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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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
16.03.2022
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Subjects | |
Online Access | Get full text |
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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. |
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DOI: | 10.48550/arxiv.2203.08728 |