Journal of Aerospace System Engineering (항공우주시스템공학회지)

The Society for Aerospace System Engineering (항공우주시스템공학회)
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Control Law Design for a Tilt-Duct Unmanned Aerial Vehicle using Sigma-Pi Neural Networks

Sigma-Pi 신경망을 이용한 틸트덕트 무인기의 제어기 설계연구

  • Kang, Youngshin (Flight Control Research Team, Korea Aerospace Research Institute) ;
  • Park, Bumjin (Flight Control Research Team, Korea Aerospace Research Institute) ;
  • Cho, Am (Flight Control Research Team, Korea Aerospace Research Institute) ;
  • Yoo, Changsun (Flight Control Research Team, Korea Aerospace Research Institute)
  • 강영신 (한국항공우주연구원 비행제어연구팀) ;
  • 박범진 (한국항공우주연구원 비행제어연구팀) ;
  • 조암 (한국항공우주연구원 비행제어연구팀) ;
  • 유창선 (한국항공우주연구원 비행제어연구팀)
  • Received : 2016.10.18
  • Accepted : 2016.12.05
  • Published : 2017.02.28
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Abstract

A Linear parameterized Sigma-Pi neural network (SPNN) is applied to a tilt-duct unmanned aerial vehicle (UAV) which has a very large longitudinal stability ($C_{L{\alpha}}$). It is uncontrollable by a proportional, integral, derivative (PID) controller due to heavy stability. It is shown that the combined inner loop and outer loop of SPNN controllers could overcome the sluggish longitudinal dynamics using a method of dynamic inversion and pseudo-control to compensate for reference model error. The simulation results of the way point guidance are presented to evaluate the performance of SPNN in comparison to a PID controller.

매우 큰 정안정성($C_{L{\alpha}}$)을 갖는 틸트덕트 운동모델에 대해 선형 파라미터를 갖는 Sigma-Pi 신경망(SPNN) 제어법칙을 적용하였다. 기존의 비례적분미분(PID) 제어기는 매우 큰 정안정성을 갖는 운동모델이 갖는 강한 기수숙임 문제를 해결하기 어려웠고 이로인해 제어성능을 높일 수 없었다. 이와 달리 외부루프와 내부루프에 모두 적용된 SPNN 제어기는 동역학역변환 및 모델오차를 줄일 수 있는 의사적응제어 명령을 이용해서 과도한 안정성을 개선할 수 있었다. 이를 검증하기 위해서 경로점 추종 시뮬레이션을 이용해서 PID제어 성능과 SPNN제어 성능을 비교하였다.

Keywords

Acknowledgement

Supported by : 산업통상자원부

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