Software environment and architecture for intelligent control

Three types of software environment and architecture for intelligent control systems are discussed. The first type is the single expert system that only processes symbolic information and provides assistance to control engineers in decision-making processes for design and offline monitoring. The second is the coupling system that couples the numerical computation programs into an expert system so that it can be used to solve engineering problems. The third is the integrated intelligent system that is a large intelligence integration environment, which can integrate different expert systems or numerical packages together to solve complex problems.<<ETX>>

[1]  Jeffrey J. P. Tsai,et al.  An intelligent decisionmaker for optimal control , 1988, Appl. Artif. Intell..

[2]  Randall Davis,et al.  Knowledge-based systems: the view in 1986 , 1990 .

[3]  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..

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

[5]  Ervin Y. Rodin,et al.  Artificial intelligence modelling of control systems , 1988, Simul..

[6]  R. Shirley.  Some lessons learned using expert systems for process control , 1987, IEEE Control Systems Magazine.

[7]  Heimo H. Adelsberger,et al.  Expert systems and simulation , 1985 .

[8]  Heimo H. Adelsberger,et al.  Intelligent simulation environments , 1986 .

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

[10]  C. Friedlander,et al.  The Pilot's Associate: a forum for the integration of knowledge-based systems and avionics , 1988, Proceedings of the IEEE 1988 National Aerospace and Electronics Conference.

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

[12]  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.

[13]  John R. James,et al.  A Survey of Knowledge-Based Systems for Computer-Aided Control System Design , 1987, 1987 American Control Conference.

[14]  G. N. Saridis,et al.  Intelligent robotic control , 1983 .

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

[16]  M. Rao,et al.  Integrated architecture for intelligent control , 1988, Proceedings IEEE International Symposium on Intelligent Control 1988.

[17]  Karl Johan Åström,et al.  Toward intelligent control , 1989 .

[18]  Felix S. Wong,et al.  Coupling of symbolic and numerical computations on a microcomputer , 1988, Artif. Intell. Eng..

[19]  Bernard P. Zeigler.  Multifaceted modeling methodology: Grappling with the irreducible complexity of systems , 1984 .

[20]  Karl-Erik Arzen,et al.  Knowledge-based control systems - aspects on the unification of conventional control systems and knowledge-based systems , 1989, 1989 American Control Conference.

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

[22]  Hany K. Eldeib.  Computer Architectures for Real-Time Knowledge-Based Control , 1989, 1989 American Control Conference.

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

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

[25]  Bernard P. Zeigler,et al.  Artificial intelligence in modelling and simulation: Directions to explore , 1987, Simul..

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

[27]  Kimon P. Valavanis,et al.  Analytical design of intelligent machines , 1985, Autom..

[28]  Eleri Cardozo,et al.  Toast: The Power System Operator's Assistant , 1986, Computer.

[29]  Gary Lamont,et al.  The role of artificial intelligence in computer-aided design of control systems , 1987, 26th IEEE Conference on Decision and Control.