Achieving Velocity Tracking Despite Model Uncertainty for a Quadruped Robot with a PD-ILC Controller

In this study, we introduce a control strategy that combines Proportional-Derivative (PD) control with Iterative Learning Control (ILC) to enhance legged robot velocity control with only the inverse kinematics and no additional system identification. This approach leverages the realtime feedback capabilities of PD control for gait tracking while incorporating ILC's learning abilities to eliminate inaccuracies from unmodeled dynamics iteratively and to reach desired velocities without residual errors. By uniting these techniques, the proposed method empowers legged robots to adapt and optimize their control behavior, achieving and maintaining desired walking velocities. Experimental results on the physical legged robot Go1 demonstrate the effectiveness of the proposed approach, highlighting its adaptability and reliability in real-world scenarios. This research represents a first step towards overcoming high computational effort and extensive data collection for quadruped robot velocity tracking through onboard learning.

I. Introduction

Quadruped robots have garnered substantial attention in recent years due to their potential to navigate complex and unstructured environments with remarkable agility and adapt-ability [1]. This burgeoning field of robotics holds immense promise for applications ranging from search and rescue missions to planetary exploration and human assistance [2]–[5]. One critical aspect in the development and deployment of quadruped robots is the control of their legged locomotion.