Advertisement

Ram Velocity Control in Plastic Injection Molding Machines with Neural Network Learning Control

  • Gaoxiang Ouyang
  • Xiaoli Li
  • Xinping Guan
  • Zhiqiang Zhang
  • Xiuling Zhang
  • Ruxu Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

In plastic injection molding, the ram velocity plays an important role in production quality. This paper introduces a new method, which is a combination of the current cycle feedback control and neural network (NN) learning, to control the ram velocity in injection process. It consists of two parts: a PD controller (current cycle feedback control) is used to stabilize the system, and the feedforward NN learning is used to compensate for nonlinear/unknown dynamics and disturbances, thereby enhancing the performance achievable with feedback control alone. The simulation results indicate that the proposed NN learning control scheme outperforms the conventional PD controller and can greatly reduce tracking errors as the iteration number increase.

Keywords

Tracking Error Injection Molding Inverse Dynamic Injection Molding Machine Iterative Learning Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dominick, V.R., Donald, V.R., Marlene, G.R.: Injection Molding Handbook, vol. 3. Kluwer Academic Publishers, New Jersey (2000)Google Scholar
  2. 2.
    Havlicsek, H., Alleyne, A.: Nonlinear Control of an Electrohydraulic Injection Molding Machine via Iterative Adaptive Learning. IEEE-ASME T. Mech. 4, 312–323 (1999)CrossRefGoogle Scholar
  3. 3.
    Zheng, D.N., Alleyne, A.: Modeling and Control of an Electro-hydraulic Injection Molding Machine with Smoothed Fill-to-pack Transition. J. Manuf. Sci. E.–T. ASME 125, 154–163 (2003)CrossRefGoogle Scholar
  4. 4.
    Tan, K.K., Tang, J.C.: Learning-enhanced PI Control of Ram Velocity in Injection Molding Machines. Eng. Appl. Artif. Intel. 15, 65–72 (2002)CrossRefGoogle Scholar
  5. 5.
    Xiao, J.Z., Song, Q., Wang, D.W.: A Learning Control Scheme Based on Neural Network for Repeatable Robot Trajectory Tracking. In: 14th IEEE Symposium on Intelligent Control/ Intelligent Systems and Semiotics, ISIC/ISAS 1999, USA (1999)Google Scholar
  6. 6.
    Psaltis, D., Sideris, A., Yamamura, A.A.: A Multilayered Neural Network Controller. IEEE Contr. Syst. Mag. 8, 17–20 (1988)CrossRefGoogle Scholar
  7. 7.
    Song, Q., Xiao, J.Z., Soh, Y.C.: Robust Back Propagation Training Algorithm for Multilayered Neural Tracking Controller. IEEE T. Neural Network 10, 1133–1141 (1999)CrossRefGoogle Scholar
  8. 8.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  9. 9.
    Polycarpou, M.M., Ioannou, P.A.: Learning and Convergence Analysis of Neural-type Structured Networks. IEEE T. Neural Network 3, 39–50 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gaoxiang Ouyang
    • 1
  • Xiaoli Li
    • 1
  • Xinping Guan
    • 1
  • Zhiqiang Zhang
    • 1
  • Xiuling Zhang
    • 1
  • Ruxu Du
    • 2
  1. 1.Institute of Electrical EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.Dept. Automation & Computer Aided Engg.Chinese University of Hong KongChina

Personalised recommendations