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：外骨骼运动需要在适应不同用户及有无负载工况的同时保持鲁棒性。针对这些挑战，本文提出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，其数据驱动模型采用Hankel矩阵与状态转移矩阵。该方法通过多层架构实现DDPC：顶层采用基于Hankel矩阵与状态转移矩阵的规划器，生成可学习并适应不同用户与负载的数据驱动模型；底层结合逆运动学与基于无源性的控制方法，将DDPC规划的轨迹映射为下肢外骨骼的全阶状态。通过在不同负载工况下对Atalante下肢外骨骼进行数值仿真与硬件实验，验证了该方法的有效性。此外，我们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。结果表明，DDPC能够在考虑模型不确定性与未知扰动的情况下，实现不同速度下的鲁棒双足行走。