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A multi-model identification method for the fiber stretching process based on the EM algorithm

机译：基于EM算法的纤维拉伸过程多模型识别方法

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In the fiber production process, the stretching process plays a key role in the quality of the final fiber product. Due to the fiber stretching process with inborn nonlinearity, the performance of a single controller and an optimizer may be compromised or even unsatisfactory. Thus, we consider a multi-model identification method for the fiber stretching process. The dynamic transitions among different operating points are achieved by the change of the operating conditions in the fiber stretching process. To excite all of the nonlinearity character in the fiber stretching process, the transitions among different operating conditions is achieved. The structure of each sub-models, operating points, operating range are assumed. Based on the input output data of the process, a linear parameter varying (LPV) model is built by applying a probability identification method. To achieve the smoothly connected among the different operating conditions, an exponential function is used. Then a global LPV model is constructed by synthesizing the local models. Simulated results show that the LPV method has the effectiveness in solving the inherent nonlinearity of the fiber stretching process.
机译：在纤维生产过程中，拉伸过程对最终纤维产品的质量起着关键作用。由于具有固有的非线性的纤维拉伸过程，单个控制器和优化器的性能可能会受到损害甚至不令人满意。因此，我们考虑了一种用于纤维拉伸过程的多模型识别方法。通过改变纤维拉伸过程中的操作条件，可以实现不同操作点之间的动态过渡。为了激发纤维拉伸过程中的所有非线性特性，实现了不同操作条件之间的过渡。假定每个子模型的结构，工作点，工作范围。基于过程的输入输出数据，通过应用概率识别方法构建线性参数变化（LPV）模型。为了实现不同操作条件之间的平滑连接，使用了指数函数。然后，通过合成局部模型来构建全局LPV模型。仿真结果表明，LPV方法可以有效解决纤维拉伸过程中固有的非线性问题。

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