Steering Control and Geomagnetism Cancellation for an Autonomous Vehicle using MR Sensors

  • 김홍렬 (목포대학교 전자공학과) ;
  • 손석준 (전남대학교 전기공학과) ;
  • 김태곤 (전남대학교 전기공학과) ;
  • 김정희 (전남대학교 전기공학과) ;
  • 임영철 (전남대학교 전기공학과) ;
  • 김의선 (서남대학교 전기전자멀티미디어 공학부) ;
  • 장영학 (목포대학교 제어계측공학과)
  • Kim, Hong-Reol (Dept. of Electronic Eng., Mokpo Natl Univ.) ;
  • Son, Seok-Jun (Dept. of Electrical Eng. And RRC, Chonnam Natl Univ.) ;
  • Kim, Tae-Gon (Dept. of Electrical Eng. And RRC, Chonnam Natl Univ.) ;
  • Kim, Jeong-Heui (Dept. of Electrical Eng. And RRC, Chonnam Natl Univ.) ;
  • Lim, Young-Cheol (Dept. of Electrical Eng. And RRC, Chonnam Natl Univ.) ;
  • Kim, Eui-Sun (School of Electrical, Electronic and Multimedia Eng., Seonam Univ.) ;
  • Chang, Young-Hak (Control and Instrumentation Eng., Mokpo Natl Univ.)
  • 발행 : 2001.09.30

초록

This paper describes the steering control and geomagnetism cancellation for an autonomous vehicle using an MR sensor. The magneto-resistive (MR) sensor obtains the vector summation of the magnetic fields from embedded magnets and the Earth. The vehicle is controlled by the magnetic fields from embedded magnets. So, geomagnetism is the disturbance in the steering control system. In this paper, we propose a new method of the sensor arrangement in order to remove the geomagnetism and vehicle body interference. The proposed method uses two MR sensors located in a level plane and the steering controller has been developed. The controller has three input variables ($dB_x$, $dB_y$, $dB_z$) using the measured magnetic field difference, and an output variable (the steering angle). A simulation program was developed to acquire the data to teach the neural network, in order to test the ability of a neural network to learn the steering control process. Also, the computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. From the simulation and field test, good result was obtained and we confirmed the robustness of the neural network controller in a real autonomous vehicle.

키워드