Data-driven integrated modeling and intelligent control methods of grinding process

  • Authors:

  • Jiesheng Wang;Xianwen Gao;Shifeng Sun

  • Affiliations:

  • College of Information Science and Engineering, Northeastern University, Shenyang, China,School of Electronic and Information Engineering, Liaoning University of Science and Technology, Anshan, Ch ...;College of Information Science and Engineering, Northeastern University, Shenyang, China;School of Electronic and Information Engineering, Liaoning University of Science and Technology, Anshan, China

  • Venue:
  • ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2012

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Abstract

The grinding process is a typical complex nonlinear multivariable process with strongly coupling and large time delays. Based on the data-driven modeling theory, the integrated modeling and intelligent control method of grinding process is carried out in the paper, which includes the soft-sensor model of the key technology indicators (grinding granularity and mill discharge rate) based on wavelet neural network optimized by the improved shuffled frog leaping algorithm (ISFLA), the optimized set-point model utilizing case-based reasoning and the self-tuning PID decoupling controller. Simulation results and industrial application experiments clearly show the feasibility and effectiveness of control methods and satisfy the real-time control requirements of the grinding process.