Towards symbolic process control

Abstract Much recent work in Intelligent Control has introduced and analyzed various approaches to the implementation of expert controllers. But very little attention has been given to a fundamental question: “How can a controller be designed by abstracting rules from observations of a real process?” This paper gives an overview of the concept of symbolic control and briefly compares it with expert control. A method for designing symbolic controllers is proposed, based on qualitative measures of state coordinates and their derivatives, which allows the symbolic to numeric interface to be expressed using a system of recurrence equations. The technique is illustrated with two examples, symbolic process control of a first order process and symbolic control of a simulation representing a car on a defined track.

[1]  W. Pedrycz,et al.  Control problems in fuzzy systems , 1982 .

[2]  Lotfi A. Zadeh,et al.  On Fuzzy Mapping and Control , 1996, IEEE Trans. Syst. Man Cybern..

[3]  Steven R. LeClair,et al.  Qualitative Process Automation vs. Quantitative Process Control , 1987, 1987 American Control Conference.

[4]  Milton W. Green,et al.  An Expert System for Real-Time Control , 1986, IEEE Software.

[5]  L. Foulloy.  Using qualitative reasoning to write expert controllers , 1989 .

[6]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artif. Intell..

[7]  Richard M. Tong,et al.  A retrospective view of fuzzy control systems , 1984 .

[8]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[9]  K. De Jong.  Intelligent control: integrating AI and control theory , 1983 .

[10]  Karl-Erik Årzén,et al.  Expert control , 1986, at - Automatisierungstechnik.

[11]  King-Sun Fu,et al.  Learning control systems and intelligent control systems: An intersection of artifical intelligence and automatic control , 1971 .

[12]  Marco Saerens,et al.  A neural controller , 1989 .

[13]  R. Tong.  Some Properties of Fuzzy Feedback Systems , 1980 .

[14]  M. Sugeno,et al.  Fuzzy algorithmic control of a model car by oral instructions , 1989 .

[15]  E. H. Mamdani,et al.  Advances in the linguistic synthesis of fuzzy controllers , 1976 .

[16]  Lowell B. Hawkinson,et al.  A Real-Time Expert System for Process Control , 1986 .

[17]  R. K. Elsley,et al.  A learning architecture for control based on back-propagation neural networks , 1988, IEEE 1988 International Conference on Neural Networks.

[18]  Laurent Foulloy,et al.  Towards a Methodology to Write Rules for Expert Controllers , 1989 .

[19]  R. R. Leitch,et al.  A real-time knowledge based system for product quality control , 1988 .

[20]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[21]  M. Sugeno,et al.  Fuzzy Control of Model Car , 1985 .

[22]  B. Zavidovique,et al.  Towards The Adaptive Laser Robot , 1985, Other Conferences.

[23]  J. C. Francis,et al.  INTELLIGENT KNOWLEDGE-BASED PROCESS CONTROL. , 1985 .

[24]  Michael K. Masten,et al.  An advanced showcase of adaptive controller designs , 1990 .

[25]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[26]  Jirí Benes,et al.  On neural networks , 1990, Kybernetika.