  This paper proposes an adaptive gravity compensation (AGC) control strategy
for a cable-driven upper-limb exosuit intended to assist the wearer with
lifting tasks. Unlike most model-based control techniques used for this
human-robot interaction task, the proposed control design does not assume
knowledge of the anthropometric parameters of the wearer's arm and the payload.
Instead, the uncertainties in human arm parameters, such as mass, length, and
payload, are estimated online using an indirect adaptive control law that
compensates for the gravity moment about the elbow joint. Additionally, the AGC
controller is agnostic to the desired joint trajectory followed by the human
arm. For the purpose of controller design, the human arm is modeled using a
1-DOF manipulator model. Further, a cable-driven actuator model is proposed
that maps the assistive elbow torque to the actuator torque. The performance of
the proposed method is verified through a co-simulation, wherein the control
input realized in MATLAB is applied to the human bio-mechanical model in
OpenSim under varying payload conditions. Significant reductions in human
effort in terms of human muscle torque and metabolic cost are observed with the
proposed control strategy. Further, simulation results show that the
performance of the AGC controller converges to that of the gravity compensation
(GC) controller, demonstrating the efficacy of AGC-based online parameter
learning.
