The design of three-motor intelligent synchronous decoupling control system

ABSTRACT

Aiming at the characteristics of multi-input and multi-output, nonlinearity, time-variation and strong coupling in the three-motor synchronous control system, and on the basis of mathematic model analysis of three-motor synchronous control system, the neural network control system is designed. It is composed of three intelligent PID controllers based on BP neural network arithmetic which adjusts the parameters of PID controllers on-line and neuron decoupling compensator. The control of speed and tension of system is realized by three intelligent PID controllers based on BP neural network, and the decoupling control of coupled variables is achieved by neuron decoupling compensator. Experiment is combined with PLC, and the results indicate that the control system can get some optimal parameters of the PID controllers according to different running state of system. The method is designed to realize better decoupling control between the speed and tension in the system, and it has better dynamic and static characteristics.

REFERENCES

• 1: XianZhong DAI, GuoHai LIU. 2005.Neural Network Inverse Synchronous Control of Two-motor Variable Frequency Speed-regulating System. ACTA AUTOMATICA SINICA. Vol.31,No.6, 890--900, November.
• 2: He. Fang, Tong. Weiming, Wang. Qiang, 2007. Synchronization Control Strategy of Multi-motor System Based on Profibus Network, Automation and Logistics, 2007 IEEE International Conference on, 18--21 Aug. 3029 -- 3034.
• 3: I. Hiroyuki, M. Youchi, S. Kunio, 1997. Looper H-Infinity Control for Hot-strip Mills, IEEE Trans. on Industrial Application, vol.33, No.3, 790--796.
• 4: Y. Koren, C.C.Lo, 1991. Variable-gain Cross-coupling Controller For Contouring, Ann. CIRP, Vol. 40, No.1, 371--374.
• 5: LIU Xingqiao, CHEN Chong, LIU Guohai, LU Xiuyin. 2008. Neural Network Control of Two-motor Synchronous System, Journal of JiangSu University (Natural Science Edition). vol.29, No.3, 240--243.
• 6: Perez-Pinal F., Calderon G, Araujo I, 2003. Relative Coupling Strategy, IEEE, IEMDC'03, Madison Wisconsin USA, Vol. 2, 1162--1166, June1--4.
• 7: Perez-Pinal, C. Nunez, R. Alvarez, and Cervantes, 2004. Comparison of Multi-motor Synchronization Techniques, Industrial Electronics Society, 2004. IECON 2004, 30th Annual Conference of IEEE 2--6 Nov, vol.2, 1670 -- 1675.
• 8: S. Seung-Ho, S. Seung-Ki. 2000.A New Tension Controller for Continuous Strip Processing Line, IEEE Trans. on Industry Applications, vol.36, No.2, 633--638.
• 9: Yoshiro, S. Kunio, A. Yoshiharu, 1991. Optimal Multivariable Looper Control for Hot Strip Finishing Mill, IEEE Trans. on Industrial Application, vol.27,No.1, 124--130

INDEX TERMS

Primary Classification:
  F. Theory of Computation
  F.1 COMPUTATION BY ABSTRACT DEVICES
      F.1.1 Models of Computation
          Subjects: Self-modifying machines (e.g., neural networks)

Additional Classification:
  F. Theory of Computation
  F.1 COMPUTATION BY ABSTRACT DEVICES
      F.1.1 Models of Computation
          Subjects: Automata (e.g., finite, push-down, resource-bounded); Computability theory

  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.0 General
          Subjects: Stability (and instability); Multiple precision arithmetic

General Terms:
Algorithms, Experimentation

Keywords:
BP neural network, decoupling control, neuron decoupling, speed, synchronous control system, tension