Data-Driven Predictive Control for Robust Exoskeleton Locomotion
<|reference_start|>Data-Driven Predictive Control for Robust Exoskeleton Locomotion: 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.<|reference_end|>
@article{li2024data-driven,
    title={Data-Driven Predictive Control for Robust Exoskeleton Locomotion},
    author={Kejun Li, Jeeseop Kim, Xiaobin Xiong, Kaveh Akbari Hamed, Yisong Yue,
  Aaron D. Ames},
    journal={arXiv preprint arXiv:2403.15658},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15658},
    primaryClass={cs.RO}
}