Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems

  • Authors:

  • Jing Chen;Yan Zhang;Ruifeng Ding

  • Affiliations:

  • Wuxi Professional College of Science and Technology, Wuxi 214028, PR China and School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, PR China;Wuxi Institute of Technology, Wuxi 214121, PR China;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, PR China and School of Communication and Control Engineering, Jiangnan Univ ...

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 2010

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Abstract

This paper considers the identification problem for multi-input multi-output nonlinear systems. The difficulty of the parameter identification of such systems is that the information vector in the identification model contains unknown variables. The solution is using the auxiliary model identification idea to overcome the difficulty. An auxiliary model based multi-innovation extended stochastic gradient algorithm is presented by expanding the innovation vector to an innovation matrix. The proposed algorithm uses not only the current innovation but also the past innovations at each recursion and thus the parameter estimation accuracy can be improved. The numerical example shows that the proposed algorithm is effective.