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A knowledge-based approach to identification and adaptation in dynamical systems controlArtificial intelligence techniques are applied to the problems of model form and parameter identification of large-scale dynamic systems. The object-oriented knowledge representation is discussed in the context of causal modeling and qualitative reasoning. Structured sets of rules are used for implementing qualitative component simulations, for catching qualitative discrepancies and quantitative bound violations, and for making reconfiguration and control decisions that affect the physical system. These decisions are executed by backward-chaining through a knowledge base of control action tasks. This approach was implemented for two examples: a triple quadrupole mass spectrometer and a two-phase thermal testbed. Results of tests with both of these systems demonstrate that the software replicates some or most of the functionality of a human operator, thereby reducing the need for a human-in-the-loop in the lower levels of control of these complex systems.
Document ID
19890041203
Document Type
Conference Paper
Authors
Glass, B. J.
(NASA Ames Research Center Moffett Field, CA, United States)
Wong, C. M.
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1988
Subject Category
Cybernetics
Meeting Information
IEEE Conference on Decision and Control(Austin, TX)
Distribution Limits
Public
Copyright
Other
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