Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle

  • Authors:

  • Dongqing Wang;Feng Ding;Yanyun Chu

  • Affiliations:

  • College of Automation Engineering, Qingdao University, Qingdao 266071, China;Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122, China;Liaocheng Vocational and Technical College, Liaocheng 252000, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

This paper concerns parameter identification of Hammerstein output error moving average systems with a two-segment piecewise nonlinearity. By combining the key-term separation principle and the data filtering technique, we transfer the Hammerstein model into two regression identification models, and present a data filtering based recursive least squares method to estimate the parameters of these two identification models. The proposed algorithm achieves a higher computational efficiency than the standard approach by using covariance matrices of smaller dimensions from the two identification models instead of one identification model in the standard approach.