Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Department of Material Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
ISA Trans. 2023 May;136:651-662. doi: 10.1016/j.isatra.2022.11.020. Epub 2022 Nov 26.
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.
张力控制对于大多数卷对卷（R2R）生产系统保持良好的产品质量至关重要。以前的工作主要集中在提高张力控制器的抗扰性能上。在这里，设计了一种鲁棒线性参数变化模型预测控制（LPV-MPC）方案，以提高用于柔性薄膜应用的沉积材料的试验性 R2R 系统的张力跟踪性能。由于与模型不确定性和 R2R 系统中缓慢变化的动力学相关的干扰，张力控制器的性能可能会下降。我们引入了一种分别处理这两个干扰源的方法。该控制器使用增量模型消除了名义模型与实际系统之间不匹配引起的误差。基于管的 MPC 公式结合预定参数充分更新模型并纠正时变动态。在 MPC 中加入了额定电机扭矩的限制，以保持控制器的可靠性并避免机器故障。我们通过对实际 R2R 系统的仿真来说明我们的控制算法的操作。该控制器在快速瞬态响应和无偏置张力跟踪方面优于基准。它还表现出对参数不确定性变化的免疫能力。
