Adaptive MPC-based quadrupedal robot control under periodic disturbances

TL;DR

This paper presents a method for adaptive model predictive control that estimates and compensates for periodic external disturbances in quadrupedal robots, improving their locomotion performance under challenging conditions.

Contribution

It introduces a lightweight regressor for estimating periodic disturbances using simplified robot dynamics, addressing a gap in existing adaptive control methods.

Findings

Performance improved over static disturbance compensation baseline

Effective estimation of disturbance magnitude and frequency

Experimental validation on quadrupedal robot tasks

Abstract

Recent advancements in adaptive control for reference trajectory tracking enable quadrupedal robots to perform locomotion tasks under challenging conditions. There are methods enabling the estimation of the external disturbances in terms of forces and torques. However, a specific case of disturbances that are periodic was not explicitly tackled in application to quadrupeds. This work is devoted to the estimation of the periodic disturbances with a lightweight regressor using simplified robot dynamics and extracting the disturbance properties in terms of the magnitude and frequency. Experimental evidence suggests performance improvement over the baseline static disturbance compensation. All source files, including simulation setups, code, and calculation scripts, are available on GitHub at https://github.com/aidagroup/quad-periodic-mpc.