Neural Networks-Based PID Precision Motion Control of a Piezo-Actuated Microinjector

  • Yizheng Yan
  • Qingsong XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11745)


Piezoelectric actuators are widely employed in the field of micro-/nanomanipulation. However, hysteresis is the dominant issue in piezoelectric actuators, which leads to a great challenge to achieve high precision micromanipulation. Proportional-integral-derivative (PID) control is an efficient approach to reduce hysteresis effect in piezoelectric actuators. However, its parameter tuning is a time-consuming work for PID motion tracking control implementation. In this work, the neural networks (NN) is adopted to provide a functional model for PID with optimized parameters. It enables an intelligent and adaptive motion tracking process. The effectiveness of the presented NN-based PID control scheme is verified by performing simulation studies.


Piezoelectric actuator Hysteresis PID control Neural networks Precision motion control 



This work was supported in part by the National Natural Science Foundation of China under Grant 51575545, the Macao Science and Technology Development Fund under Grant 179/2017/A3, and Research Committee of the University of Macau under Grant MYRG2018-00034-FST.


  1. 1.
    Croft, D., Shedd, G., Devasia, S.: Creep, hysteresis, and vibration compensation for piezoactuators: atomic force microscopy application. In: Proceedings of 2000 American Control Conference (ACC), vol. 3, pp. 2123–2128 (2000)Google Scholar
  2. 2.
    Nan, Z., Xu, Q.: Depth detection for a stereo cell micro-injection system with dual cameras. In: Proceedings of 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1106–1111 (2017)Google Scholar
  3. 3.
    Zhang, X., Xu, Q.: Design and testing of a new 3-DOF spatial flexure parallel micropositioning stage. Int. J. Precis. Eng. Manuf. 19(1), 109–118 (2018)CrossRefGoogle Scholar
  4. 4.
    Xu, Q.: Micromachines for Biological Micromanipulation. Springer, Cham (2018). Scholar
  5. 5.
    Wang, G., Xu, Q.: LuGre model based hysteresis compensation of a piezo-actuated mechanism. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds.) IAS 2016. AISC, vol. 531, pp. 645–657. Springer, Cham (2017). Scholar
  6. 6.
    Lin, C.J., Chen, S.Y.: Evolutionary algorithm based feedforward control for contouring of a biaxial piezo-actuated stage. Mechatronics 19(6), 829–839 (2009)CrossRefGoogle Scholar
  7. 7.
    Zhang, Y., Xu, Q.: Adaptive sliding mode control with parameter estimation and kalman filter for precision motion control of a piezo-driven microgripper. IEEE Trans. Control Syst. Technol. 25(2), 728–735 (2017)CrossRefGoogle Scholar
  8. 8.
    Ali, A., Ahmed, S.F., Joyo, M.K., Kushsairy, K.: MPC-PID comparison for controlling therapeutic upper limb rehabilitation robot under perturbed conditions. In: Proceedings of IEEE International Conference on Engineering Technologies and Social Sciences (ICETSS), pp. 1–5 (2017)Google Scholar
  9. 9.
    Zhuang, M., Atherton, D.: Automatic tuning of optimum PID controllers. In: IEE Proceedings D-Control Theory and Applications, vol. 140, pp. 216–224 (1993)CrossRefGoogle Scholar
  10. 10.
    Youssef, A.: Optimized PID tracking controller for piezoelectric hysteretic actuator model. World J. Model. Simul. 9(3), 223–234 (2013)Google Scholar
  11. 11.
    Xu, Q., Tan, K.K.: Advanced Control of Piezoelectric Micro-/Nano-positioning Systems. AIC. Springer, Cham (2016). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electromechanical Engineering, Faculty of Science and TechnologyUniversity of MacauMacauChina

Personalised recommendations