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Preface to the papers from the IEEE workshop on neuro-fuzzy control: instrumentation and control applications
Control Eng. Practice, Vol. 2, No. 1, pp. 87-88, 1994 Printed in Great Britain. All rights reserved.
0967-0661/94 $6.00 + 0.00 © 1994 Pergamon Pzess I.,td
PREFACE TO THE PAPERS FROM THE IEEE WORKSHOP ON NEURO-FUZZY CONTROL: INSTRUMENTATION AND CONTROL APPLICATIONS A. De Carli
Department of Comptaers and Systems Sciences, University of Rome "La Sapienza", Via Eudossiaaa 18, [-00184 Roma, Italy
After the Workshop, the Control Engineering Prac6ce Associate Editors N. Sundararajan and A. De Carli, with the help of Professor Lofli A. Zadeh and other members of the International Programme Committee, decided to suggest to the authors of some of the tutorial and regular papers that they should prepare an expanded version for publication in this journal. These papers give an overview of the very wide range of interests emerging from this workshop.
Many industrial systems, such as chemical processes, textiles, and pulp and paper, to name just a few, have very complex structures. A feasible mathematical model is therefore a hard task to derive by applying conventional methods. Fuzzy logic and neural networks provide a powerful and mathematical method of representing complex and non-linear systems. Fuzzy logic and neural networks can also be used to implement simpler and less-expensive control strategies, in which human experience and intuition can be tran~erred in an easier way for achieving superior performance. These innovative controllers are certainly not a panacea to feedback control problems, but they can be regarded as an additional tool in a control engineer's toolbox, to be appropriately applied as needed.
H, N. Koivo shows the use of neural networks in static and dynamic fault diagnosis, and control. Applications to paper machine web breaks and realtime control are highlights of the paper. The application of neural networks to fault detection is a really new and interesting application. The paper from T. Shibata and T. Fukuda presents a new strategy for the motion planning of a multiagent robotic system in a decentralised realisation. Each robot acquires knowledge about its environment, expressed by fuzzy logic. A genetic algorithm is applied to plan task strategies optimally.
In the last few years many conferences and symposia on fuzzy logic have been organised all over the world. Very-general ones have been held in Japan and in the United States. The topics ranged from the theoretical approaches to many applications. The expectations of the control community have therefore been only partially satisfied.
The paper from B. R. Lin and R. Hofl reviews neural-network and fuzzy-logic approaches in some power electronics applications, e.g., buck conveners, and inverters, and in induction motors and d,c, motor drives. Neural-network and fuzzy-logic applications are presently one of the most active research areas in power electronics.
The Workshop on Neuro and Fuzzy Control that was held in Muroran, Japan, in the Spring of 1993, was unique in the sense that it brought together similar and yet very diverse themes of neuro-fuzzy theories and applications. It was promoted by the IEEE, organised by the Muroran Institute of Technology, and partially supported by the Nippon Steel Corporation and other companies. Professor Yasuhiko Dote and Mr Takashi Waseda were the General Co-Chairmen. More than a hundred people attended the Workshop, many from the United States and Europe. The Proceedings Volume includes more than 80 papers.
The paper from H. Takada et al. deals with a design environment for neural networks, developed to enhance productivity in some plants belonging to the Nippon Steel Corporation. Applications to a blast furnace operation and an on-line process monitoring system are illustrated. 87
88
A. De Carli
H. Ushida et al. propose a two-degree-of-freedom fuzzy control system that uses an associative memory system. The proposed system represents operational and causal knowledge for static and dynamic fuzzy knowledge. Two degrees of freedom are obtained, since static fuzzy knowledge allows operational models to adjust flexibly to the state of the plant, and thereby to maintain a smoothly running plant, whereas dynamic fuzzy knowledge generates prospective state transition patterns for characteristic fluctuations occurring in plant operation.
In the paper of De Carli et al., fuzzy logic is used to achieve the on-line adaptation of the parameter values for a proportional and integral control strategy. A design procedure for the fuzzy logic algorithm is presented, and a set of validation tests gives satisfactory information on its validity. The papers summarised above give an insight into what is going on in the application of neuro-fuzzy controllers. Some trends in modern industrial development can be deduced, and new research areas are indicated.
0967-0661/94 $6.00 + 0.00 © 1994 Pergamon Pzess I.,td
PREFACE TO THE PAPERS FROM THE IEEE WORKSHOP ON NEURO-FUZZY CONTROL: INSTRUMENTATION AND CONTROL APPLICATIONS A. De Carli
Department of Comptaers and Systems Sciences, University of Rome "La Sapienza", Via Eudossiaaa 18, [-00184 Roma, Italy
After the Workshop, the Control Engineering Prac6ce Associate Editors N. Sundararajan and A. De Carli, with the help of Professor Lofli A. Zadeh and other members of the International Programme Committee, decided to suggest to the authors of some of the tutorial and regular papers that they should prepare an expanded version for publication in this journal. These papers give an overview of the very wide range of interests emerging from this workshop.
Many industrial systems, such as chemical processes, textiles, and pulp and paper, to name just a few, have very complex structures. A feasible mathematical model is therefore a hard task to derive by applying conventional methods. Fuzzy logic and neural networks provide a powerful and mathematical method of representing complex and non-linear systems. Fuzzy logic and neural networks can also be used to implement simpler and less-expensive control strategies, in which human experience and intuition can be tran~erred in an easier way for achieving superior performance. These innovative controllers are certainly not a panacea to feedback control problems, but they can be regarded as an additional tool in a control engineer's toolbox, to be appropriately applied as needed.
H, N. Koivo shows the use of neural networks in static and dynamic fault diagnosis, and control. Applications to paper machine web breaks and realtime control are highlights of the paper. The application of neural networks to fault detection is a really new and interesting application. The paper from T. Shibata and T. Fukuda presents a new strategy for the motion planning of a multiagent robotic system in a decentralised realisation. Each robot acquires knowledge about its environment, expressed by fuzzy logic. A genetic algorithm is applied to plan task strategies optimally.
In the last few years many conferences and symposia on fuzzy logic have been organised all over the world. Very-general ones have been held in Japan and in the United States. The topics ranged from the theoretical approaches to many applications. The expectations of the control community have therefore been only partially satisfied.
The paper from B. R. Lin and R. Hofl reviews neural-network and fuzzy-logic approaches in some power electronics applications, e.g., buck conveners, and inverters, and in induction motors and d,c, motor drives. Neural-network and fuzzy-logic applications are presently one of the most active research areas in power electronics.
The Workshop on Neuro and Fuzzy Control that was held in Muroran, Japan, in the Spring of 1993, was unique in the sense that it brought together similar and yet very diverse themes of neuro-fuzzy theories and applications. It was promoted by the IEEE, organised by the Muroran Institute of Technology, and partially supported by the Nippon Steel Corporation and other companies. Professor Yasuhiko Dote and Mr Takashi Waseda were the General Co-Chairmen. More than a hundred people attended the Workshop, many from the United States and Europe. The Proceedings Volume includes more than 80 papers.
The paper from H. Takada et al. deals with a design environment for neural networks, developed to enhance productivity in some plants belonging to the Nippon Steel Corporation. Applications to a blast furnace operation and an on-line process monitoring system are illustrated. 87
88
A. De Carli
H. Ushida et al. propose a two-degree-of-freedom fuzzy control system that uses an associative memory system. The proposed system represents operational and causal knowledge for static and dynamic fuzzy knowledge. Two degrees of freedom are obtained, since static fuzzy knowledge allows operational models to adjust flexibly to the state of the plant, and thereby to maintain a smoothly running plant, whereas dynamic fuzzy knowledge generates prospective state transition patterns for characteristic fluctuations occurring in plant operation.
In the paper of De Carli et al., fuzzy logic is used to achieve the on-line adaptation of the parameter values for a proportional and integral control strategy. A design procedure for the fuzzy logic algorithm is presented, and a set of validation tests gives satisfactory information on its validity. The papers summarised above give an insight into what is going on in the application of neuro-fuzzy controllers. Some trends in modern industrial development can be deduced, and new research areas are indicated.