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Identification of Continuous Time Linear Parameter Varying models based on Reinitialized Partial Moments | IEEE Conference Publication | IEEE Xplore

Identification of Continuous Time Linear Parameter Varying models based on Reinitialized Partial Moments


Abstract:

In this paper we propose a practical identification solution for Continuous-Time (CT) Input-Output (IO) Linear Parameter Varying (LPV) systems. The main steps of the prop...Show More

Abstract:

In this paper we propose a practical identification solution for Continuous-Time (CT) Input-Output (IO) Linear Parameter Varying (LPV) systems. The main steps of the proposed method are: (a) CT-Reinitialized Partial Moments (RPM) is used as modeling tools; (b) the use of a Kronecker product framework to obtain linear regression form; (c) a least squares identification algorithm is used to estimate the parameters. Since the least squares technique does not give consistent estimates, the instrumental variables method is proposed to achieve this property. The model can represent both linear and non-linear behavior with polynomial scheduling parameters dependency. We assume that both the IO and the scheduling parameters are measured. Monte Carlo simulation analysis demonstrates the performance of the proposed identification approach.
Date of Conference: 23-26 October 2016
Date Added to IEEE Xplore: 22 December 2016
ISBN Information:
Conference Location: Florence, Italy

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