Tension control for multi-span web transport systems with roll inertia uncertainty compensation using RBF neural network

Authors

  • Thi Ly Tong Hanoi University of Industry
  • Thanh Tung Nguyen Hanoi University of Science and Technology
  • Duc Duong Minh
  • Danh Huy Nguyen Hanoi University of Science and Technology
  • Tung Lam Nguyen Hanoi University of Science and Technology

Keywords:

Web Tension Control, Web Transport Systems, Multispan Roll to Roll (R2R) Systems, Backstepping controller, Lyapunov Stability Theorem, Neural Radial Basis Function (RBF)

Abstract

A roll-to-roll system which is a flexible multi-shaft web transport system is very common in the industries such as paper, metal processing, polymers, fabric and so on. However, web tension and speed control of the roll-to-roll system are difficult because of the nature of the system including multi-input multi-output, time variance, and nonlinearity. In this paper, modeling and controling problems of the multispan roll to roll systerm are investigated. From the governing equations of the web dynamics, a backstepping based controller with Neural RBF for web velocity and tension regulation is developed. The neural network design is based on the Radial Basis Function network that estimates the uncertainty of roll inertia. Simulation results show the effectiveness of the proposed approach.

Downloads

Download data is not yet available.

Published

2021-05-27

How to Cite

Tong, T. L., Nguyen, T. T., Dương Minh, Đức, Nguyen, D. H., & Nguyen, T. L. (2021). Tension control for multi-span web transport systems with roll inertia uncertainty compensation using RBF neural network. Measurement, Control, and Automation, 2(1). Retrieved from https://mca-journal.org/index.php/mca/article/view/45

Issue

Section

Control Theory and Application