  Exoskeleton locomotion must be robust while being adaptive to different users
with and without payloads. To address these challenges, this work introduces a
data-driven predictive control (DDPC) framework to synthesize walking gaits for
lower-body exoskeletons, employing Hankel matrices and a state transition
matrix for its data-driven model. The proposed approach leverages DDPC through
a multi-layer architecture. At the top layer, DDPC serves as a planner
employing Hankel matrices and a state transition matrix to generate a
data-driven model that can learn and adapt to varying users and payloads. At
the lower layer, our method incorporates inverse kinematics and passivity-based
control to map the planned trajectory from DDPC into the full-order states of
the lower-body exoskeleton. We validate the effectiveness of this approach
through numerical simulations and hardware experiments conducted on the
Atalante lower-body exoskeleton with different payloads. Moreover, we conducted
a comparative analysis against the model predictive control (MPC) framework
based on the reduced-order linear inverted pendulum (LIP) model. Through this
comparison, the paper demonstrates that DDPC enables robust bipedal walking at
various velocities while accounting for model uncertainties and unknown
perturbations.
