| Autor: |
Zhiyi Chen, Boning Qu, Baoyang Jiang, Stephen R. Forrest, Jun Ni |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
ISA transactions. |
| ISSN: |
1879-2022 |
| Popis: |
Tension control is critical for maintaining good product quality in most roll-to-roll (R2R) production systems. Previous work has primarily focused on improving the disturbance rejection performance of tension controllers. Here, a robust linear parameter-varying model predictive control (LPV-MPC) scheme is designed to enhance the tension tracking performance of a pilot R2R system for deposition of materials used in flexible thin film applications. The performance of a tension controller may degrade due to disturbances associated with model uncertainties and the slowly-changing dynamics in R2R systems. We introduce a method that separately treats these two sources of disturbance. The controller utilizes an incremental model to eliminate the errors caused by the mismatch between the nominal model and the actual system. A tube-based MPC formulation combined with scheduled parameters adequately updates models and corrects for the time-varying dynamics. Constraints on the rated motor torque are incorporated in the MPC to maintain the controller reliability and avoid machine failures. We illustrate the operation of our control algorithm through simulation of an actual R2R system. The controller outperforms the benchmarks in terms of fast transient response and offset-free tension tracking. It also demonstrates immunity from variations due to parametric uncertainties. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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