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.

翻译：本文提出了一种用于人形机器人行走的离散时间模型预测控制器（MPC），该控制器具备在线脚步调整功能。所提出的控制器采用分层控制方法。高层控制器使用低维线性倒立摆模型（LIPM）来确定期望的脚部放置位置和质心（CoM）运动，以防止跌倒并维持期望速度。随后，任务空间控制器（TSC）利用人形机器人的全身动力学，对高层控制器生成的期望运动进行跟踪。与现有用于行走模式生成的MPC方法不同，我们的方法不依赖于预定义的脚步规划或参考压力中心（CoP）轨迹。该整体方法在扭矩控制的人形机器人上进行了仿真测试。结果表明，所提出的控制方法能够生成稳定的行走步态，并在受到推力干扰时有效防止跌倒。