Backstepping Control of Open-Chain Linkages Actuated by Antagonistic Hill Muscles

Abstract

For human–machine interaction, the forward progression of technology, particularly controls, regularly brings about new possibilities. Indeed, healthcare applications have flourished in recent years, including robotic rehabilitation, exercise, and prosthetic devices. Testing these devices with human subjects is inherently risky and frequently inconsistent. This work offers a novel simulation framework toward overcoming many of these difficulties. Specifically, generating a closed-loop dynamic model of a human or a human subsystem that can connect to device simulations allows simulated human–machine interaction. In this work, a muscle-actuated open kinematic chain linkage is generated to simulate the human, and a backstepping controller based on inverse dynamics is derived. The control architecture directly addresses muscle redundancy, and two options to resolve this redundancy are evaluated. The specific case of a muscle-actuated arm linkage is developed to illustrate the framework. Trajectory tracking is achieved in simulation. The muscles recruited to meet the tracking goal are in agreement with the method used to solve the redundancy problem. In the future coupling such simulations to any relevant simulation of a machine will provide safe, insightful preprototype test results.