Identification of robotic systems with hysteresis using Nonlinear AutoRegressive eXogenous input models. (24th June 2017)

• Record Type: Journal Article
• Authors: Zhang, Wanxin; Zhu, Jihong; Gu, Dongbing
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 3(2017:May/Jun.)
• Publication Date: 2017-06-24
• Subjects: Nonlinear identification -- NARX model -- hysteresis -- prediction error sum of squares -- term selection; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417705845
• Languages: English
• ISSNs: 1729-8806

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.