Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2024 IEEE 63rd Conference on Decision and Control, CDC 2024 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 4143-4150 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9798350316339 |
| Publikationsstatus | Veröffentlicht - 16 Dez. 2024 |
| Veranstaltung | 63rd IEEE Conference on Decision and Control, CDC 2024 - Milan, Italien Dauer: 16 Dez. 2024 → 19 Dez. 2024 |
Publikationsreihe
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| ISSN (Print) | 0743-1546 |
| ISSN (elektronisch) | 2576-2370 |
Abstract
Optimizing controllers for reference tracking in real-world environments typically requires laborious manual tuning of a control policy to ensure safe operation under constraints. In this work, a Reference-Adapting Iterative Learning Control (RAILC) scheme is proposed that enables autonomous motion optimization for multi-input/multi-output systems with linear, inequality constraints. The proposed method consists of a standard ILC system that iteratively updates an input feedforward trajectory to learn to perform the desired, optimal motion which is encoded as a reference trajectory. To also ensure compliance with the constraints on every single trial, the standard ILC is modularly extended by a reference adaptation scheme. Both feasibility and constraint compliance of the proposed RAILC method are formally proven. Furthermore, it is shown that monotonic convergence of the underlying ILC scheme guarantees stability and monotonic convergence of the proposed RAILC method. The method's capability to solve reference tracking and motion optimization problems for constrained MIMO systems is validated by two simulation examples including a two-link robot that - by means of the proposed method - learns to increase the execution speed of a desired motion by a factor of five.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Modellierung und Simulation
- Mathematik (insg.)
- Steuerung und Optimierung
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- BibTex
- RIS
2024 IEEE 63rd Conference on Decision and Control, CDC 2024. Institute of Electrical and Electronics Engineers Inc., 2024. S. 4143-4150 (Proceedings of the IEEE Conference on Decision and Control).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Reference-Adapting Iterative Learning Control for Motion Optimization in Constrained Environments
AU - Meindl, Michael
AU - Bachhuber, Simon
AU - Seel, Thomas
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/12/16
Y1 - 2024/12/16
N2 - Optimizing controllers for reference tracking in real-world environments typically requires laborious manual tuning of a control policy to ensure safe operation under constraints. In this work, a Reference-Adapting Iterative Learning Control (RAILC) scheme is proposed that enables autonomous motion optimization for multi-input/multi-output systems with linear, inequality constraints. The proposed method consists of a standard ILC system that iteratively updates an input feedforward trajectory to learn to perform the desired, optimal motion which is encoded as a reference trajectory. To also ensure compliance with the constraints on every single trial, the standard ILC is modularly extended by a reference adaptation scheme. Both feasibility and constraint compliance of the proposed RAILC method are formally proven. Furthermore, it is shown that monotonic convergence of the underlying ILC scheme guarantees stability and monotonic convergence of the proposed RAILC method. The method's capability to solve reference tracking and motion optimization problems for constrained MIMO systems is validated by two simulation examples including a two-link robot that - by means of the proposed method - learns to increase the execution speed of a desired motion by a factor of five.
AB - Optimizing controllers for reference tracking in real-world environments typically requires laborious manual tuning of a control policy to ensure safe operation under constraints. In this work, a Reference-Adapting Iterative Learning Control (RAILC) scheme is proposed that enables autonomous motion optimization for multi-input/multi-output systems with linear, inequality constraints. The proposed method consists of a standard ILC system that iteratively updates an input feedforward trajectory to learn to perform the desired, optimal motion which is encoded as a reference trajectory. To also ensure compliance with the constraints on every single trial, the standard ILC is modularly extended by a reference adaptation scheme. Both feasibility and constraint compliance of the proposed RAILC method are formally proven. Furthermore, it is shown that monotonic convergence of the underlying ILC scheme guarantees stability and monotonic convergence of the proposed RAILC method. The method's capability to solve reference tracking and motion optimization problems for constrained MIMO systems is validated by two simulation examples including a two-link robot that - by means of the proposed method - learns to increase the execution speed of a desired motion by a factor of five.
UR - http://www.scopus.com/inward/record.url?scp=86000624992&partnerID=8YFLogxK
U2 - 10.1109/CDC56724.2024.10886730
DO - 10.1109/CDC56724.2024.10886730
M3 - Conference contribution
AN - SCOPUS:86000624992
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4143
EP - 4150
BT - 2024 IEEE 63rd Conference on Decision and Control, CDC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 63rd IEEE Conference on Decision and Control, CDC 2024
Y2 - 16 December 2024 through 19 December 2024
ER -