System Identification and Two-Degree-of-Freedom Control of Nonlinear, Viscoelastic Tissues

TL;DR

This paper introduces a two-degree-of-freedom force control scheme for muscle tissues, enabling precise, automated measurements of muscle shortening velocity with minimal overshoot and user input, using system identification and nonlinear modeling.

Contribution

It presents a novel control approach combining feedback and inverse-model-based feedforward control for nonlinear muscle tissues, improving automation and accuracy.

Findings

Successfully controlled force with minimal overshoot and settling time.

Validated robustness across different muscle types.

Reduced user input for muscle force measurements.

Abstract

Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. Results: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. Conclusion: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. Significance: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials.