A Novel Control Approach: Combination of Self-tuning PID and PSO-Based ESO

  • Yanchun Chang
  • Feng Pan
  • Junyi Shu
  • Weixing Li
  • Qi GaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9141)


This study focuses on the steady speed control of brushless DC motor with load torque disturbance from the cam and spring mechanism. Due to the nonlinearity and complexity of the load torque, the control system proposed in this paper is divided into the inner-loop compensator, which is to feed-forward compensate the disturbance, and the outer-loop controller. The inner-loop compensator uses a nonlinear extended state observer (ESO) to compensate the actual system as a nominal model, and the outer-loop pole assignment self-tuning PID controller is used to stabilize the nonlinear nominal model. Since a set of suitable nonlinear ESO parameters are difficult to get normally, particle swarm optimization (PSO) is employed to optimize the observer. The simulation results with high precision verify the effectiveness of the proposed control system.


Brushless DC motor Nonlinear extended state observer Particle swarm optimization Self-tuning PID 


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This work is supported by National Natural Science Foundation of China (61433003, 61273150), and Beijing Higher Education Young Elite Teacher Project (YETP1192).


  1. 1.
    Han, J.: The “Extended State Observer” of a Class of Uncertain Systems. Control and Decision 10(1), 85–88 (1995)Google Scholar
  2. 2.
    Wang, L., Jian-bo, S.: Attitute tracking of aircraft based on disturbance rejection control. Control Theory & Applications 30(12), 1609–1616 (2013)Google Scholar
  3. 3.
    Zhang, P., Shan, D., Li, C., Wang, Y.: New Linear Active Disturbance Rejection Control Design for Gun Control System Infantry Fighting Vehicle. Fire Control & Command Control 39(6), 159–162 (2014)Google Scholar
  4. 4.
    Han, J.-Q.: Active Disturbance Rejection Control Technique-the technique for estimating and compensating the uncertainties. National Defense Industry Press, Beijing (2008)Google Scholar
  5. 5.
    Kun, H., Zhang, X., Liu, C.: Unmanned Underwater Vehicle Depth ADRC Based on Genetic Algorithm Near Surface. Acta Armamentarii 34(2), 217–222 (2013)Google Scholar
  6. 6.
    Qi, X., Li, J., Han, S.: Adaptive Active Disturbance Rejection Control and Its Simulation Based on BP Neural Networks. Acta Armamentarii 34(6), 776–782 (2013)Google Scholar
  7. 7.
    Chen, W., Chu, F., Yan, S.: Stepwise Optimal Design of Active Disturbances Rejection Vibration Controller for Intelligent Truss Structure Based on Adaptive Genetic Algorithm. Journal of Mechanical Engineering 46(7), 74–81 (2010)CrossRefGoogle Scholar
  8. 8.
    Pan, F., Chen, J., Xin, B., Zhang, J.: Several Characteristics Analysis of Particle Swarm Optimizer. Acta Automatica Sinica 35(7), 1010–1016 (2009)CrossRefGoogle Scholar
  9. 9.
    Li, Y., Zhan, Z., Lin, S., Zhang, J., Luo, X.: Competitive and Cooperative Particle Swarm Optimization with Information Sharing Mechanism for Global Optimization Problems. Information Sciences 293(1), 370–382 (2015)CrossRefGoogle Scholar
  10. 10.
    Shen, M., Zhan, Z., Chen, W., Gong, Y., Zhang, J., Li, Y.: Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks. IEEE Transactions on Industrial Electronics 61(12), 7141–7151 (2014)CrossRefGoogle Scholar
  11. 11.
    Valdez, F., Melin, P., Castillo, O.: An Improved Evolutionary Method with Fuzzy Logic for Combining Particle Swarm Optimization and Genetic Algorithms. Applied Soft Computing 11(2), 2625–2632 (2011)CrossRefGoogle Scholar
  12. 12.
    Precup, R.-E., David, R.-C., Petriu, E.M., Preitl, S., Paul, A.S.: Gravitational Search Algorithm-Based Tuning of Fuzzy Control Systems with a Reduced Parametric Sensitivity. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (eds.) Soft Computing in Industrial Applications. AISC, vol. 96, pp. 141–150. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  13. 13.
    Zhou, W., Chow, T.W.S., Cheng, S., Shi, Y.-H.: Contour Gradient Optimization. International Journal of Swarm Intelligence Research 4(2), 1–28 (2013)Google Scholar
  14. 14.
    El-Hefnawy, N.A.: Solving Bi-level Problems Using Modified Particle Swarm Optimization Algorithm. International Journal of Artificial Intelligence 12(2), 88–101 (2014)Google Scholar
  15. 15.
    Liu, Z., Zhang, Y.: Coefficient diagram method based on PSO and its application in aerospace. Flight Dynamics 28(6), 64–67 (2010)Google Scholar
  16. 16.
    Ye, Y., Lin, H.: Drying Room Temperature Predictive Functional Control Based on PSO Parameter Estimation. Machinery 50, 21–24 (2012)Google Scholar
  17. 17.
    Dong, N.: Adaptive Control. Beijing Institute of Technology Press, Beijing (2009)Google Scholar
  18. 18.
    Li, H.: Electric drive control system, 100–109. Electronic Industry Press, Beijing (2006)Google Scholar
  19. 19.
    Zou, H., Dong, S.: Modern Design of Cam Mechanisms, 99–100. Shanghai Jiao Tong University Press, Shanghai (1991)Google Scholar
  20. 20.
    Han, J.: From PID Technique to Active Disturbances Rejection Control Technique. Control Engineering of China 9(3), 13–18 (2002)Google Scholar
  21. 21.
    Gao, Z.-Q.: Scaling and Bandwidth-Parameterization Based Controller Tuning. In: Proceedings of the American Control Conference, pp. 4989–4996 (2003)Google Scholar
  22. 22.
    Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation. IEEE Service Center, Piscataway (1997)Google Scholar
  23. 23.
    Pan, F., Zhang, Q., Liu, J., Li, W., Gao, Q.: Consensus analysis for a class of stochastic PSO algorithm. Applied Soft Computing 23, 567–578 (2014)CrossRefGoogle Scholar
  24. 24.
    Clerc, M., Kennedy, J.: The particle swarm: explosion, stability and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  25. 25.
    Zhao, Z-L.: Convergence of Nonlinear Active Disturbance Rejection Control, 55–104. University of Science and Technology of China (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yanchun Chang
    • 1
  • Feng Pan
    • 1
  • Junyi Shu
    • 1
  • Weixing Li
    • 1
  • Qi Gao
    • 1
    Email author
  1. 1.School of AutomationBeijing Institute of TechnologyBeijingChina

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