Adaptive Model Predictive Control with Data-driven Error Model for Quadrupedal Locomotion

TL;DR

This paper introduces a data-driven error correction method integrated into Model Predictive Control to improve quadruped robot locomotion accuracy, especially under model uncertainties and external disturbances.

Contribution

It proposes combining an ARMAV-based data-driven error model with MPC, enhancing control accuracy for quadruped robots in real-world conditions.

Findings

Improved locomotion performance demonstrated in simulations.

Successful real-world experiments with payload carrying.

Enhanced control accuracy despite model uncertainties.

Abstract

Model Predictive Control (MPC) relies heavily on the robot model for its control law. However, a gap always exists between the reduced-order control model with uncertainties and the real robot, which degrades its performance. To address this issue, we propose the controller of integrating a data-driven error model into traditional MPC for quadruped robots. Our approach leverages real-world data from sensors to compensate for defects in the control model. Specifically, we employ the Autoregressive Moving Average Vector (ARMAV) model to construct the state error model of the quadruped robot using data. The predicted state errors are then used to adjust the predicted future robot states generated by MPC. By such an approach, our proposed controller can provide more accurate inputs to the system, enabling it to achieve desired states even in the presence of model parameter inaccuracies or disturbances. The proposed controller exhibits the capability to partially eliminate the disparity between the model and the real-world robot, thereby enhancing the locomotion performance of quadruped robots. We validate our proposed method through simulations and real-world experimental trials on a large-size quadruped robot that involves carrying a 20 kg un-modeled payload (84% of body weight).