Nanyang Cigarette Factory of Henan China Tobacco Industry Co., Ltd, Nanyang, 473000, People's Republic of China.
College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, People's Republic of China.
Sci Rep. 2023 Jun 16;13(1):9752. doi: 10.1038/s41598-023-36888-6.
An expanded-sandwich system is a nonlinear extended block-oriented system in which memoryless elements in conventional block-oriented systems are displaced by memory submodels. Expanded-sandwich system identification has received extensive attention in recent years due to the powerful ability of these systems to describe actual industrial systems. This study proposes a novel recursive identification algorithm for an expanded-sandwich system, in which an estimator is developed on the basis of parameter identification error data rather than the traditional prediction error output information. In this scheme, a filter is introduced to extract the available system information based on miserly structure layout, and some intermediate variables are designed using filtered vectors. According to the developed intermediate variables, the parameter identification error data can be obtained. Thereafter, an adaptive estimator is established by integrating the identification error data compared with the classic adaptive estimator based on the prediction error output information. Thus, the design framework introduced in this research provides a new perspective for the design of identification algorithms. Under a general continuous excitation condition, the parameter estimation values can converge to the true values. Finally, experimental results and illustrative examples indicate the availability and usefulness of the proposed method.
扩展三明治系统是一种非线性扩展块状系统，其中传统块状系统中的无记忆元件被记忆子模型取代。由于这些系统能够描述实际的工业系统，因此扩展三明治系统辨识近年来受到了广泛关注。本研究提出了一种新颖的扩展三明治系统递归辨识算法，其中基于参数辨识误差数据而不是传统的预测误差输出信息来开发估计器。在该方案中，引入了一个滤波器，根据吝啬的结构布局从系统中提取可用的信息，并使用滤波向量设计一些中间变量。根据所开发的中间变量，可以获得参数辨识误差数据。然后，通过将基于预测误差输出信息的经典自适应估计器与比较的辨识误差数据相结合，建立自适应估计器。因此，本研究引入的设计框架为辨识算法的设计提供了新的视角。在一般的连续激励条件下，参数估计值可以收敛到真实值。最后，实验结果和实例表明了所提出方法的有效性和实用性。
