用于鲁棒外骨骼运动的数据驱动预测控制
Data-Driven Predictive Control for Robust Exoskeleton Locomotion
AI总结：
本文提出多层 DDPC 框架，利用 Hankel 矩阵与状态转移矩阵适应不同使用者及负载，并结合逆运动学和无源性控制，在 Atalante 外骨骼上验证其优于基于 LIP 的 MPC。
摘要：
外骨骼运动必须具有鲁棒性，同时能够适应不同使用者在有负载和无负载情况下的需求。为应对这些挑战，本工作引入了一种数据驱动预测控制（DDPC）框架，用于合成下肢外骨骼的行走步态，并在其数据驱动模型中采用 Hankel 矩阵和状态转移矩阵。所提出的方法通过多层架构利用 DDPC。在顶层，DDPC 充当规划器，采用 Hankel 矩阵和状态转移矩阵生成数据驱动模型，该模型能够学习并适应不同使用者和负载。在底层，我们的方法结合逆运动学和基于无源性的控制，将 DDPC 规划的轨迹映射为下肢外骨骼的全阶状态。我们通过在 Atalante 下肢外骨骼上针对不同负载开展数值仿真和硬件实验，验证了该方法的有效性。此外，我们还与基于降阶线性倒立摆（LIP）模型的模型预测控制（MPC）框架进行了对比分析。通过该比较，本文证明 DDPC 能够在考虑模型不确定性和未知扰动的同时，实现多种速度下的鲁棒双足行走。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
