Multi-innovation stochastic gradient algorithm for output error systems based on the auxiliary model

Authors:
Dongqing Wang;Feng Ding;Peter X. Liu
Affiliations:
College of Automation Engineering, Qingdao University, Qingdao, P.R. China;Control Science and Engineering Research Center, Jiangnan University, Wuxi, P.R. China;Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

Citing 5
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Performance analysis of the auxiliary models based multi-innovation stochastic gradient estimation algorithm for output error systems

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Abstract

This paper combines the multi-innovation theory with the auxiliary model identification idea to present the auxiliary model based multi-innovation stochastic gradient algorithm by expanding the scalar innovation to an innovation vector and introducing the innovation length. Convergence analysis in the stochastic framework indicates that the parameter estimation error consistently converges to zero under certain excitation condition. Finally, we illustrate and test the proposed algorithm with an example.