This paper presents a multi-phase whole-body model predictive control (MPC) approach for bipedal walking, combining a detailed whole-body model in the near horizon with a simplified single-rigid-body model in the later prediction steps. This reduces computational complexity while retaining prediction capabilities. The resulting nonlinear optimal control problem is solved entirely within the general-purpose, off-the-shelf nonlinear MPC framework acados, using sequential quadratic programming (SQP). Given a contact schedule and a target walking speed, the controller optimizes joint torques without depending on preselected footstep locations. The controller is validated in MuJoCo simulation on the 18-DoF bipedal robot HyPer-2.

翻译：本文提出了一种用于双足步行的多阶段全身模型预测控制（MPC）方法，该方法在近时间域结合了详细的全身模型，而在后续预测步骤中采用简化的单刚体模型。这降低了计算复杂度，同时保留了预测能力。所得到的非线性最优控制问题完全在通用现成的非线性MPC框架acados中求解，采用序列二次规划（SQP）。在给定接触时序和目标步行速度的条件下，该控制器能够优化关节力矩，而无需依赖预选脚步位置。该控制器在18自由度双足机器人HyPer-2上的MuJoCo仿真中得到了验证。