In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a radial basis function (RBF) neural network with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop system, a geometric analysis based ILC method is adopted in the outer loop. Finally, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.

中文翻译：

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精炼过程中基于几何分析的纤维长度分布输出PDF整形的双闭环迭代学习控制


为了提高纸浆质量、降低能耗，纤维长度分布（FLD）一般被作为磨浆过程的重要工艺指标之一。考虑到传统的纤维长度均值和方差无法充分表征非高斯分布特性，本文提出了一种基于几何分析的双闭环迭代学习控制（ILC）方法，用于概率密度函数（PDF）整形。精炼过程中输出FLD。首先，利用高斯型径向基函数 (RBF) 神经网络来近似内循环中的平方根 PDF，其中通过使用 ILC 在任意两个相邻批次之间调整 RBF 基函数参数（中心和宽度）律，并应用子空间辨识方法建立权向量的状态空间模型。然后，为了加快闭环系统的收敛速度，外环采用了基于几何分析的ILC方法。最后，仿真和实验证明了该方法的有效性和实用性。