论文标题
机器人两足步行的强大干扰拒绝：系统级合成具有逐步动力学近似
Robust Disturbance Rejection for Robotic Bipedal Walking: System-Level-Synthesis with Step-to-step Dynamics Approximation
论文作者
论文摘要
我们提出了一个垫脚的稳定控制，该控制措施解决了双皮亚步行机器人上的外部推动障碍。基于机器人的渐进式（S2S）动力学合成阶梯控制，该动力学的质量（COM）高度约为恒定。我们首先学习了一个线性S2S动力学，并从机器人的不受干扰的步行行为中具有有界模型的差异，其中步行步长被视为S2S动力学的控制输入。然后将外部推动视为对学习的S2S（L-S2S）动力学的干扰。然后，我们将系统级合成（SLS）方法应用于受干扰的L-S2S动力学上，以稳健地将机器人稳定到所需的行走上，同时满足机器人的运动学约束。我们成功地实现了针对双足机器人琥珀和Cassie行走的拟议方法，以推动干扰，这表明该方法是通用，有效且计算上有效的稳健干扰排斥反应的。
We present a stepping stabilization control that addresses external push disturbances on bipedal walking robots. The stepping control is synthesized based on the step-to-step (S2S) dynamics of the robot that is controlled to have an approximately constant center of mass (COM) height. We first learn a linear S2S dynamics with bounded model discrepancy from the undisturbed walking behaviors of the robot, where the walking step size is taken as the control input to the S2S dynamics. External pushes are then considered as disturbances to the learned S2S (L-S2S) dynamics. We then apply the system-level-synthesis (SLS) approach on the disturbed L-S2S dynamics to robustly stabilize the robot to the desired walking while satisfying the kinematic constraints of the robot. We successfully realize the proposed approach on the walking of the bipedal robot AMBER and Cassie subject to push disturbances, showing that the approach is general, effective, and computationally-efficient for robust disturbance rejection.
