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.

The applications of neural networks are areas of considerable interest at present Concise and very tightly targetted text Will be of interest to academic control engineers outside the series' normal industrial audience

Klappentext

The primary purpose of this book is to present a set of techniques which allow the design of controllers able to guarantee stability, convergence and robustness for dynamical systems with unknown nonlinearities and of manufacturing systems.To compensate for the significant amount of uncertainty in system structure, a neural network model developed recently, namely the Recurrent High Order Neural Network (RHONN), is employed.Real applications are provided with illustrations and tables for clarification; the book contains material on:- RHONN structure and approximation capabilities- indirect adaptive control- direct adaptive control- scheduling for manufacturing systems- test case for scheduling using RHONNs.The book is primarily intended for industrial and institutional practitioners but should be of significant interest to undergraduate and graduate students and academic scientists working with neural networks and their applications in engineering.

Inhalt

1. Introduction.- 1.1 General Overview.- 1.2 Book Goals & Outline.- 1.3 Notation.- 2. Identification of Dynamical Systems Using Recurrent High-order Neural Networks.- 2.1 The RHONN Model.- 2.2 Learning Algorithms.- 2.3 Robust Learning Algorithms.- 2.4 Simulation Results.- Summary.- 3. Indirect Adaptive Control.- 3.1 Identification.- 3.2 Indirect Control.- 3.3 Test Case: Speed Control of DC Motors.- Summary.- 4. Direct Adaptive Control.- 4.1 Adaptive Regulation Complete Matching.- 4.2 Robustness Analysis.- 4.3 Modeling Errors with Unknown Coefficients.- 4.4 Tracking Problems.- 4.5 Extension to General Affine Systems.- Summary.- 5. Manufacturing Systems Scheduling.- 5.1 Problem Formulation.- 5.2 Continuous-time Control Law.- 5.3 Real-time Scheduling.- 5.4 Simulation Results.- Summary.- 6. Scheduling using RHONNs: A Test Case.- 6.1 Test Case Description.- 6.2 Results & Comparisons.- Summary.- References.