This paper presents an extension of previous model predictive control (MPC) schemes for dynamic walking to the stabilization of the time-varying divergent component-of-motion (DCM). In order to address the control authority limitations caused by fixed footholds, the step positions and rotations are treated as control inputs, allowing the generation and execution of stable walking motions, both at high speeds and in the face of disturbances. The use of the time-varying DCM allows consideration of height changes on the DCM dynamics, improving the robustness of the controller over varying terrain. Footstep rotation is included to allow for better modeling of the adjustment effects on reachability for stability and navigation of complex environments. This is done by formulating a quadratically constrained mixed-integer quadratic program (MIQCQP), which, when combined with the use of the time-varying DCM to account for the effects of height changes and use of angular momentum, improves the capabilities of MPC strategies for bipedal walking. While the MIQCQP cannot be solved at the desired control frequency, a method for compensating for the DCM dynamics between solves is presented. Simulation results of fast walking over flat ground and navigating varying-height terrain is presented with the ESCHER humanoid. This is combined with experiments that recover from a variety pushes, which demonstrate the effectiveness of this approach.

中文翻译：

---

使用具有脚步位置和偏航适应的运动发散分量的稳定行走模型预测控制

本文介绍了以前用于动态行走的模型预测控制 (MPC) 方案的扩展，以稳定时变发散运动分量 (DCM)。为了解决由固定立足点引起的控制权限限制，步位置和旋转被视为控制输入，允许在高速和面对干扰时生成和执行稳定的步行运动。时变 DCM 的使用允许考虑 DCM 动态的高度变化，从而提高控制器在不同地形上的鲁棒性。包括足迹旋转，以便更好地模拟复杂环境的稳定性和导航对可达性的调整效果。这是通过制定二次约束混合整数二次规划 (MIQCQP) 来完成的，当结合使用时变 DCM 来解释高度变化的影响和使用角动量时，提高了 MPC 策略的双足步行能力。虽然 MIQCQP 无法在所需的控制频率下求解，但提出了一种补偿求解之间的 DCM 动力学的方法。使用 ESCHER 人形机器人在平坦地面上快速行走和在不同高度的地形上导航的模拟结果。这与从各种推动中恢复的实验相结合，证明了这种方法的有效性。提出了一种补偿求解之间的 DCM 动力学的方法。使用 ESCHER 人形机器人在平坦地面上快速行走和在不同高度的地形上导航的模拟结果。这与从各种推动中恢复的实验相结合，证明了这种方法的有效性。提出了一种补偿求解之间的 DCM 动力学的方法。使用 ESCHER 人形机器人在平坦地面上快速行走和在不同高度的地形上导航的模拟结果。这与从各种推动中恢复的实验相结合，证明了这种方法的有效性。