An Error-State Model Predictive Control on Connected Matrix Lie Groups for Legged Robot Control

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 trackin...

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Bibliographic Details
Published in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 8850 - 8857
Main Authors Teng, Sangli, Chen, Dianhao, Clark, William, Ghaffari, Maani
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.10.2022
Subjects
Algebra
Dynamics
Mathematical models
Robot control
Three-dimensional displays
Tracking
Trajectory
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Summary: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.
ISSN:2153-0866
DOI:10.1109/IROS47612.2022.9981282