Predictive Control of Achilles Tendon Force During Cyclic Motions in a Simulated Musculoskeletal System With Parallel Actuation

Abstract

Recent advancements in wearable exoskeletons for human lower extremities have primarily focused on augmenting walking capacity by either reducing metabolic costs or providing joint torque support based on measured electromyography or predicted joint torques. However, less attention has been given to the use of robotic exoskeletons for controlling the mechanics of specific biological tissues, such as elastic tendons. Achieving closed-loop control over in-vivo musculotendon mechanics during movement could revolutionize injury prevention and personalized rehabilitation. Here, we introduce a framework utilizing musculoskeletal modeling and nonlinear model predictive control (NMPC) to close the loop around tendon force in a simulation of cyclic force production of the human ankle plantarflexors in parallel with a powered exoskeleton. The proposed framework integrates a computationally efficient model comprising explicit closed-form ordinary differential equations governing musculotendon and ankle joint with parallel actuation dynamics. The model’s computational time, in the microsecond range, allows prediction of future states in real-time closed-loop control. Compared to a predictive proportional-derivative controller, the NMPC-based framework more effectively maintained Achilles tendon force within a predetermined threshold across varying levels of muscle excitation amplitude and frequency. Remarkably, the NMPC framework demonstrates robustness to muscle excitation variations during cyclic motions, making it suitable for real-world applications.