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.

翻译：外骨骼行走必须具有稳健性，同时能够适应不同用户（无论是否携带负载）的需求。为应对这些挑战，本研究提出了一种数据驱动的预测控制（DDPC）框架，用于合成下肢外骨骼的步态。该框架采用汉克尔矩阵和状态转移矩阵构建其数据驱动模型。所提出的方法通过多层架构实现DDPC：在顶层，DDPC作为规划器，利用汉克尔矩阵和状态转移矩阵生成能够学习并适应不同用户及负载变化的数据驱动模型；在底层，该方法结合逆运动学与基于无源性的控制，将DDPC规划的轨迹映射至下肢外骨骼的全阶状态。通过在Atalante下肢外骨骼上搭载不同负载进行的数值仿真与硬件实验，我们验证了该方法的有效性。此外，本研究还将其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。通过比较表明，DDPC能够在考虑模型不确定性与未知扰动的情况下，实现多种速度下的稳健双足行走。