Identification of systems with slowly sampled outputs using LPV model
Identification of systems with slowly sampled outputs using LPV model
复制标题
使用 LPV 模型识别具有缓慢采样输出的系统
DOI：
10.1016/j.compchemeng.2018.02.022
复制
发表时间：
2018
影响因子：
4.3
通讯作者：
Liu Xin
中科院分区：
文献类型：
--
作者：
Yan Wengang;Zhu Yucai;Zhu Lingyu;Liu Xin
研究了输出慢采样系统的辨识问题。采用具有多模型结构的线性变参数（LPV）模型来解决该问题。输出误差（OE）方法被用来估计模型参数。首先，局部模型和加权函数的估计分别使用优化方法。然后，松弛迭代法的发展，以细化的总模型的参数。对于LPV模型结构的确定，提出了一种工程方法，结合过程知识与所谓的最终输出误差准则（FOE）。利用仿真数据和工业数据对该方法进行了验证。在工业实例研究中，LPV模型比线性动态模型和静态非线性模型对产品质量的预测精度更高，说明了在软测量开发中使用测试信号的必要性。
Identification of systems with slowly sampled output is studied. A linear parameter varying (LPV) model with multi-model structure is used to solve the problem. The output error (OE) method is used to estimate model parameters. Firstly, the local models and weighting functions are estimated separately using optimization methods. Then, a relaxation iteration method is developed to refine the parameters of the total model. For LPV model structure determination, an engineering approach is proposed that combines process knowledge with the so-called final output error criteria (FOE). The method is verified using both simulation data and industrial data. In the industrial case study, the LPV models give more accurate prediction of product qualities than that of a linear dynamic model and that of a static nonlinear model; the result also indicates the necessity of using test signals in soft-sensor development.