Brief paper: Weighted least squares based recursive parametric identification for the submodels of a PWARX system

Authors:
Wen-Xiao Zhao;Tong Zhou
Affiliations:
Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China and National Center for Mathe ...;Department of Automation, TNList, Tsinghua University, Beijing 100084, PR China
Venue:
Automatica (Journal of IFAC)
Year:
2012

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Abstract

A piecewise affine autoregressive system with exogenous inputs (PWARX) is composed of a finite number of ARX subsystems, each of which corresponds to a polyhedral partition of the regression space. In this work a weighted least squares (WLS) estimator is suggested to recursively estimate the parameters of the ARX submodels, in which a sequence of kernel functions are introduced. Conditions on the input signal and the PWARX system are imposed to guarantee the almost sure convergence of the WLS estimates. Some numerical examples are included to illustrate performances of the algorithm.