工业智能系统前沿征稿+无模型自适应迭代学习控制在注塑过程测量扰动抑制的应用

Application of Model-Free Adaptive Iterative Learning Control in Measurement Disturbance Suppression of Injection Molding Process

  • 摘要: 在注塑成型过程中,注射速度对塑料制品的品质起着决定性作用。在实际的注塑过程中,注塑机可能会受到扰动作用,从而使注射速度无法跟踪设定的期望速度,最终影响产品质量。针对这一问题,本文首先描述注塑过程并建立注塑过程注射段模型。注射段模型具有强非线性、不确定等特征,难以使用传统策略进行控制器设计,本文针对这一问题建立具有迭代特征的数据模型,基于最优二次型指标函数设计并实现了注射段速度控制策略和伪偏导数在迭代轴上的学习。为了抑制外部扰动对注射速度的影响,在所提MFAILC策略的基础上引入衰减因子,在期望意义下证明了MFAILC算法的输出跟踪误差均值为0。最后在MATLAB中利用一个实例验证本文所提控制策略的可行性。

     

    Abstract: In the injection molding process, the injection speed plays a decisive role in the quality of plastic products. In the actual injection molding process, the injection molding machine may be subjected to disturbances, which makes the injection speed unable to track the desired speed, ultimately affecting the product quality. To address this issue, this paper first describes the injection molding process and establishes a model for the injection section of the injection molding process. The injection section model is characterized by strong nonlinearity and uncertainty, making it difficult to design a controller using traditional strategies. Aiming at this problem, this paper establishes a data model with iterative characteristics, designs and implements an injection section speed control strategy The learning of pseudo partial derivatives on the iterative axis based on the optimal quadratic index function. In order to suppress the influence of external disturbances on the injection speed, a decay factor is introduced based on the proposed MFAILC (Model-Free Adaptive Iterative Learning Control) strategy, and it is proved in the sense of expectation that the mean value of the output tracking error of the MFAILC algorithm is zero. Finally, an example is used in MATLAB to verify the feasibility of the control strategy proposed in this paper.

     

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