arXiv 2508.102692025-08-14cs.RO

用于鲁棒和反应性外骨骼步态合成的混合数据驱动预测控制

Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis

Department of Computation and Neural Systems, Caltech（计算与神经系统系，加州理工学院）；Department of Mechanical and Civil Engineering, Caltech（机械与土木工程系，加州理工学院）；Wandercraft；Department of Computing and Mathematical Sciences, Caltech（计算与数学科学系，加州理工学院）；Department of Control and Dynamical Systems, Caltech（控制与动态系统系，加州理工学院）

Kejun Li, Jeeseop Kim, Maxime Brunet, Marine Pétriaux, Yisong Yue, Aaron D. Ames

备注：: 8 pages; 8 figures

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AI总结：

本文提出混合数据驱动预测控制（HDDPC）框架，通过结合基于Hankel矩阵的系统建模、步间转换以及接触调度与轨迹规划的统一，实现了外骨骼鲁棒且反应性的双足行走。

摘要：

外骨骼的鲁棒双足行走需要具备实时动态响应环境变化的能力。本文提出了混合数据驱动预测控制（HDDPC）框架，作为数据驱动预测控制的扩展，通过同时规划足部接触调度和连续域轨迹来解决这些挑战。该框架利用基于Hankel矩阵的表示来建模系统动力学，并结合步间（S2S）转换以增强在动态环境中的适应性。通过将接触调度与轨迹规划相结合，该框架为步态运动合成提供了一种高效、统一的解决方案，通过在线重规划实现鲁棒和反应性行走。我们在Atalante外骨骼上验证了该方法，展示了其鲁棒性和适应性的提升。

英文摘要：

Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predictive control (HDDPC) framework, an extension of the data-enabled predictive control, that addresses these challenges by simultaneously planning foot contact schedules and continuous domain trajectories. The proposed framework utilizes a Hankel matrix-based representation to model system dynamics, incorporating step-to-step (S2S) transitions to enhance adaptability in dynamic environments. By integrating contact scheduling with trajectory planning, the framework offers an efficient, unified solution for locomotion motion synthesis that enables robust and reactive walking through online replanning. We validate the approach on the Atalante exoskeleton, demonstrating improved robustness and adaptability.

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