State Estimation For An Agonistic‐Antagonistic Muscle System. Issue 1 (24th October 2018)

• Record Type: Journal Article
• Authors: Nguyen, Thang Tien; Warner, Holly; La, Hung; Mohammadi, Hanieh; Simon, Dan; Richter, Hanz
• Other Names: Karimi Hamid Reza guestEditor.; Wang Yueying guestEditor.; Shi Peng guestEditor.
• Is Part Of: Asian journal of control. Volume 21:Issue 1(2019)
• Page Start: 354
• Page End: 363
• Publication Date: 2018-10-24
• Subjects: Adaptive sliding mode -- high gain observer -- Hill muscle model -- human muscles -- state estimation -- sliding mode observer; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.1916
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: Research on assistive technology, rehabilitation, and prosthetics requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic‐antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of estimating the state variables and activation signals of the dual muscle system is considered. In this work, parameter uncertainty and unknown inputs are taken into account for the estimation problem. Three observers are presented: a high gain observer, a sliding mode observer, and an adaptive sliding mode observer. Theoretical analysis shows the convergence of the three observers. Numerical simulations reveal that the three observers are comparable and provide reliable estimates.