Closed-loop identification: application to the estimation of limb impedance in a compliant environment.
The force and position data used to construct models of limb impedance are often obtained from closed-loop experiments. If the system is tested in a stiff environment, it is possible to treat the data as if they were obtained in open loop. However, when limb impedance is studied in a compliant environment, the presence of feedback cannot be ignored. While unbiased estimates of a system can be obtained directly using the prediction error method, the same cannot be said when linear regression or correlation analysis is used to fit nonparametric time- or frequency-domain models. We develop a prediction error minimization-based identification method for a nonparametric time-domain model augmented with a parametric noise model. The identification algorithm is tested on a dynamic mass-spring-damper system and returns consistent estimates of the system's properties under both stiff and compliant feedback control. The algorithm is then used to estimate the impedance of a human elbow joint in both stiff and compliant environments.
Department of Electrical and Computer Engineering, Schulich School of Engineering at the University of Calgary, AB T2N 1N4, Canada. dwestwic@ucalgary.ca
David T
DT
Westwick
Eric J
EJ
Perreault
D000465:Algorithms; D004550:Elbow / Q000502:physiology*; D017097:Electric Impedance; D005246:Feedback; D006801:Humans; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric