针对卷绕系统工作时卷料张力波动较大问题，提出了一种基于神经网络区间观测器的反演非奇异快速终端滑模张力控制方法。构建卷绕系统数学模型，利用神经网络逼近卷绕系统中卷料半径、惯量等参数变化部分所引起的随机响应，设计区间状态观测器估计系统转速、卷料张力的上下界。根据估计出的状态值，构建反演非奇异终端滑模控制器，使张力跟踪误差在有限时间内快速收敛到零，有效增强了系统鲁棒性能。仿真实验结果表明，所设计的控制方法使卷料上的张力在 1.6 s 后达到给定值并保持恒定，相较于常规的滑模控制器和已发表文献中的滑模控制器，其调节时间分别减少了57%和33%，证明了所提出控制方法的有效性和可靠性，满足卷绕设备收卷工艺的要求。
Aiming at the problem of large fluctuation of winding tension when the winding system is working, an inverse nonsingular fast terminal sliding mode tension control method based on neural network interval observer is proposed. The mathematical model of the winding system is constructed, and the neural network is used to approximate the random response caused by the change of parameters such as the radius and inertia of the winding system. The interval state observer is designed to estimate the upper and lower bounds of the system speed and the winding tension. According to the estimated state value, the backstepping nonsingular terminal sliding mode controller is constructed to make the tension tracking error converge to zero quickly in a finite time, which effectively enhances the robust performance of the system. The simulation results show that the designed control method makes the tension on the coil reach a given value and remain constant after 1.6 s. Compared with the conventional sliding mode controller and the sliding mode controller in the published literature, the adjustment time is reduced by 57 % and 33 % respectively, which proves the effectiveness and reliability of the proposed control method and meets the requirements of the winding process of the winding equipment.