Identification of Dual-Rate Sampled Hammerstein Systems with a Piecewise-Linear Nonlinearity Using the Key Variable Separation Technique

Algorithms

Ying-Ying Wang¹  Xiang-Dong Wang²  Dong-Qing Wang¹ 

[1] College of Automation Engineering, Qingdao University, 308 Ningxia Road, Qingdao 266071, China; E-Mail:;College of Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China; E-Mail:

关键词: Hammerstein system;    dual-rate;    key variable separation technique;    polynomial transformation;    least squares;   

DOI  :  10.3390/a8030366

来源: mdpi

【 摘 要 】

The identification difficulties for a dual-rate Hammerstein system lie in two aspects. First, the identification model of the system contains the products of the parameters of the nonlinear block and the linear block, and a standard least squares method cannot be directly applied to the model; second, the traditional single-rate discrete-time Hammerstein model cannot be used as the identification model for the dual-rate sampled system. In order to solve these problems, by combining the polynomial transformation technique with the key variable separation technique, this paper converts the Hammerstein system into a dual-rate linear regression model about all parameters (linear-in-parameter model) and proposes a recursive least squares algorithm to estimate the parameters of the dual-rate system. The simulation results verify the effectiveness of the proposed algorithm.

【 授权许可】

CC BY   
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