Representation-Free Model Predictive Control for Dynamic Motions in Quadrupeds

This article presents a novel representation-free model predictive control (RF-MPC) framework for controlling various dynamic motions of a quadrupedal robot in three-dimensional (3-D) space. Our formulation directly represents the rotational dynamics using the rotation matrix, which liberates us from the issues associated with the use of Euler angles and quaternion as the orientation representations. With a variation-based linearization scheme and a carefully constructed cost function, the MPC control law is transcribed to the standard quadratic program form. The MPC controller can operate at real-time rates of 250 Hz on a quadruped robot. Experimental results including periodic quadrupedal gaits and a controlled backflip validate that our control strategy could stabilize dynamic motions that involve singularity in 3-D maneuvers.

Automated summary

This article introduces a novel representation-free model predictive control framework for controlling dynamic motions of a quadrupedal robot in three-dimensional space. The controller can operate at real-time rates of 250 Hz and stabilize dynamic motions that involve singularity in 3-D maneuvers.