Adaptive tracking and recursive identification for Hammerstein systems

Authors:
Wen-Xiao Zhao;Han-Fu Chen
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, PR China;Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 55 Zhongguancundonglu, Beijing 100190, PR China
Venue:
Automatica (Journal of IFAC)
Year:
2009

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Abstract

A weighted least squares (WLS) based adaptive tracker is designed for a class of Hammerstein systems. It is proved that the tracking error is asymptotically minimized. Incorporating with the diminishing excitation technique, the minimality of the tracking error and strong consistency of the estimates for parameters of the system are simultaneously achieved. Numerical examples are given and the simulation results are consistent with the theoretical analysis.