A simulation-based study for optimizing proportional-integral-derivative controller gains for different control strategies of an active upper extremity model using experimental data

Abstract

This study investigates the effect of PID controller gains, reaction time, and initial muscle activation values on active human model behavior while comparing three different control strategies. The controller gains and reaction delays were optimized using published experimental data focused on the upper extremity. The data describes the reaction of five male subjects in four tests based on two muscle states (relaxed and tensed) and two states of awareness (open and closed eye). The study used a finite element model of the left arm isolated from the Global Human Body Models Consortium (GHBMC) average male simplified occupant model for simulating biomechanical simulations. Major skeletal muscles of the arm were modeled as 1D beam elements and assigned a Hill-type muscle material. Angular position control, muscle length control, and a combination of both were used as a control strategy. The optimization process was limited to 4 variables; three Proportional-Integral-Derivative (PID) controller gains and one reaction delay time. The study assumed the relaxed and tensed condition require distinct sets of controller gains and initial activation and that the closed-eye simulations can be achieved by increasing the reaction delay parameter. A post-hoc linear combination of angle and muscle length control was used to arrive at the final combined control strategy. The premise was supported by variation in the controller gains depending on muscle state and an increase in reaction delay based on awareness. The CORA scores for open-eye relaxed, closed-eye relaxed, open-eye tensed, and closed-eye tensed was 0.95, 0.90, 0.95, and 0.77, respectively using the combined control strategy.

Keywords:

• Active muscle
• computational modeling
• human body models
• GHBMC
• muscle activation

Acknowledgments

All simulations were run on the DEAC cluster at Wake Forest University with support from Stevens Cody and Adam Carlson.

Disclosure statement

Scott Gayzik is a member of Elemance, LLC., which distributes academic and commercial licenses for the use of GHBMC-owned computational human body models.

Additional information

Funding

This study was supported by the Global Human Body Models Consortium, LLC and NHTSA under GHBMC Project No.: WFU-006.