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Identification of robotic systems with hysteresis using Nonlinear AutoRegressive eXogenous input models

Zhang, Wanxin and Zhu, Jihong and Gu, Dongbing (2017) 'Identification of robotic systems with hysteresis using Nonlinear AutoRegressive eXogenous input models.' International Journal of Advanced Robotic Systems, 14 (3). ISSN 1729-8806

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Abstract

Identification of robotic systems with hysteresis is the main focus of this article. Nonlinear AutoRegressive eXogenous input models are proposed to describe the systems with hysteresis, with no limitation on the nonlinear characteristics. The article introduces an efficient approach to select model terms. This selection process is achieved using an orthogonal forward regression based on the leave-one-out cross-validation. A sampling rate reduction procedure is proposed to be incorporated into the term selection process. Two simulation examples corresponding to two typical hysteresis phenomena and one experimental example are finally presented to illustrate the applicability and effectiveness of the proposed approach.

Item Type: Article
Uncontrolled Keywords: Nonlinear identification, NARX model, hysteresis, prediction error sum of squares, term selection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 11 Nov 2018 16:00
Last Modified: 11 Nov 2018 16:00
URI: http://repository.essex.ac.uk/id/eprint/23454

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