Extended Kalman filtering for state estimation of a Hill muscle model

Content Provider	Semantic Scholar
Author	Mohammadi, Hanieh Khademi, Gholamreza Nguyen, Thang Trung Simon, Daniel Jean Richter, Hanz
Copyright Year	2018
Abstract	The objectives of this study are five-fold: (i) design an extended Kalman filter (EKF) for the single-muscle and two-muscle Hill models; (ii) design an EKF for unknown-input estimation of the muscle models; (iii) investigate the detectability of the muscle models; (iv) examine the robustness of the EKF to modeling errors; and (v) improve state estimation by incorporating physical constraints into the estimator. Two noisy measurements are available for state estimation: muscle length, which is measured from joint angles; and muscle activation, which is measured from electromyography sensors. Simulation results verify that the EKF is an effective approach for estimation of the activation signals and the states of the system if the system is detectable; the EKF outperforms the high gain observer; and the EKF is robust to modeling errors. The standard deviations of the estimation errors are in the range 0.01-0.1 mm for the muscle lengths, which is one to two orders of magnitude more accurate than the measurements. The standard deviations of the dimensionless muscle activation estimation errors are in the range 0.01-0.02, which is one order of magnitude more accurate than the measurements. A projection approach accounts for constraints to further improve the estimates.
Starting Page	384
Ending Page	394
Page Count	11
File Format	PDF HTM / HTML
DOI	10.1049/iet-cta.2017.0645
Volume Number	12
Alternate Webpage(s)	https://academic.csuohio.edu/simond/pubs/Mohammadi2018b.pdf
Alternate Webpage(s)	https://doi.org/10.1049/iet-cta.2017.0645
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article