Apache helicopter stabilization using neural dynamic programming

Russell Enns, Jennie Si

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


A new form of neural control is introduced, neural dynamic programming (NDP), a model-free online learning control scheme. NDP is shown to perform exceedingly well as a learning controller for practical systems of higher dimension, such as helicopters. The discussion is focused on providing a viable alternative helicopter control system design approach rather than providing extensive comparisons among various available controllers. A comprehensive treatise of NDP and extensive simulation studies of NDP designs for controlling an Apache helicopter under different flight conditions is presented. Design robustness is addressed by performing simulations under various disturbance conditions. All of the designs are based on FLYRT, a sophisticated industry-scale nonlinear validated model of the Apache helicopter.

Original languageEnglish (US)
Pages (from-to)19-25
Number of pages7
JournalJournal of Guidance, Control, and Dynamics
Issue number1
StatePublished - 2002

ASJC Scopus subject areas

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


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