データ駆動型予測制御による頑健な外骨格歩行
Data-Driven Predictive Control for Robust Exoskeleton Locomotion
ヘンケル行列と状態遷移行列を用いたデータ駆動型予測制御（DDPC）を提案し、下半身外骨格の歩行軌道を生成。ユーザーや積載量の変化に適応し、シミュレーションと実機実験でMPCより頑健な歩行を実証した。
著者: Kejun Li, Jeeseop Kim, Xiaobin Xiong, Kaveh Akbari Hamed, Yisong Yue, Aaron D. Ames
分類: cs.RO
原文アブストラクト
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.
