Continuous-time adaptive control with dynamic adaptation gain
File(s)
Accepted version
Author(s)
Chen, Kaiwen
Zhang, Kangkang
Landau, Ioan Doré
Astolfi, Alessandro
Type
Conference Paper
Abstract
This paper investigates a class of continuous-time parameter identifiers with the so-called “dynamic adaptation gain (DAG)”, inspired by its discrete-time counterpart [1]. A modular control law is first presented: this helps re-parametrize the system and highlight the required properties for the identifier. Then, a dynamic adaptation gain is constructed using a strictly passive system, strengthened by a feedthrough path: the resulting DAG is input strictly passive. Analysis shows that the identifier with the proposed DAG satisfies specific signal properties. An integrated analysis of both the control law and the identifier establishes boundedness of all closedloop signals and state convergence. The convergence of the parameter estimate is also established under a persistence of excitation condition. Finally, an example inspired by a pathfollowing problem demonstrates the convergence improvement achieved by the proposed DAG.
Date Issued
2025-02-26
Date Acceptance
2024-12-01
Citation
2024 IEEE 63rd Conference on Decision and Control (CDC), 2025, pp.2815-2820
ISSN
0743-1546
Publisher
IEEE
Start Page
2815
End Page
2820
Journal / Book Title
2024 IEEE 63rd Conference on Decision and Control (CDC)
Copyright Statement
Copyright © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
2024 IEEE 63rd Conference on Decision and Control (CDC)
Publication Status
Published
Start Date
2024-12-16
Finish Date
2024-12-19
Coverage Spatial
Milan, Italy