State Estimation For An Agonistic-Antagonistic Muscle System
For researchers in assistive technology and rehabilitation, this work provides estimation frameworks for complex muscle systems, though it is incremental as it applies existing observer designs to a specific muscle model.
This paper addresses state and activation signal estimation in a nonlinear agonistic-antagonistic muscle system with parameter uncertainty and unknown inputs, presenting three observers (high gain, sliding mode, adaptive sliding mode). Numerical simulations show comparable, reliable estimates in noise-free and noisy cases.
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.