Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
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Gyroscope Signal Denoising of Ship's Autopilot using Kalman Filter and Multi-Layer Perceptron

칼만필터와 다층퍼셉트론을 이용한 선박 오토파일럿의 자이로스코프 신호 잡음제거

  • Kim, Min-Kyu (Ocean Science and Technology School, Korea Maritime and Ocean University) ;
  • Kim, Jong-Hwa (Ocean Science and Technology School, Korea Maritime and Ocean University) ;
  • Yang, Hyun (Ocean Science and Technology School, Korea Maritime and Ocean University)
  • 김민규 (한국해양대학교 해양과학기술전문대학원) ;
  • 김종화 (한국해양대학교 해양과학기술전문대학원) ;
  • 양현 (한국해양대학교 해양과학기술전문대학원)
  • Received : 2019.09.17
  • Accepted : 2019.10.28
  • Published : 2019.10.31
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Abstract

Since January 1, 2020, the International Maritime Organization (IMO) has put in place strong regulations to reduce air pollution caused by ships by lowing the upper limit of ship fuel oil sulfur content from 3.5% to 0.5% for ships passing through all sea areas around the world. Although it is important to reduce air pollutants by using fuel oil with low sulfur content, reducing the amount of energy waste through the economic operation of a ship can also help reduce air pollutants. Ships can follow designated routes accurately even under the influence of noise using autopilot systems. However, regardless of their quality, the performance of these systems is af ected by noise; heading angles with added measurement noise from the gyroscope are input into the autopilot system and degrade its performance. A technique to solve these problems reduces noise effects through the application of a Kalman filter, which is widely used in condition estimation. This method, however, cannot completely eliminate the effects of noise. Therefore, to further improve noise removal performances, in this study we propose a better denoising method than the Kalman filter technique by applying a multi-layer perceptron (MLP) in forward direction motion and a Kalman Filter in rotation motion. Simulations show that the proposed method improves forward direction motion by preventing the malfunction of a rudder more so than merely using a Kalman Filter.

2020년 1월 1일부터 국제해사기구(IMO)는 전 세계 모든 해역을 지나가는 선박을 대상으로 선박연료유의 황 함유량 상한선을 3.5 %에서 0.5 %로 낮춰 선박으로 인해 발생하는 대기오염을 줄이기 위한 강력한 규제를 실시한다. 황 함유량이 낮은 연료유를 사용하여 대기오염 물질을 줄이는 것도 중요하지만 선박을 경제적으로 운영하여 불필요한 에너지 낭비를 줄이는 것 또한 대기오염 물질을 줄이는데 큰 도움이 된다. 따라서 선박은 잡음의 영향을 받더라도 항로를 정확하게 유지하여야 한다. 항로를 정확하게 추종하기 위해 오토파일럿 시스템이 사용되지만 오토파일럿 시스템의 성능이 아무리 우수하다 하더라도 잡음의 영향을 받게 된다면 성능에 한계를 가진다. 실제 환경에서는 자이로스코프에서 측정잡음이 더해진 회두각이 오토파일럿 시스템의 입력으로 들어가 오토파일럿 시스템의 성능을 저하시킨다. 이와 같은 문제를 해결하기 위해 상태추정에 쓰이는 Kalman Filter를 적용하여 잡음의 영향을 줄여주는 기법이 있지만 이 또한 역시 잡음의 영향을 완전히 제거시키는 것이 불가능하다. 따라서 본 논문에서는 잡음제거 성능을 더욱 더 개선시키기 위해 전진방향 구간에서는 인공지능 기술 중 하나인 다층퍼셉트론(Multi-Layer Perceptron; MLP)를 적용하고, 회전구간에서는 Kalman Filter를 적용하여 Kalman Filter만을 사용한 경우보다 우수한 잡음제거 기법을 제안한다. 시뮬레이션을 통해 제안한 방법이 Kalman Filter만을 사용한 경우보다 조타기의 오동작을 방지하여 선박의 전진방향 운동이 개선됨을 확인할 수 있다.

Keywords

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