Work

Continuous-Time Adaptive Control with Dynamic Adaptation Gain

Year: 2024

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.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc56724.2024.10886025

Authors: Kaiwen Chen, Kangkang Zhang, Ioan Doré Landau, Alessandro Astolfi

Institutions: Imperial College London, Centre National de la Recherche Scientifique, GIPSA-Lab

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Engineering and Physical Sciences Research Council

Award: G5185555754