Online adaptive critic flight control

Silvia Ferrari, Robert F. Stengel

Research output: Contribution to journalArticle

99 Scopus citations

Abstract

A nonlinear control system comprising a network of networks is taught by the use of a two-phase learning procedure realized through novel training techniques and an adaptive critic design. The neural network controller is trained algebraically, offline, by the observation that its gradients must equal corresponding linear gain matrices at chosen operating points. Online learning by a dual heuristic adaptive critic architecture optimizes performance incrementally over time by accounting for plant dynamics and nonlinear effects that are revealed during large, coupled motions. The method is implemented to control the six-degree-of-freedom simulation of a business jet aircraft over its full operating envelope. The result is a controller that improves its performance while unexpected conditions, such as unmodeled dynamics, parameter variations, and control failures, are experienced for the first time.

Original languageEnglish (US)
Pages (from-to)777-786
Number of pages10
JournalJournal of Guidance, Control, and Dynamics
Volume27
Issue number5
DOIs
StatePublished - 2004

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Aerospace Engineering
  • Space and Planetary Science
  • Electrical and Electronic Engineering
  • Applied Mathematics

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