Adaptive MPC-based quadrupedal robot control under periodic disturbances

2025-05-18

AI Summary

This work addresses motion control of quadrupedal robots under periodic external disturbances—such as wind loads and mechanical vibrations. We propose an adaptive model predictive control (MPC) framework. Our key contributions are threefold: (i) the first explicit time-domain modeling and compensation of periodic disturbance forces and torques for quadrupeds; (ii) a lightweight online regressor, integrated with a simplified rigid-body dynamics model, enabling real-time estimation of disturbance amplitude and frequency—without requiring high-dimensional state augmentation or prior frequency-domain assumptions; and (iii) experimental validation demonstrating a 32% reduction in trajectory tracking error compared to static disturbance compensation baselines, along with significantly improved robustness. All code, simulation environments, and computational scripts are publicly released.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Estimating periodic disturbances in quadrupedal robot locomotion
• Improving control performance under periodic force/torque disturbances
• Developing lightweight regressor for disturbance magnitude/frequency extraction

Innovation

• Adaptive MPC for periodic disturbance control
• Lightweight regressor estimates disturbance properties
• Simplified dynamics enhance performance over baseline