State Estimation For An Agonistic-Antagonistic Muscle System

TL;DR

This paper develops and analyzes three observers for estimating states and activations in a nonlinear agonistic-antagonistic muscle system, accounting for uncertainties and noise, with potential applications in rehabilitation and prosthetics.

Contribution

It introduces three convergence-guaranteed observers for complex muscle models considering uncertainties and noise, advancing state estimation in biomechanical systems.

Findings

Observers perform reliably in noise-free conditions.

All three observers provide comparable estimates in simulations.

The framework supports development of adaptive assistive devices.

Abstract

Research on assistive technology, rehabilitation, and prosthesis 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. To facilitate numerical simulations, a backstepping controller is employed to drive the muscle system to track a desired trajectory. Numerical simulations reveal that the three observers are comparable and provide reliable estimates in noise free and noisy cases. The proposed schemes may serve as frameworks for estimation of complex multi-muscle systems, which could lead to intelligent exercise machines for adaptive training and rehabilitation, and adaptive prosthetics and exoskeletons.