Helicopter flight control design using a learning control approach

R. Enns, Jennie Si

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

25 Scopus citations

Abstract

In this paper we introduce a new neural learning control mechanism for helicopter flight control design. The significance of our contribution is twofold. First neural dynamic programming (NDP) is in its early development stage and successful applications to date have been limited to simple systems, typically those possessing only a single control and a handful of states. With our industrial scale helicopter model, we consider a very realistic class of complex design problem. To accommodate such complex systems we introduce the concept of a trim network which is seamlessly integrated into our NDP control structure and is trained using our NDP control structure. Second, we introduce a new class of design methodologies to the helicopter control system design community. This approach is expected to be effective in dealing with real-time learning applications such as reconfigurable control. The paper consists of a comprehensive treatise of NDP and extensive simulation studies of NDP designs for controlling an Apache helicopter. All of our designs are tested using FLYRT, a sophisticated industry-scale non-linear validated model of the Apache helicopter. Though illustrated for helicopters, our NDP control system framework should be applicable to general control systems.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Pages1754-1759
Number of pages6
Volume2
StatePublished - 2000
Event39th IEEE Confernce on Decision and Control - Sydney, NSW, Australia
Duration: Dec 12 2000Dec 15 2000

Other

Other39th IEEE Confernce on Decision and Control
Country/TerritoryAustralia
CitySydney, NSW
Period12/12/0012/15/00

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

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