Helicopter tracking control using direct neural dynamic programming

R. Enns, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


This paper advances a newly introduced neural learning control mechanism for helicopter flight control design. Based on direct neural dynamic programming (DNDP), the control system is tailored to learn to maneuver a helicopter in addition to its trimming and stabilization capabilities presented in earlier works. The paper consists of a comprehensive treatise of DNDP and extensive simulation studies of DNDP designs for controlling an Apache helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industry-scale non-linear validated model of the Apache helicopter. Though illustrated for helicopters, our DNDP control system framework should be applicable for general purpose tracking control.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages6
StatePublished - 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001


OtherInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC

ASJC Scopus subject areas

  • Software


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