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Preface to the papers from the 1995 IFAC Workshop on artificial intelligence in real-time control
ControlEng. Practice, Vol. 4, No. 11, pp. 1571-1572, 1996
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Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0967-0661/96 $15.00 + 0.00
Pergamon PII:S0967-0661 (96)00171-2
PREFACE TO THE PAPERS FROM THE 1995 IFAC WORKSHOP ON ARTIFICIAL INTELLIGENCE IN REAL-TIME CONTROL J. Kocijan and R. Karba
Faculty of Electrical Engineering, Universityof Ljubljana, Trzaska25, 1000Ljubljana, Slovenia([email protected])
Received May 1996)
Artificial Intelligence and Control Engineering are two research disciplines that have gone hand in hand since the dawn of the first developments in the area of artificial intelligence. Recently, these two disciplines have been becoming even more closely interconnected, and together they divide into many subdisciplines which are heading towards the edges of what may be thought of as "intelligent control".
all researchers and industrialists working with applications of artificial intelligence methods for realtime control. This special section of Control Engineering Practice contains revised and updated versions of some selected papers that will give an overview of the topics presented at the workshop.
The aim of the current series of workshops and symposia on Artificial Intelligence in Real-Time Control is to present the state of the art in the application of artificial intelligence approaches in real-time control, on the one hand, and on the other, to bring together experts from the areas of control, artificial intelligence and different applications areas.
A new method of fault detection is reported in the paper by H. Konrad, Fault detection in milling, using parameter estimation and classification methods. The author describes how the combination of identification through parameter estimation with a subsequent classification involving fuzzy thresholding and neural networks is used for making decisions regarding the nature of various faults. The approach is applied to a milling tool.
The most recent workshop was a continuation of a series of five previous successful events in this field. It was held in Bled, Slovenia, between November 29 and December 1, 1995. The workshop was organized (under the sponsorship of the IFAC Technical Committee on Artificial Intelligence in Real-Time Control, and the co-sponsorship of IMACS) by the Faculty of Electrical and Computer Engineering, University of Ljubljana, in co-0peration with the Jozef Stefan Institute and the Automatic Control Society of Slovenia (the IFAC National Member Organisation in Slovenia).
The paper by Cass and Radl, entitled Adaptive process optimization using functional-link networks and evolutionary optimization, presents an application of artificial neural networks for the modeling 'of coal-fired furnaces, and combustion optimization based upon this model. The benefits gained by using this technique are demonstrated by its realization in practice. It is sometimes difficult to choose between the various available fuzzy control approaches, for application to a particular problem. An assessment of three such methods for use in pressure control is given in the paper by Babugka, et al., Comparison of intelligent control schemes for real-time pressure control. Results show how the design of a fuzzy controller is not a trivial task, even though the use of the techniques is justifiable, in that they represent an already proven technology.
The technical programme of the workshop highlighted a variety of methods, taken from the artificial intelligence field, and applied to control problems. Fuzzy control, artificial neural networks, expert systems, machine learning and other approaches were presented, in a total of 46 papers. The workshop was attended by 75 participants from 16 countries. The papers were of general interest to 1571
1572
J. Kocijan and R. Karba and Schmidt, Neural and fuzzy approaches to vision-based parking control, where
The value of fuzzy logic in the identification of nonlinear process models and their control has frequently been demonstrated as being a very useful (though not yet mature) technology, which is being used more and more often in control applications. An overview of the developments in fuzzy modeling, with some approaches to control design based on these models, is clearly presented in the plenary paper by Babugka and Verbruggen, An overview of
Daxwanger
fuzzy modeling for control.
The increasing numbers of control systems that include AI-based components, and that are already being implemented in industry, need to be evaluated scientifically. The contributions at the workshop that deal with these recent achievements in this area, constitute a significant step in this direction.
Finally, the interconnection of two subfields in area of artificial intelligence, namely fuzzy logic artificial neural networks, is assessed in application to parking control in the paper
the and an by
the authors make a comparison between a visionbased parking control system with an artificial neural network, and one with a fuzzy-neural hybrid architecture. Both structures are used to transfer a human driver's skills to an automatic parking controller.
~ )
Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0967-0661/96 $15.00 + 0.00
Pergamon PII:S0967-0661 (96)00171-2
PREFACE TO THE PAPERS FROM THE 1995 IFAC WORKSHOP ON ARTIFICIAL INTELLIGENCE IN REAL-TIME CONTROL J. Kocijan and R. Karba
Faculty of Electrical Engineering, Universityof Ljubljana, Trzaska25, 1000Ljubljana, Slovenia([email protected])
Received May 1996)
Artificial Intelligence and Control Engineering are two research disciplines that have gone hand in hand since the dawn of the first developments in the area of artificial intelligence. Recently, these two disciplines have been becoming even more closely interconnected, and together they divide into many subdisciplines which are heading towards the edges of what may be thought of as "intelligent control".
all researchers and industrialists working with applications of artificial intelligence methods for realtime control. This special section of Control Engineering Practice contains revised and updated versions of some selected papers that will give an overview of the topics presented at the workshop.
The aim of the current series of workshops and symposia on Artificial Intelligence in Real-Time Control is to present the state of the art in the application of artificial intelligence approaches in real-time control, on the one hand, and on the other, to bring together experts from the areas of control, artificial intelligence and different applications areas.
A new method of fault detection is reported in the paper by H. Konrad, Fault detection in milling, using parameter estimation and classification methods. The author describes how the combination of identification through parameter estimation with a subsequent classification involving fuzzy thresholding and neural networks is used for making decisions regarding the nature of various faults. The approach is applied to a milling tool.
The most recent workshop was a continuation of a series of five previous successful events in this field. It was held in Bled, Slovenia, between November 29 and December 1, 1995. The workshop was organized (under the sponsorship of the IFAC Technical Committee on Artificial Intelligence in Real-Time Control, and the co-sponsorship of IMACS) by the Faculty of Electrical and Computer Engineering, University of Ljubljana, in co-0peration with the Jozef Stefan Institute and the Automatic Control Society of Slovenia (the IFAC National Member Organisation in Slovenia).
The paper by Cass and Radl, entitled Adaptive process optimization using functional-link networks and evolutionary optimization, presents an application of artificial neural networks for the modeling 'of coal-fired furnaces, and combustion optimization based upon this model. The benefits gained by using this technique are demonstrated by its realization in practice. It is sometimes difficult to choose between the various available fuzzy control approaches, for application to a particular problem. An assessment of three such methods for use in pressure control is given in the paper by Babugka, et al., Comparison of intelligent control schemes for real-time pressure control. Results show how the design of a fuzzy controller is not a trivial task, even though the use of the techniques is justifiable, in that they represent an already proven technology.
The technical programme of the workshop highlighted a variety of methods, taken from the artificial intelligence field, and applied to control problems. Fuzzy control, artificial neural networks, expert systems, machine learning and other approaches were presented, in a total of 46 papers. The workshop was attended by 75 participants from 16 countries. The papers were of general interest to 1571
1572
J. Kocijan and R. Karba and Schmidt, Neural and fuzzy approaches to vision-based parking control, where
The value of fuzzy logic in the identification of nonlinear process models and their control has frequently been demonstrated as being a very useful (though not yet mature) technology, which is being used more and more often in control applications. An overview of the developments in fuzzy modeling, with some approaches to control design based on these models, is clearly presented in the plenary paper by Babugka and Verbruggen, An overview of
Daxwanger
fuzzy modeling for control.
The increasing numbers of control systems that include AI-based components, and that are already being implemented in industry, need to be evaluated scientifically. The contributions at the workshop that deal with these recent achievements in this area, constitute a significant step in this direction.
Finally, the interconnection of two subfields in area of artificial intelligence, namely fuzzy logic artificial neural networks, is assessed in application to parking control in the paper
the and an by
the authors make a comparison between a visionbased parking control system with an artificial neural network, and one with a fuzzy-neural hybrid architecture. Both structures are used to transfer a human driver's skills to an automatic parking controller.