论文标题
对腿机器人控制的连接矩阵谎言组的错误状态模型预测控制
An Error-State Model Predictive Control on Connected Matrix Lie Groups for Legged Robot Control
论文作者
论文摘要
本文报告了用于机器人控制的连接矩阵谎言组的新的错误状态模型预测控制（MPC）方法。线性化跟踪误差动力学和线性运动方程在LIE代数中得出。此外，在初始条件下，线性化跟踪误差动力学和运动方程在全球有效，并且独立于系统轨迹而发展。通过利用问题的对称性，提出的方法比基于基于变异的线性化的最先进的几何变异MPC同时显示旋转和位置的收敛速度更快。在跟踪完整的3D刚体动力学的跟踪控制时，数值模拟证实了与基准相比，所提出的方法的好处。此外，在四足动物MIT MINI Cheetah上进行的姿势控制和运动实验中还验证了所提出的MPC。
This paper reports on a new error-state Model Predictive Control (MPC) approach to connected matrix Lie groups for robot control. The linearized tracking error dynamics and the linearized equations of motion are derived in the Lie algebra. Moreover, given an initial condition, the linearized tracking error dynamics and equations of motion are globally valid and evolve independently of the system trajectory. By exploiting the symmetry of the problem, the proposed approach shows faster convergence of rotation and position simultaneously than the state-of-the-art geometric variational MPC based on variational-based linearization. Numerical simulation on tracking control of a fully-actuated 3D rigid body dynamics confirms the benefits of the proposed approach compared to the baselines. Furthermore, the proposed MPC is also verified in pose control and locomotion experiments on a quadrupedal robot MIT Mini Cheetah.
