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

verfasst von

Manuel Weiss, Andrew Stirling, Alexander Pawluchin, Dustin Lehmann, Yannis Hannemann, Thomas Seel, Ivo Boblan

Abstract

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.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Externe Organisation(en)

Berliner Hochschule für Technik (BHT)
McGill University
Technische Universität Berlin
Berlin International University of Applied Sciences

Typ

Aufsatz in Konferenzband

Seiten

134-140

Anzahl der Seiten

7

Publikationsdatum

25.06.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerung und Optimierung, Modellierung und Simulation

Elektronische Version(en)

https://doi.org/10.23919/ECC64448.2024.10590932 (Zugang: Geschlossen)