Reference-Adapting Iterative Learning Control for Motion Optimization in Constrained Environments
Meindl M, Bachhuber S, Seel T (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 4143-4150
Conference Proceedings Title: Proceedings of the IEEE Conference on Decision and Control
Event location: Milan
ISBN: 9798350316339
DOI: 10.1109/CDC56724.2024.10886730
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.
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Germany (DE)How to cite
APA:
Meindl, M., Bachhuber, S., & Seel, T. (2024). Reference-Adapting Iterative Learning Control for Motion Optimization in Constrained Environments. In Proceedings of the IEEE Conference on Decision and Control (pp. 4143-4150). Milan, IT: Institute of Electrical and Electronics Engineers Inc..
MLA:
Meindl, Michael, Simon Bachhuber, and Thomas Seel. "Reference-Adapting Iterative Learning Control for Motion Optimization in Constrained Environments." Proceedings of the 63rd IEEE Conference on Decision and Control, CDC 2024, Milan Institute of Electrical and Electronics Engineers Inc., 2024. 4143-4150.
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