Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2024 European Control Conference, ECC 2024 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 134-140 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9783907144107 |
| ISBN (Print) | 979-8-3315-4092-0 |
| Publikationsstatus | Veröffentlicht - 25 Juni 2024 |
| Veranstaltung | 2024 European Control Conference, ECC 2024 - Stockholm, Schweden Dauer: 25 Juni 2024 → 28 Juni 2024 |
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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Mathematik (insg.)
- Modellierung und Simulation
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2024 European Control Conference, ECC 2024. Institute of Electrical and Electronics Engineers Inc., 2024. S. 134-140.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Achieving Velocity Tracking Despite Model Uncertainty for a Quadruped Robot with a PD-ILC Controller
AU - Weiss, Manuel
AU - Stirling, Andrew
AU - Pawluchin, Alexander
AU - Lehmann, Dustin
AU - Hannemann, Yannis
AU - Seel, Thomas
AU - Boblan, Ivo
N1 - Publisher Copyright: © 2024 EUCA.
PY - 2024/6/25
Y1 - 2024/6/25
N2 - 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.
AB - 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.
KW - Iterative Learning Control
KW - Nonlinear Systems
KW - Real-time Control
KW - Robotics
UR - http://www.scopus.com/inward/record.url?scp=85200571473&partnerID=8YFLogxK
U2 - 10.23919/ECC64448.2024.10590932
DO - 10.23919/ECC64448.2024.10590932
M3 - Conference contribution
AN - SCOPUS:85200571473
SN - 979-8-3315-4092-0
SP - 134
EP - 140
BT - 2024 European Control Conference, ECC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 European Control Conference, ECC 2024
Y2 - 25 June 2024 through 28 June 2024
ER -