Yu Feng, Mao Zhizhong, Yuan Ping, He Dakuo, Jia Mingxing
Department of Control Theory and Control Engineering, Northeastern University, No.11, Lane 3, WenHua Road, HePing District, Shenyang, China.
ISA Trans. 2017 Sep;70:104-115. doi: 10.1016/j.isatra.2017.05.012. Epub 2017 Jun 11.
This paper focuses on the recursive parameter estimation for the single input single output Hammerstein-Wiener system model, and the study is then extended to a rarely mentioned multiple input single output Hammerstein-Wiener system. Inspired by the extended Kalman filter algorithm, two basic recursive algorithms are derived from the first and the second order Taylor approximation. Based on the form of the first order approximation algorithm, a modified algorithm with larger parameter convergence domain is proposed to cope with the problem of small parameter convergence domain of the first order one and the application limit of the second order one. The validity of the modification on the expansion of convergence domain is shown from the convergence analysis and is demonstrated with two simulation cases.
本文聚焦于单输入单输出 Hammerstein-Wiener 系统模型的递归参数估计，随后将该研究扩展至一个较少被提及的多输入单输出 Hammerstein-Wiener 系统。受扩展卡尔曼滤波算法启发，从一阶和二阶泰勒近似推导出两种基本递归算法。基于一阶近似算法的形式，提出一种具有更大参数收敛域的改进算法，以应对一阶算法参数收敛域小以及二阶算法应用受限的问题。从收敛性分析表明了对收敛域扩展进行修改的有效性，并通过两个仿真案例进行了验证。
