NEUROCONTROL：TOWARDS AN INDUSTRIAL CONTROL METHODOLOGY

A complete guide to the design and implementation of successful neurocontrol applications

Neurocontrol: Towards an Industrial Control Methodology is the first and only volume that presents a unified framework for neural network-based techniques. It demystifies neurocontroller design and promotes the broad application of neurocontrol to nonlinear control problems. Divided into two major parts --the theoretical and the practical --this book links neurocontrol with the concepts of classical control theory, describes the steps necessary to implement a working algorithm, and provides the information necessary to develop competitive applications of industrial size and complexity. Throughout, the focus is on the most important issues faced by control systems engineers working in this area, including
* Fundamental approaches to neurocontrol viewed as optimization tasks
* Neural network architectures for neurocontrol
* Learning algorithms viewed as optimization algorithms
* Identification of plant models from measured data
* Training of an optimal neurocontroller
* Robustness, adaptiveness, stability, and other special topics
* Implementation of neurocontrol applications


Supplemented with case studies of real-world industrial control applications --from car drive train control to wastewater treatment plant control --Neurocontrol is an important professional reference for control engineers in a wide range of industries as well as for automatic control and adaptive control researchers. It is also an excellent text for graduate and senior undergraduate students in neurocontrol and automatic control.

Tomas Hrycej is Senior Researcher at the Daimler-Benz Research Center in Ulm, Germany; former senior researcher at PCS Computer Systems in Munich; and the author of Modular Learning in Neural Networks. The case studies presented in this book are based on Dr. Hrycej's work at the Daimler-Benz Research Center.

METHODS.

Important Concepts of Classical Control.

Fundamental Approaches to Neurocontrol.

Neural Networks for Control.

Optimization Methods for Neurocontrol.

Plant Identification.

Controller Training.

Robust Neurocontrol.

Learning and Adaptiveness in Neurocontrol.

Stability of Neurocontrollers.

A Neurocontrol Algorithm--The Easiest Way to the Goal.

CASE STUDIES.

Introductory Remarks to Case Studies.

Elastomere Test Bench Control.

Drive Train Test Bench Control.

Lateral Control of an Autonomous Vehicle.

Biological Wastewater Treatment Control.

Conclusion: Application Potential of Neurocontrol.

References.

Index.