目的 为了满足卷到卷多功能印刷机系统在张力稳定性方面的需求，提出一种新型张力控制方法，旨在解决张力耦合问题，并增强系统的抗干扰能力和鲁棒性。方法 通过分析张力系统的运行机制，建立放卷单元与印刷单元之间的两级张力耦合数学模型，并推导系统的动态解耦与静态解耦模型。基于自抗扰控制（Active Disturbance Rejection Control，ADRC）方法，引入径向基函数（Radial Basis Function，RBF）神经网络对非线性组合部分的关键参数进行在线调整，设计一种RBF神经网络自抗扰控制器（RBF-ADRC）。结果 与其他控制器的仿真对比结果显示，RBF-ADRC能够有效实现张力系统的解耦控制，显著提升系统的恒张力稳定性，并增强其内部鲁棒性和抗干扰能力。结论 RBF神经网络自抗扰控制策略成功解决了张力耦合控制问题，不仅实现了系统的恒张力稳定运行，还显著提高系统的抗干扰能力和鲁棒性，实现了研究目标。

In order to meet the demand of tension stability in reel-to-reel multifunctional printing machine system, the work aims to propose a new tension control method to solve the tension coupling problem and enhance the anti-interference ability and robustness of the system. By analyzing the operating mechanism of the tension system, the two-stage tension coupling mathematical model between the unwinding unit and the printing unit was established, and the dynamic decoupling and static decoupling models of the system were derived. Based on the Active Disturbance Rejection Control (ADRC) method, Radial Basis Function (RBF) neural network was introduced to adjust the key parameters of the nonlinear composite part online. An RBF neural network active disturbance rejection controller (RBF-ADRC) was designed. Compared with the simulation results of other controllers, RBF-ADRC could effectively realize the decoupling control of the tension system, significantly improve the constant tension stability of the system, and enhance the internal robustness and anti-interference ability. RBF neural network active disturbance rejection control strategy successfully solves the tension coupling control problem, which not only realizes the stable operation of the system at constant tension, but also significantly improves the anti-interference ability and robustness of the system, achieving the research goal.