Tension control for multi-span web transport systems with roll inertia uncertainty compensation using RBF neural network
Keywords:Web Tension Control, Web Transport Systems, Multispan Roll to Roll (R2R) Systems, Backstepping controller, Lyapunov Stability Theorem, Neural Radial Basis Function (RBF)
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.
How to Cite
Copyright (c) 2021 Measurement, Control, and Automation
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.