JACIII Vol.21 No.2 pp. 271-277
doi: 10.20965/jaciii.2017.p0271


Precise Synchronization Control for Biaxial System via a Cross-Iterative PID Neural Networks Control Algorithm

Wang-Yong He, Rui-Huan Zhang, Yong-Bo Li, and Jian Peng

School of Automation, China University of Geosciences
Wuhan, Hubei, China

June 13, 2016
November 10, 2016
Online released:
March 15, 2017
March 20, 2017
biaxial synchronization system, coupling dynamic model, PID neural networks, cross-iterative control, synchronization error

The crossiterative proportion, integration, and differentiation (PID) Neural Networks control algorithm presented here enhances position synchronization control in machine tools driven by two ball screws. An electromechanical coupling dynamics model reflecting typical system characteristics is established and then, based on dynamic analysis, a coordination control between two motor forces is investigated by separating machine tool translational and rotational dynamics. Based on state feedback, we adopt a crossiterative PID Neural Networks control algorithm using the Lyapunov function to guarantee controller stability to achieve coordination between two motor forces. Computer simulation and experimental results indicate that the algorithm follows reference input well and shows good control performance in reducing synchronization errors. The proposed algorithm also has good control performance on a biaxial synchronous machine system regardless of whether interference effects are large or small.

  1. [1] R. Smith, “Wide Bandwidth Control of High-Speed Milling Machine Feed Drives,” Ph.D. Thesis, University of Florida, Department of Mechanical Engineering, Florida, A.D., 1999.
  2. [2] M. Nakamura, O. Kunimatsu, S. Goto, and N. Kyura, “Method of contour control of industrial articulated robot arm by use of synchronous positioning control with dynamic compensation of master and slave axes,” Trans. of the Society of Instrument and Control Engineers, Vol.37, No.11, pp. 1062-1067, 2001.
  3. [3] D. Sun and R. Lu, “Synchronous Tracking Control of Parallel Manipulators Using Cross-coupling Approach,” The Int. . of Robotics Research, Vol.25, No.11, pp. 1137-1147, 2006.
  4. [4] D. J. Gordon, K. Erkorkmaz, “Precision control of a T-type gantry using sensor actuator averaging and active vibration damping,” Precision Engineering, Vol.36, pp. 299-314, 2012.
  5. [5] Y. Xiao and Y. Pang, “Synchronous control for high-accuracy biaxial motion systems,” Control Theory Application, Vol.11, No.2, pp. 294-298, 2013.
  6. [6] T. C. Chen and C. H. Yu, “Robust control for a biaxial servo with time delay system based on adaptive tuning technique,” ISA Trans. Vol.48 pp. 283-294, 2009.
  7. [7] M. H. Cheng, Y. J. Li, and E. G. Bakhoum, “Controller synthesis of tracking and synchronization for multiaxis motion system,” IEEE Trans. on Control System Technology, Vol.22, No.1, pp. 378-386, 2014.
  8. [8] H. Y. Chuang and C. H. Liu, “A model-referenced adaptive control strategy for improving contour accuracy of multi-axis machine tools,” IEEE Trans. on Industry Applications, Vol.28, No.1, pp. 221-227, 2002.
  9. [9] D. Sun, “Adaptive Coupling Control of Two Working Operations in CNC Integrated Machines,” J. of Dynamic Systems, Measurement, and Control, Vol.125, pp. 662-666, 2003.
  10. [10] S. H. Ling, “PID neural network control system,” National defense industry press, 2006.
  11. [11] J. R. Shewchuk, “An Introduction to the Conjugate Gradient Method Without the Agonizing Pain,” School of Computer Science Carnegie Mellon University Pittsburgh, 1994.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 24, 2017