Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Controller Transition Management of Hybrid Position Control System for Unmanned Expedition Vehicles

무인탐사차량의 위치제어를 위한 복합제어 시스템의 제어기 전이관리

  • 양철관 (중앙대학교 전자전기공학부) ;
  • 심덕선 (중앙대학교 전자전기공학부)
  • Published : 2008.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

A position control problem is studied for UEV(Unmanned Expedition Vehicles), which is to follow pre-determined paths via fixed way-points. Hybrid control systems are used for position control of UEV depending on the operating condition. Speed control consists of three controllers: PID control, adaptive PI control, and neural network. Heading control consists of two controllers, PID and adaptive PID control. The controllers are selected based on the changes of road conditions. We suggest an adaptive PI control algorithm for speed control and an transition management algorithm among the controllers. The algorithm adapts the road conditions and variation of vehicle dynamical characteristics and selects a suitable controller.

Keywords

References

  1. M. Guler, S. Clements, L. M. Wills, B. S. Heck, and G. J. Vachtsevanos, 'Transition management for reconfigurable hybrid control systems,' IEEE Control Systems Magazine, pp. 36-49, February 2003
  2. 심덕선, 양철관, 안규섭, 이준학, '유비쿼터스 환경에서 개방형 제어 플랫폼에 기반한 무인탐사차량의 재형상 가능 위치제어,' 제어.자동화.시스템공학회 논문지 , vol. 11, no, 12, pp. 1002-1010, 2005. 12 https://doi.org/10.5302/J.ICROS.2005.11.12.1002
  3. S. Chaib, M. S. Netto, S. Mammar, '$H_{\infty}$, Adaptive, PID and fuzzy control: a comparison of controllers for vehicle lane keeping,' Proceedings of 2004 IEEE Intelligent Vehicles Symposium, pp. 139-144, 2004. 6
  4. N. A. Pohlman, 'Estimation and control of a multi-vehicle testbed using GPS doppler sensing,' Master's thesis, MIT, September 2002
  5. F. L. Lewis, Applied Optimal Control and Estimation, Prentice-Hall, Inc., pp. 251-297, 1992
  6. L. H. Tsoukalas, R. E. Uhrig, Fuzzy and Neural Approaches in Engineering, John Wiley & Sons, Inc., 1997