让机器人走路时能实时调整步伐，抗推倒能力更强。
Discrete time model predictive control for humanoid walking with step adjustment
- 用低维模型预测步位和重心运动，动态调整脚的位置。
- 仿真中对推力干扰有良好抵抗，步行稳定不摔倒。
- 无需预设脚步或质心轨迹，适合复杂地形适应。
本文提出一种用于人形机器人行走的离散时间模型预测控制方法，支持在线步位调整。控制器采用分层结构：高层使用低维线性倒立摆模型（LIPM）确定期望步位和重心运动，以防止跌倒并保持目标速度；底层任务空间控制器（TSC）利用全身动力学跟踪高层输出的运动轨迹。与现有方法不同，该方法不依赖预设步序或参考质心轨迹。整个方案在扭矩控制的人形机器人仿真环境中验证，结果表明所提方法能生成稳定行走，并有效抵抗外部推力干扰。
原文摘要 · Abstract (English)
This paper presents a Discrete-Time Model Predictive Controller (MPC) for humanoid walking with online footstep adjustment. The proposed controller utilizes a hierarchical control approach. The high-level controller uses a low-dimensional Linear Inverted Pendulum Model (LIPM) to determine desired foot placement and Center of Mass (CoM) motion, to prevent falls while maintaining the desired velocity. A Task Space Controller (TSC) then tracks the desired motion obtained from the high-level controller, exploiting the whole-body dynamics of the humanoid. Our approach differs from existing MPC methods for walking pattern generation by not relying on a predefined foot-plan or a reference center of pressure (CoP) trajectory. The overall approach is tested in simulation on a torque-controlled Humanoid Robot. Results show that proposed control approach generates stable walking and prevents fall against push disturbances.
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