From jswift@oai-pop.lerc.nasa.gov Mon Jun 13 00:03:59 BST 1994 Article: 270 of ieee.announce Path: lyra.csx.cam.ac.uk!doc.ic.ac.uk!pipex!swrinde!howland.reston.ans.net!usenet.ins.cwru.edu!lerc.nasa.gov!lerc.nasa.gov!oai20453.lerc.nasa.gov!jswift From: Julie Swift Newsgroups: ieee.announce Subject: Fuzzy Control Short Course - Cleveland Ohio - August 4-5 Date: 7 Jun 1994 13:57:48 GMT Organization: Ohio Aerospace Institute Lines: 126 Distribution: usa Message-ID: <2t1ucs$fuf@sulawesi.lerc.nasa.gov> NNTP-Posting-Host: oai20453.lerc.nasa.gov X-UserAgent: Version 1.1.3 X-XXMessage-ID: X-XXDate: Tue, 7 Jun 94 10:01:59 GMT Theory and Applications of Fuzzy Control August 4 and 5, 1994 Held at the Ohio Aerospace Institute in Cleveland Contact Person: Julie Swift (ph: 216-962-3033, e-mail: jswift@oai-pop.lerc.nasa.gov) Program Overview: There is a growing interest in fuzzy control in the U.S. due to its successful application in Japan and Europe (e.g., for consumer products, control of passenger trains, and so on). While there is an increased use of fuzzy control for some applications, control engineers often hesitate to use fuzzy control in "critical environments" (e.g., aircraft control, nuclear reactors, etc.). Reasons for this include the fact that (i) there are relatively few results on mathematical, control-theoretic modeling and analysis of fuzzy control systems in the literature (e.g., stability analysis), and (ii) engineers often need to have proper justification to use fuzzy control over conventional well-studied control techniques that have certain known performance and reliability characteristics. Overall, there is a significant need to come to an understanding of the basics of fuzzy control and identify the advantages and disadvantages of its use so that its appropriate role can be found. The goal of this short course is to provide an introduction to the area of fuzzy control and show how engineering analysis with attention to applications can be used to evaluate the advantages and disadvantages of fuzzy control. In this course we begin to address these issues by first providing an introduction to fuzzy control and then: (i) by introducing and comparing fuzzy control techniques (direct, supervisory, and learning) with conventional control approaches (e.g., PID, non-linear, and adaptive), (ii) by explaining how to use nonlinear analysis (e.g., stability analysis) for fuzzy control systems, and (iii) by using several control applications as testbeds to evaluate fuzzy and conventional control. Moreover, we provide an introduction to the state-of-the-art software that is used for computer-aided-design of fuzzy control systems, with on-line demonstrations and simulation in the presentation. Who Should Attend: >From Academe: Faculty and students (undergraduate and graduate) . >From Industry and Government Laboratories: Engineers, mathematicians, technicians, management personnel, and basically anyone who interfaces with control systems, in any area of application. Instructors: Kevin M. Passino received the Ph.D in electrical engineering from the University of Notre Dame. He has worked in the control systems group at Magnavox Electronic Systems Co. in Ft. Wayne and at McDonnell Aircraft Co., St. Louis, on research in intelligent flight control. He spent a year at Notre Dame as a Visiting Assistant Professor and is currently an Assistant Professor in the Department of Electrical Engineering at The Ohio State University. Professor Passino is currently on the Board of Governors for the IEEE Control Systems Society, is an Associate Editor for the IEEE Transactions on Automatic Control, and is on the Editorial Board of the Int. Journal for Engineering Applications of Artificial Intelligence. He served as the Guest Editor for the Special Issue on Intelligent Control for IEEE Control Systems Magazine in June 1993 and is currently serving as a Guest Editor for a Special Track of Papers on Intelligent Control in IEEE Expert Magazine. He is co-editor (with P.J. Antsaklis) of the book "An Introduction to Intelligent and Autonomous Control", (Kluwer Academic Press, 1993). He was Program Co-Chairman for the 8th IEEE Int. Symp. on Intelligent Control, 1993. His research interests include intelligent and autonomous control, fuzzy and expert systems for control, genetic algorithms for control, discrete event systems, stability theory, and failure detection and identification systems. Stephen Yurkovich received the Ph.D. degree in electrical engineering from the University of Notre Dame. He held teaching and postdoctoral research positions at Notre Dame in 1984, and a Visiting Associate Professor position there in 1992. In 1984 he moved to the Department of Electrical Engineering at The Ohio State University, where he is currently Associate Professor. His research interests include system identification and parameter set estimation for control, and fuzzy logic for control, in application areas including flexible mechanical structures, intelligent vehicle highway systems, and automotive systems. Professor Yurkovich teaches a variety of undergraduate and graduate level courses in control theory, and has authored the text Control Systems Laboratory. He has held numerous positions within the IEEE Control Systems Society: he is past chairman of the Standing Committees on Student Activities and on Publications, is currently serving a three-year term as an elected member of the Board of Governors, and was an Executive Officer in 1993. He is a Senior Member of IEEE and is currently Editor-in-chief of IEEE Control Systems. Schedule: Part I: Overview and Introduction to Fuzzy Control (8:30a.m. - 12:00p.m.; Aug. 4) Overview - Trends in theory, software, and hardware Introduction to Fuzzy Systems - Fuzzy sets and fuzzy logic - Fuzzy implications and inference - Defuzzification Fuzzy Control Nonlinear Analysis - Stability Analysis - Applications Part II: Techniques and Applications in Fuzzy Control (1:30-5:00p.m.; Aug. 4) Applications of Direct Fuzzy Control - Ball and Beam Laboratory Testbed - Robotic systems - Automotive systems Adaptive Fuzzy Control - Fuzzy Learning Control - Applications Supervisory Control - PID tuning and laboratory application - Flexible-link robot laboratory testbed Part III: Computer-Aided Design of Fuzzy Control Systems (9:00a.m.-12:00p.m.; Aug. 5) Design Issues Software - Defining a fuzzy system - Defining the control system - Simulating the control system Issues in evaluation Open forum for discussion/questions on design ==========================================================