Asymptotic statistical analysis of virtual reference feedback tuning control

Author(s)
Hong Wang-jian, Dipartimento di Elettronica, Informazione Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Guo Xiao-yong, School of Science, Henan University of Engineering, Zhengzhou 451191, China

Abstract
Virtual reference feedback tuning control is a data-driven control strategy. No model identification of the plant is needed in this method. As the asymptotic covariance matrix is an important factor in the whole system identification theory. So here the error about the unknown parameter estimation is derived through Taylor series expression. Then the corresponding covariance matrix of the parameter estimation error is established. The two diagonal sub-matrices in the covariance matrix are obtained using some matrix operations. These two diagonal sub- matrices are the asymptotic covariance matrix expression of the two unknown parameter estimation vectors in the closed-loop system. Based on this asymptotic covariance matrix, an optimal filter is obtained by solving an optimization problem which includes some trace operation. Finally, the efficiency and possibility of the proposed strategy can be confirmed by the simulation example results.

Keywords
Virtual reference feedback tuning control; Asymptotic analysis; Stochastic optimization

Cite this paper
Hong Wang-jian, Guo Xiao-yong, Asymptotic statistical analysis of virtual reference feedback tuning control, SCIREA Journal of Electrics, Communication. Vol. 1 , No. 1 , 2016 , pp. 1 - 14 .

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