论文标题
通过在线切换时间优化，全身模型的预测性控制与刚性接触
Whole-body model predictive control with rigid contacts via online switching time optimization
论文作者
论文摘要
这项研究介绍了具有刚性接触的机器人系统的全身模型预测控制（MPC），使用在线切换时间优化（STO）的给定接触序列下。我们将机器人动态用刚性接触视为开关系统，并制定开关系统的最佳控制问题以实现MPC。我们为MPC问题使用有效的解决方案算法，该算法同时优化了切换时间和轨迹。与现有的现有方法不同，目前的有效算法可以在线优化和切换时间。通过数字模拟四倍的机器人的动态跳跃运动，比较了与传统的MPC相比，与在线STO进行了在线STO的提议的MPC。在模拟比较中，提出的MPC成功控制了动态跳跃运动的两倍，这表明所提出的方法扩展了整体MPC的能力。我们进一步在四倍体机器人单位A1上进行硬件实验，并证明所提出的方法在实际机器人上实现了动态运动。
This study presents a whole-body model predictive control (MPC) of robotic systems with rigid contacts, under a given contact sequence using online switching time optimization (STO). We treat robot dynamics with rigid contacts as a switched system and formulate an optimal control problem of switched systems to implement the MPC. We utilize an efficient solution algorithm for the MPC problem that optimizes the switching times and trajectory simultaneously. The present efficient algorithm, unlike inefficient existing methods, enables online optimization as well as switching times. The proposed MPC with online STO is compared over the conventional MPC with fixed switching times, through numerical simulations of dynamic jumping motions of a quadruped robot. In the simulation comparison, the proposed MPC successfully controls the dynamic jumping motions in twice as many cases as the conventional MPC, which indicates that the proposed method extends the ability of the whole-body MPC. We further conduct hardware experiments on the quadrupedal robot Unitree A1 and prove that the proposed method achieves dynamic motions on the real robot.
