Identification methods for Hammerstein nonlinear systems

  • Authors:

  • Feng Ding;Xiaoping Peter Liu;Guangjun Liu

  • Affiliations:

  • Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, PR China and Control Science and Engineering Research Center, Jiangnan Univ ...;Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada K1S 5B6;Department of Aerospace Engineering, Ryerson University, Toronto, Canada M5B 2K3

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2011

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Abstract

This paper considers the identification problems of the Hammerstein nonlinear systems. A projection and a stochastic gradient (SG) identification algorithms are presented for the Hammerstein nonlinear systems by using the gradient search method. Since the projection algorithm is sensitive to noise and the SG algorithm has a slow convergence rate, a Newton recursive and a Newton iterative identification algorithms are derived by using the Newton method (Newton-Raphson method), in order to reduce the sensitivity of the projection algorithm to noise, and to improve convergence rates of the SG algorithm. Furthermore, the performances of these approaches are analyzed and compared using a numerical example, including the parameter estimation errors, the stationarity and convergence rates of parameter estimates and the computational efficiency.