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DIFFERENTIAL NEURAL NETWORKS FOR ROBUST NONLINEAR CONTROL
Identification, State Estimation and Trajectory Tracking
by Alexander S Poznyak, Edgar N Sanchez & Wen Yu (CINVESTAV-IPN, Mexico)
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.).
Contents:
- Theoretical Study:
- Neural Networks Structures
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Nonlinear System Identification: Differential Learning
- Sliding Mode Identification: Algebraic Learning
- Neural State Estimation
- Passivation via Neuro Control
- Neuro Trajectory Tracking
- Neurocontrol Applications:
- Neural Control for Chaos
- Neuro Control for Robot Manipulators
- Identification of Chemical Processes
- Neuro Control for Distillation Column
- General Conclusions and Future Work
- Appendices:
- Some Useful Mathematical Facts
- Elements of Qualitative Theory of ODE
- Locally Optimal Control and Optimization
Readership: Graduate students, researchers, academics/lecturers and
industrialists in neural networks.
"This book is the result of many years of research and publications by the authors. Overall, it is a good one that could benefit the researchers and practitioners in the field of intelligent nonlinear control systems. Design methods and analytical results are well presented and substantiated by closely-related simulation examples and engineering applications. It is a very good addition to the libraries of those interested in the subject. It is also qualified to be used as a postgraduate-level reference."
| International Journal of Adaptive Control and Signal Processing |
| 456pp |
Pub. date: Oct 2001 |
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