Advertisement

International Journal of Automotive Technology

, Volume 19, Issue 6, pp 959–967 | Cite as

DC Motor Current Control Algorithm Using Proportional-Integral LQT with Disturbance Observer

  • Ung Jon
  • Jihwan Kim
  • Hyeongcheol LeeEmail author
Article
  • 14 Downloads

Abstract

This paper proposes a DC motor current control algorithm using a proportional-integral linear quadratic tracking (LQT) controller with a disturbance observer for the electronic stability control (ESC) brake system. Previously researched algorithms related to current control using disturbance rejection are robust control, adaptive control, LQT, or proportional-integral disturbance observer (PI-DOB); each of them has both advantages and disadvantages. The proposed algorithm uses a disturbance observer in order to improve disturbance rejection performance while avoiding the drawbacks of high gain property. Additionally, the proposed algorithm adds integral control in order to improve performance in the low frequency bands. In order to assess the performance of the proposed algorithm, simulations and experiments are performed in the time and frequency domains to compare the proposed algorithm with different algorithms which are actually implemented into the ESC. The proposed algorithm shows good characteristics near the cut-off frequency, which can be confirmed clearly by the time domain results.

Key Words

Linear quadratic tracking control DC motor Electronic stability control Disturbance rejection Uncertainties Integral action 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Åström, K. J. and Hägglund, T. (2006). Advanced PID Control. ISA-The Instrumentation, Systems and Automation Society. North Carolina, USA.Google Scholar
  2. Austin, L. and Morrey, D. (2000). Recent advances in antilock braking systems and traction control systems. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 214, 6, 625–638.Google Scholar
  3. Bartolini, G., Pisano, A., Punta, E. and Usai, E. (2003). A survey of applications of second-order sliding mode control to mechanical systems. Int. J. Control 76, 9–10, 875–892.MathSciNetzbMATHGoogle Scholar
  4. Burl, J. B. (1998). Linear Optimal Control: H (2) and H (Infinity) Methods. Addison-Wesley Longman Publishing Co., Inc. Massachusetts, USA.Google Scholar
  5. Canudas, C., Astrom, K. and Braun, K. (1987). Adaptive friction compensation in DC-motor drives. IEEE J. Robotics and Automation 3, 6, 681–685.CrossRefGoogle Scholar
  6. Chen, F. M., Tsai, J. S. H., Liao, Y. T., Guo, S. M., Ho, M. C., Shaw, F. Z. and Shieh, L. S. (2014). An improvement on the transient response of tracking for the sampleddata system based on an improved PD-type iterative learning control. J. Franklin Institute 351, 2, 1130–1150.CrossRefzbMATHGoogle Scholar
  7. Cho, H. and Yi, S. J. (2003). Vehicle trajectory control using the fuzzy logic controller. J. Korean Society of Precision Engineering 20, 11, 91–99.Google Scholar
  8. Choi, S. B. (2011). Sensorless adaptive speed control of a permanent-magnet dc motor for anti-lock brake systems. Int. J. Automotive Technology 12, 2, 207–212.CrossRefGoogle Scholar
  9. Coelingh, E., Eidehall, A. and Bengtsson, M. (2010). Collision warning with full auto brake and pedestrian detection–A practical example of automatic emergency braking. Proc. Int. IEEE Conf. Intelligent Transportation Systems, Funchal, Portugal.Google Scholar
  10. Han, J., Kim, H., Joo, Y., Jo, N. H. and Seo, J. H. (2013). A simple noise reduction disturbance observer and Q-filter design for internal stability. Proc. IEEE 13th Int. Conf. Control, Automation and Systems (ICCAS), Gwangju, Korea.Google Scholar
  11. Hwang, D.-S. and Hsu, P.-L. (1997). A practical design for a multivariable proportional-integral controller in industrial applications. Industrial & Engineering Chemistry Research 36, 7, 2739–2748.CrossRefGoogle Scholar
  12. Jiang, G.-P., Wang, S.-P. and Song, W.-Z. (2000). Design of observer with integrators for linear systems with unknown input disturbances. Electronics Letters 36, 13, 1168–1169.CrossRefGoogle Scholar
  13. Johnson, R. W., Evans, J. L., Jacobsen, P., Thompson, J. R. and Christopher, M. (2004). The changing automotive environment: High-temperature electronics. IEEE Trans. Electronics Packaging Manufacturing 27, 3, 164–176.CrossRefGoogle Scholar
  14. Karimi-Ghartemani, M., Khajehoddin, S. A., Jain, P. and Bakhshai, A. (2011). Linear quadratic output tracking and disturbance rejection. Int. J. Control 84, 8, 1442–1449.MathSciNetCrossRefzbMATHGoogle Scholar
  15. Kim, J., Lee, H. and Choi, S. (2012). A robust road bank angle estimation based on a proportional-integral H∞ filter. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 226, 6, 779–794.Google Scholar
  16. Kong, G., Zhang, N. and Zhang, B. (2016). Novel hybrid optimal algorithm development for DC motor of Automated Manual Transmission. Int. J. Automotive Technology 17, 1, 135–143.CrossRefGoogle Scholar
  17. Lorenz, R. D. and Haines, L. P. (2000). Understanding Modern Power Conversion: Robert D. Lorenz and Lance P. Haines. University of Wisconsin. Wisconsin, USA.Google Scholar
  18. Minorsky, N. (1922). Directional stability of automatically steered bodies. Naval Engineers Journal 32, 2, 280–309.CrossRefGoogle Scholar
  19. Naidu, D. S. (2002). Optimal Control Systems. CRC Press. Florida, USA.CrossRefGoogle Scholar
  20. Park, M., Lee, S., Kim, M., Lee, J. and Yi, K. (2015). Integrated differential braking and electric power steering control for advanced lane-change assist systems. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 229, 7, 924–943.Google Scholar
  21. Plestan, F., Glumineau, A. and Laghrouche, S. (2008). A new algorithm for high-order sliding mode control. Int. J. Robust and Nonlinear Control 18, 4–5, 441–453.MathSciNetCrossRefzbMATHGoogle Scholar
  22. Rahimi, S. and Naraghi, M. (2017). Design of an integrated control system to enhance vehicle roll and lateral dynamics. Trans. Institute of Measurement and Control 40, 5, 1435–1446.CrossRefGoogle Scholar
  23. Sabanovic, A., Fridman, L. M. and Spurgeon, S. K. (2004). Variable Structure Systems: From Principles to Implementation. IET. Hertfordshire, UK.Google Scholar
  24. Sariyildiz, E. and Ohnishi, K. (2015). Stability and robustness of disturbance-observer-based motion control systems. IEEE Trans. Industrial Electronics 62, 1, 414–422.CrossRefGoogle Scholar
  25. Satoh, T., Kaneko, K. and Saito, N. (2014). Improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems. Int. J. Computers Communications & Control 7, 3, 550–564.CrossRefGoogle Scholar
  26. Söffker, D., Yu, T.-J. and Müller, P. C. (1995). State estimation of dynamical systems with nonlinearities by using proportional-integral observer. Int. J. Systems Science 26, 9, 1571–1582.CrossRefzbMATHGoogle Scholar
  27. Tipsuwan, Y. and Chow, M.-Y. (1999). Fuzzy logic microcontroller implementation for DC motor speed control. Conf. Proc. 25th Annual Conf. IEEE Industrial Electronics Society, San Jose, California, USA.Google Scholar
  28. Yang, I., Lee, W. and Hwang, I. (2003). A model-based design analysis of hydraulic braking system. SAE Paper No. 2003–01-0253.Google Scholar
  29. Young, K. D., Utkin, V. I. and Ozguner, U. (1996). A control engineer’s guide to sliding mode control. Proc. IEEE Int. Workshop on Variable Structure Systems, Tokyo, Japan.Google Scholar
  30. Van Zanten, A. T. (2000). Bosch ESP systems: 5 years of experience. SAE Paper No. 2000–01-1633.Google Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringHanyang UniversitySeoulKorea

Personalised recommendations