JACIII Vol.23 No.5 pp. 810-822
doi: 10.20965/jaciii.2019.p0810


Discrete Adaptive Control with Multiple-Step-Guess Estimation for Brushless DC Motor

Guirong Shao*1,*2, Minling Zhu*3,†, Hongbin Ma*1, and Xinghong Zhang*4

*1Beijing Institute of Technology
5 South Zhongguancun Street, Haidian District, Beijing 100081, China

*2Department of Physics and Electronic Engineering, Yuncheng University
No.1155 Fudan West Street, Yanhu District, Shanxi 044000, China

*3Beijing Information Science and Technology University
No.35 Beisihuan Middle Road, Chaoyang District, Beijing 100101, China

*4Henan Institute of Technology
No.699 Pingyuan Road, Xinxiang 453003, China

Corresponding author

March 30, 2018
January 4, 2019
September 20, 2019
BLDCM, parameters change, reference speed mutation, discrete adaptive control, Multiple-Step-Guess

The brushless DC motor (BLDCM) speed control system has various kinds of uncertainties, such as reference speed mutation, noise and parameters change, etc. However, proportional integral (PI) control method used widely cannot handle the uncertainties in the control system well. A novel discrete adaptive control with Multiple-Step-Guess (MSG) estimation for BLDCM speed control system is proposed in this contribution. MSG estimation is firstly developed and applied in BLDCM speed control system, which estimate the BLDCM model parameters online with only five steps history information sampled from the input signal and output signal. The tracking adaptive control law is designed to ensure the speed can track reference speed rapidly and accurately. Compared with PI control and recursive least square adaptive control (RLSAC), extensive simulations verify that the BLDCM speed response under MSG adaptive control (MSGAC) has better dynamic and steady state performance in the case of reference speed mutation and BLDCM parameters change. Simulation results illustrate that the novel proposed method is effective and robust for uncertainties of BLDCM speed control system.

Cite this article as:
G. Shao, M. Zhu, H. Ma, and X. Zhang, “Discrete Adaptive Control with Multiple-Step-Guess Estimation for Brushless DC Motor,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.5, pp. 810-822, 2019.
Data files:
  1. [1] N. Hemati and M. C. Leu, “A Complete Model Characterization of Brushless DC Motors,” IEEE Trans. on Industry Applications, Vol.28, No.1, pp. 172-180, 1992.
  2. [2] H. Wu, S. Cheng, and S. Cui, “A Controller of Brushless DC Motor for Electric Vehicle,” IEEE Trans. on Magnetics, Vol.41, No.1, pp. 509-513, 2005.
  3. [3] S.-J. Kim, J.-W. Park, W.-S. Im, H.-W. Jung, and J.-M. Kim, “An Optimal Current Distribution Method of Dual-Rotor BLDCM Machines,” J. of Power Electronics, Vol.13, No.2, pp. 250-255, 2013.
  4. [4] N. Hemati, J. S. Thorp, and M. C. Leu, “Robust nonlinear control of brushless DC Motors for direct-drive robotic applications,” IEEE Trans. on Industrial Electronics, Vol.37, No.6, pp. 460-468, 1990.
  5. [5] C. Wang and S.-J. Zhao, “Research for control system of soccer robot based on DSP,” Advances in Future Computer and Control Systems, Vol.2, pp. 91-95, 2012.
  6. [6] C. Wang, “Design of direct-drive motor for wheeled robot,” Shenyang University of Technolgy, Vol.30, No.2, pp. 121-124, 2008.
  7. [7] R. Nadolski, K. Ludwinek, J. Staszak, and M. Jaśkiewicz, “Utilization of BLDC motor in electrical vehicles,” Przeglad Elektrotechniczny, Vol.88, No.4A, pp. 180-186, 2012.
  8. [8] Y. Wang, X. Zhang, X. Yuan, and G. Liu, “Position-Sensorless Hybrid Sliding-Mode Control of Electric Vehicles with Brushless DC Motor,” IEEE Trans. on Vehicular Technology, Vol.60, No.2, pp. 421-432, 2011.
  9. [9] M.-J. Yang, H-.L. Jhou, B.-Y. Ma, and K.-K. Shyu, “A Cost-Effective Method of Electric Brake With Energy Regeneration for Electric Vehicles,” IEEE Trans. on Industrial Electronics, Vol.56, No.6, pp. 2203-2212, 2009.
  10. [10] L. Tian and S. Xu, “Attitude Control Considering Variable Input Saturation Limit for a Spacecraft Equipped with Flywheels,” Chinese J. of Aeronautics, Vol.25, No.3, pp. 437-445, 2012.
  11. [11] X. Zhou and J. Fang, “Precise Braking Torque Control for Attitude Control Flywheel with Small Inductance Brushless DC Motor,” IEEE Trans. on Power Electronics, Vol.28, No.11, pp. 5380-5390, 2013.
  12. [12] V. Bist and B. Singh, “A PFC Based BLDCM Drive for Low Power Household Appliances,” EPE J., Vol.24, No.2, pp. 21-30, 2014.
  13. [13] J.-W. Park, S.-H. Hwang, and J.-M. Kim, “Sensorless Control of Brushless DC Motors with Torque Constant Estimation for Home Appliances,” IEEE Trans. on Industry Applications, Vol.48, No.2, pp. 677-684, 2012.
  14. [14] D.-K. Hong, B.-C. Woo, D.-H. Koo, and U.-J. Seo, “A Single-Phase Brushless DC Motor With Improved High Efficiency for Water Cooling Pump Systems,” IEEE Trans on Magnetics, Vol.47, No.10, pp. 4250-4253, 2011.
  15. [15] J. Fonseca, A. Andrade, D. E. Nicolosi et al., “A new technique to control brushless motor for blood pump application,” Artificial Organs, Vol.32, No.4, pp. 355-359, 2006.
  16. [16] S. He and S. Li, “Method of Eliminating Velocity Ripples in Brushless DC Motor Based on Internal Model Principle and Sliding Model Control,” 28th Chinese Control and Decision Conf., pp. 3197-3202, 2016.
  17. [17] H.-J. Guo, S. Sagawa, and O. Ichinokura, “Position Sensorless Driving of BLDCM Using Neural Networks,” Electrical Engineering in Japan, Vol.162, No.4, pp. 64-71, 2008.
  18. [18] X. Y. Zhang, “Intelligent information of TS fuzzy PID control system of BLDCM,” Advanced Materials Research, Vol.846-847, pp. 313-316, 2014.
  19. [19] Y. Liu, M. Xia, and J. Zhao, “A Novel Adaptive Fuzzy Controller Approach of Brushless DC Motors without Hall and Position Sensors,” Przeglad Elektrotechniczny, Vol.88, No.12A, pp. 290-294, 2012.
  20. [20] P. Song, J. Zhang, and K. Zhang, “Simulation of BLDCM speed control system based on PI controller with fuzzy parameter regulators,” Applied Mechanics and Materials, Vol.29-32, pp. 841-846, 2010.
  21. [21] J. Xiu, S. Wang, and Y. Xiu, “Fuzzy adaptive single neuron NN control of brushless DC motor,” Neural Computing and Applications, Vol.22, No.3, pp. 607-613, 2013.
  22. [22] Y. Lü, L. Xie, and F. Li, “A fuzzy sliding mode controller for EMA in Aircraft,” Fire Control & Command Control, No.8, pp. 64-66, 70, 2011.
  23. [23] H.-P. Wang and Y.-T. Liu, “Integrated Design of Speed-Sensorless and Adaptive Speed Controller for a Brushless DC Motor,” IEEE Trans. on Power Electronics, Vol.21, No.2, pp. 518-523, 2006.
  24. [24] L. H. Rong, H. B. Ma, and M. L. Wang, “Nontrivial closed-loop analysis for an extremely simple one-step-guess adaptive controller,” Proc. of the 23rd Chinese Control and Decision Conf., pp. 1385-1390, 2011.
  25. [25] H. B. Ma, L. H. Rong, M. L. Wang, and M. Y. Fu, “Adaptive tracking with one-step-guess estimator and its variants,” Proc. of the 30th Chinese Control Conf., pp. 2521-2526, 2011.
  26. [26] L. Rong, H. Ma, C. Yang, and M. Wang, “Decentralized Adaptive Tracking with One-Step-Guess Estimator,” Proc. of the 9th IEEE Int. Conf. on Control and Automation Conf., pp. 1320-1325, 2011.
  27. [27] H. Zhou, H. Ma, H. Zhan et al., “Sampled Adaptive Control for Multi-joint Robotic Manipulator with Force uncertainties,” Int. Conf. on Intelligent Robotics and Applications, pp. 14-25, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024