness: 17 mm
Weight
635 gr
ISBN-13
978-0-89871-505-7 (9780898715057)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Content
Preface
Chapter 1: Background on Neural Networks and Fuzzy Logic Systems
Chapter 2: Background on Dynamical Systems and Industrial Actuators
Chapter 3: Neurocontrol of Systems with Friction
Chapter 4: Neural and Fuzzy Control of Systems with Deadzones
Chapter 5: Neural Control of Systems with Backlash
Chapter 6: Fuzzy Logic Control of Vehicle Active Suspension
Chapter 7: Neurocontrol Using the Adaptive Critic Architecture
Chapter 8: Neurocontrol of Telerobotic Systems with Time Delays
Chapter 9: Implementation of Neural Network Control Systems
Appendix A: C Code for Neural Network Friction Controller
Appendix B: C Code for Continuous-Time Neural Network Deadzone Controller
Appendix C: C Code for Discrete-Time Neural Network Backlash Controller
Appendix D: Versatile Real-Time Executive Code for Implementation of Neural Network Backstepping Controller on ATB1000 Tank Gun Barrel
References
Index.
Chapter 1: Background on Neural Networks and Fuzzy Logic Systems
Chapter 2: Background on Dynamical Systems and Industrial Actuators
Chapter 3: Neurocontrol of Systems with Friction
Chapter 4: Neural and Fuzzy Control of Systems with Deadzones
Chapter 5: Neural Control of Systems with Backlash
Chapter 6: Fuzzy Logic Control of Vehicle Active Suspension
Chapter 7: Neurocontrol Using the Adaptive Critic Architecture
Chapter 8: Neurocontrol of Telerobotic Systems with Time Delays
Chapter 9: Implementation of Neural Network Control Systems
Appendix A: C Code for Neural Network Friction Controller
Appendix B: C Code for Continuous-Time Neural Network Deadzone Controller
Appendix C: C Code for Discrete-Time Neural Network Backlash Controller
Appendix D: Versatile Real-Time Executive Code for Implementation of Neural Network Backstepping Controller on ATB1000 Tank Gun Barrel
References
Index.