A Study on the Accurate Stopping Control of a Train for the Urban Rail Transit Using Kalman Filter

칼만 필터를 이용한 도시철도 열차 정위치 정차에 관한 연구

  • Kim, Jungtai (Metropolitan Transportation Research Center, Korea Railroad Research Institue) ;
  • Lee, Jaeho (Metropolitan Transportation Research Center, Korea Railroad Research Institue) ;
  • Kim, Moo Sun (Metropolitan Transportation Research Center, Korea Railroad Research Institue) ;
  • Park, Chul Hong (Metropolitan Transportation Research Center, Korea Railroad Research Institue)
  • 김정태 (한국철도기술연구원 광역도시교통본부) ;
  • 이재호 (한국철도기술연구원 광역도시교통본부) ;
  • 김무선 (한국철도기술연구원 광역도시교통본부) ;
  • 박철홍 (한국철도기술연구원 광역도시교통본부)
  • Received : 2016.10.05
  • Accepted : 2016.11.10
  • Published : 2016.11.30


Accurate stopping control is important for trains, especially now that many train stations are equipped with platform screen doors. Various algorithms have been proposed for accurate stopping control. However, most metro trains in South Korea use classic control algorithms such as PID control because other algorithms are too complex to realize. PID control has merits of simple structure and operation. However, PID control sometimes fails, and much time is needed to find the proper coefficients due to the long control period and the brake delay. We propose a control algorithm that uses a Kalman filter. The Kalman filter estimates the states at the time when braking starts. Then, a suitable control input is derived for proper control. System modeling and a computer simulation were performed with consideration of the brake properties and the period of the control system. The superiority of the proposed control algorithm is shown by analyzing stop errors.


Accurate Stopping Control;Stop Accuracy;PID Control;Kalman Filter;Modeling and Simulation


Supported by : 한국철도기술연구원


  1. ATC/ATO Training Team, Training Report, Daegu Metropolitan Transit Cooperation, November, 1998.
  2. S. C. Seo, "A Study on the Controls of Precise PositionStop in Rapid Transit Trains", Ph. D. Thesis, Seoul National University of Science and Technology, 2009.
  3. H. A. Ahmad, "Dynamic Braking Control for Accurate Train Braking Distance Estimation under Different Operating Conditions", Virginia Polytechnic Institute and State University, 2013.
  4. Q. Song, Y. Song, and W. Cai, "Adaptive Backstepping Control of Train Systems with Traction/Braking Dynamics and Uncertain ResistiveForces", Vehicle System Dynamics, vol. 49, no. 9, pp. 1441-1454, 2011. DOI:
  5. D. Chen and C. Gao, "Soft Computing Methods Appliedto Train Station Parking in Urban Rail Transit", Applied Soft Computing, vol. 12, pp. 759-767, 2012. DOI:
  6. Korea Railroad Research Institute, Research Report of the Metropolitan Railroad Standardization Research, Ministry of Infrastructure and Transport, 2001.
  7. R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems", Trans. of the ASME-Journal of Basic Engineering, vol. 82 (Series D), pp. 35-45, 1960.
  8. G. Welch and G.Biship, "An Introduction to the Kalman Filter", UNC-Chapel Hill, TR 95-041, pp. 29-45, July 2006.
  9. J. Kim, M. S. Kim, K. J. Ko, and D. U. Jang, "The Study on the Standardization of the Maximum Acceleration of the Electric Multiple Unit through the Analysis of the Traction and the Adhesion Characteristics", Journal of the Korea Academia-Industrial Cooperation Society, vol. 16, no. 11, pp. 7934-7940, 2015. DOI:
  10. Adrian Boeing Blog, accessed May2016, http://adrian
  11. M. Ghanai and K. Chafaa, "Kalman filter in Control and Modeling", INTECH Open Access Publisher, 2009. DOI: