Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
Part of Frontiers in Applied Mathematics
- Authors:
- F. L. Lewis, University of Texas, Arlington
- J. Campos, Universidad de Zaragoza
- R. Selmic, Louisiana Tech University
- Date Published: April 2002
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial null Mathematics for availability.
- format: Hardback
- isbn: 9780898715057
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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. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities 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.
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×Product details
- Date Published: April 2002
- format: Hardback
- isbn: 9780898715057
- length: 258 pages
- dimensions: 261 x 181 x 17 mm
- weight: 0.635kg
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial null Mathematics for availability.
Table of Contents
Preface
1. Background on Neural Networks and Fuzzy Logic Systems
2. Background on Dynamical Systems and Industrial Actuators
3. Neurocontrol of Systems with Friction
4. Neural and Fuzzy Control of Systems with Deadzones
5. Neural Control of Systems with Backlash
6. Fuzzy Logic Control of Vehicle Active Suspension
7. Neurocontrol Using the Adaptive Critic Architecture
8. Neurocontrol of Telerobotic Systems with Time Delays
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.
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