用分阶段精度控制实现足式机器人实时行走，兼顾计算效率与运动稳定性。
Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking
- 近段用高精度全身模型，远段用简化质心模型，降低计算负担
- 在18自由度的HyPer-2机器人上实现稳定行走，无需预设落脚点
- 基于acados框架的SQP求解器，全程无需手动设计控制器结构
本文提出一种多阶段全身体型模型预测控制（MPC）方法，用于足式机器人行走控制。在近段预测中采用详细全身体型模型，在远段预测中使用简化的单刚体模型，从而在保持预测能力的同时显著降低计算复杂度。所形成的非线性最优控制问题通过通用开源非线性MPC框架acados中的序列二次规划（SQP）方法求解。给定接触计划和目标行走速度后，控制器可自主优化关节力矩，无需预先设定足步位置。该方法在MuJoCo仿真环境中对18自由度的足式机器人HyPer-2进行了验证。
原文摘要 · Abstract (English)
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.
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