Software tools for distributed intelligent control systems

Conference · Mon Sep 23 00:00:00 EDT 1991 · Annual Review in Automatic Programming

DOI:https://doi.org/10.1016/0066-4138(91)90014-3· OSTI ID:10105405

James, J. R. ^[1]; Herget, C. J. ^[2]

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1. United States Army, Fort Monroe, VA (United States). Training and Doctrine Command
2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

The future of intelligent control systems depends upon the extent to which Artificial Intelligence (AI) technology can help control engineers deliver practical solutions to difficult control engineering problems. Conventional control design approaches have achieved notable successes in the design and implementation of robust, adaptive controllers for systems with well-defined mathematical models. However, conventional approaches have had difficulty supporting engineers in the design and implementation of control systems when an accurate mathematical model is not available. Also, verification that computer-controlled systems perform to specifications, validation of the specifications, higher-level control, operator decision aids, system diagnosis, operator alerting, and reconfiguration of systems which experience large changes over time or potentially catastrophic failures are significant challenges to control science and engineering. It is in these difficult areas where the AI technologies of knowledge representation, learning, search, diagnosis, planning, and decision are being used to aid control engineers. Algorithms for computer-controlled systems and software tools to help implement these algorithms have been a subject of research and commercialization for decades. Computer-Aided Control Engineering (CACE) tools have achieved a degree of success in the past decade based on their ability to assist in the control system design and implementation process. Specialized tools have been made available for system identification, system simulation, controller design and controller implementation. Recently, efforts have been made to build integrated CACE environments. This paper will describe the results of the workshop and subsequent efforts to use these results to shape a DARPA software development project.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 10105405
• Report Number(s): UCRL-JC--108590; CONF-9109316--2; ON: DE92004656
• Journal Information: Annual Review in Automatic Programming, Journal Name: Annual Review in Automatic Programming Journal Issue: 1 Vol. 16; ISSN 0066-4138
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
99 GENERAL AND MISCELLANEOUS
990200
ARTIFICIAL INTELLIGENCE
COMPUTERIZED CONTROL SYSTEMS
DISTRIBUTED DATA PROCESSING
EXPERT SYSTEMS
MATHEMATICS AND COMPUTERS
REAL TIME SYSTEMS
decision aids
diagnosis
distributed processing
expert systems
intelligent control
knowledge representation
learning
possibility theory
propagation of uncertainty
reconfiguration