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

TL;DR

This paper introduces a novel error-state Model Predictive Control method on connected matrix Lie groups, improving convergence speed and robustness for legged robot control through Lie algebra linearization and symmetry exploitation.

Contribution

It presents a new error-state MPC framework on Lie groups with globally valid linearized dynamics, enhancing control performance over existing geometric variational MPC methods.

Findings

Faster convergence of rotation and position control.

Validated on simulation and quadrupedal robot experiments.

Outperforms baseline control methods.

Abstract

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.