  Within the concept of physical human-robot interaction (pHRI), the most
important criterion is the safety of the human operator interacting with a high
degree of freedom (DoF) robot. Therefore, a robust control scheme is in high
demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In
this paper, an adaptive decentralized control strategy is designed to
accomplish the abovementioned objectives. To do so, a human upper limb model
and an exoskeleton model are decentralized and augmented at the subsystem level
to enable a decentralized control action design. Moreover, human exogenous
force (HEF) that can resist exoskeleton motion is estimated using radial basis
function neural networks (RBFNNs). Estimating both human upper limb and robot
rigid body parameters, along with HEF estimation, makes the controller
adaptable to different operators, ensuring their physical safety. The barrier
Lyapunov function (BLF) is employed to guarantee that the robot can operate in
a safe workspace while ensuring stability by adjusting the control law. Unknown
actuator uncertainty and constraints are also considered in this study to
ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole
system is established by means of the virtual stability concept and virtual
power flows (VPFs) under the proposed robust controller. The experimental
results are presented and compared to proportional-derivative (PD) and
proportional-integral-derivative (PID) controllers. To show the robustness of
the designed controller and its good performance, experiments are performed at
different velocities, with different human users, and in the presence of
unknown disturbances. The proposed controller showed perfect performance in
controlling the robot, whereas PD and PID controllers could not even ensure
stable motion in the wrist joints of the robot.
