An IV-SVM-based approach for identification of state-space LPV models under generic noise conditions

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20161402196745
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(1) Complex Systems Control Laboratory (CSCL), College of Engineering, University of Georgia, Athens; GA; 30602, United States; (2) Control Systems Group, Dept. of Electrical Engineering, Eindhoven University of Technology, P.O.Box 513, Eindhoven; 5600 MB, Netherlands; (3) Dept. of Electrical Engineering, Qatar University, Doha, Qatar | 1600年 / Cybernet Systems; et al.; Kozo Keikaku Engineering (KKE); MathWorks; Mitsubishi Electric; Springer卷 / Institute of Electrical and Electronics Engineers Inc.期
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This paper presents a nonparametric identification method for state-space linear parameter-varying (LPV) models using a modified support vector machine (SVM) approach. While most LPV identification schemes in the state-space form fall under the general category of parametric methods; regularization-based SVMs provide a viable alternative to model scheduling dependencies; without the need of specifying the dependency structure and with an attractive bias-variance trade-off. In this paper; a solution is proposed for nonparametric identification of LPV state-space models in terms of least-squares SVMs (LS-SVM) and is then extended in a way that the proposed estimation is robust to errors in the noise model estimation. The so-called instrumental variables (IV) method has been used in linear system identification for quite some time; and has recently seen its application in the identification of both nonlinear and LPV systems in the input-output (IO) form. The IV method reduces the bias in estimated LPV state-space models in case the noise model is not estimated properly or is unknown. In the proposed method of this paper; the attractive bias-variance trade-off properties of LS-SVMs are combined with statistical properties of IV-based methods to give robust estimates of the functional dependencies. Numerical examples are provided to compare the performances of the proposed IV-based technique with the LS-SVM-based LPV model identification methods. © 2015 IEEE;
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