论文标题
机器人系统的模型层次结构预测控制
Model Hierarchy Predictive Control of Robotic Systems
论文作者
论文摘要
这封信为高维机器人系统提供了一种新的预测控制体系结构。与传统的模型预测控制（MPC）进行运动的方法相反，该方法是提出优化问题的层次结构序列的，而拟议的工作则提出了在模型层次结构上提出的单个优化问题，因此被命名为模型层次结构预测性控制（MHPC）。 MHPC被配制为多相后退轨迹轨迹优化（TO）问题，可以使用任何通用多相求解器来实现。 MHPC在仿真的仿真中标有基准测试，在四足动物，双子和四个四个方面，在每种情况下都保持较低的计算成本，以表现出在PAR或超过全体MPC上的控制性能。在MIT MINI Cheetah上进行了控制策略，从而实现了脱机，这表明了生成的轨迹的身体有效性，并激发了未来工作的在线MHPC。
This letter presents a new predictive control architecture for high-dimensional robotic systems. As opposed to a conventional Model Predictive Control (MPC) approach to locomotion that formulates a hierarchical sequence of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC). MHPC is formulated as a multi-phase receding-horizon Trajectory Optimization (TO) problem, and can be implemented using any general multi-phase TO solver. MHPC is benchmarked in simulation on a quadruped, a biped, and a quadrotor, demonstrating control performance on par or exceeding whole-body MPC while maintaining a lower computational cost in each case. A preliminary gap jumping experiment is conducted on the MIT Mini Cheetah with the control policy generated offline, demonstrating the physical validity of the generated trajectories and motivating online MHPC in future work.
