Adaptive Control with Recurrent High-order Neural Networks

1. Front Matter

   Pages I-XII

   PDF

2. Introduction

   • George A. Rovithakis, Manolis A. Christodoulou

   Pages 1-8

3. Identification of Dynamical Systems Using Recurrent High-Order Neural Networks

   • George A. Rovithakis, Manolis A. Christodoulou

   Pages 9-28

4. Indirect Adaptive Control

   • George A. Rovithakis, Manolis A. Christodoulou

   Pages 29-51

5. Direct Adaptive Control

   • George A. Rovithakis, Manolis A. Christodoulou

   Pages 53-135

6. Manufacturing Systems Scheduling

   • George A. Rovithakis, Manolis A. Christodoulou

   Pages 137-164

7. Scheduling Using Rhonns: A Test Case

   • George A. Rovithakis, Manolis A. Christodoulou

   Pages 165-183

8. Back Matter

   Pages 185-194

   PDF

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.

• Non-linear Control
• Notation
• Scheduling
• Tracking
• adaptive control
• algorithms
• artificial intelligence
• control
• model
• modeling
• neural networks
• simulation
• stability
• uncertainty
• complexity

• Department of Electronic and Computer Engineering, Technical University of Crete, Chania, Crete, Greece

  George A. Rovithakis, Manolis A. Christodoulou

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