Rule-based mechanisms of learning for intelligent adaptive flight control.

David A. Handelman, Robert F. Stengel

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

The authors investigate how certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

Original languageEnglish (US)
Pages (from-to)208-213
Number of pages6
JournalProceedings of the American Control Conference
Volume88 pt 1-3
DOIs
StatePublished - 1988

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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