Integrated environment for intelligent control

A new architecture for designing real-time intelligent control systems is developed, which consists of several symbolic reasoning systems, numerical computation routines and a meta-system to control the selection, operation and communication of these programs. The key issue to construct such an integrated intelligent system is to organize a meta-system. Briefly, the main functions of the meta-system are: (I) To coordinate and manage all symbolic reasoning systems and numerical computation routines in the integrated intelligent system; (2) To distribute knowledge into separate expert systems and numerical routines so that the integrated intelligent system can be managed effectively; (3) To acquire new knowledge; (4) To find an optimal solution for the conflict facts and events among different expert systems; (5) To provide the possibility of parallel processing in the integrated intelligent system; (6) To communicate with the measuring devices and the final control element in the control system. The meta-system is implemented with a Lisp-like language on a VAX 11/780 computer under UNIX environment. The configuration of the real-time intelligent control system has attracted significant attention from both the industry and the academia and is expected to lead to a new era for the applications of AI technique to chemical and process engineering.

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

[2]  W. S. Faught,et al.  “ A Real Time Expert System for Process Control " , 2022 .

[3]  J. S. Kowalik,et al.  Coupling Symbolic and Numeric Computing in Knowledge-Based Systems , 1987 .

[4]  James R. Slagle,et al.  A Heuristic Program that Solves Symbolic Integration Problems in Freshman Calculus , 1963, JACM.

[5]  George Stephanopoulos The future of expert systems in chemical engineering , 1987 .

[6]  Joel Moses,et al.  Symbolic integration: the stormy decade , 1966, CACM.

[7]  Bertrand Zavidovique,et al.  Behavior Rule Systems for Distributed Process Control , 1985, Conference on Artificial Intelligence Applications.

[8]  Nancy Martin,et al.  Programming Expert Systems in OPS5 - An Introduction to Rule-Based Programming(1) , 1985, Int. CMG Conference.

[9]  A. Kitchen,et al.  Knowledge based systems in artificial intelligence , 1985, Proceedings of the IEEE.

[10]  Piero P. Bonissone,et al.  A Retrospective View of CACE-III: Considerations in Coordinating Symbolic and Numeric Computation in a Rule-Based Expert System , 1985, CAIA.

[11]  Elaine Kant,et al.  Programming expert systems in OPS5 , 1985 .

[12]  C T Kitzmiller,et al.  Coupling symbolic and numeric computing in KB systems , 1987 .

[13]  Manfred Morari,et al.  ROBEX: An Expert System for Robust Control Design , 1987, 1987 American Control Conference.

[14]  Jeffrey J. P. Tsai,et al.  IDSCA: An intelligent direction selector for the controller's action in multiloop control systems , 1988, Int. J. Intell. Syst..

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

[16]  J.H. Taylor,et al.  An expert system architecture for computer-aided control engineering , 1984, Proceedings of the IEEE.