Abstract

Many recent results on distributed control of multi-agent networks rely on a number of simplifying assumptions that facilitate the solution of regulation problems associated with large-scale networked systems. Identical subsystem models are a typical assumption made in networked control systems which often fail in practice. In this paper, we propose a systematic method for removing this assumption, leading to a general approach to distributed-control design for stabilising networks of multiple non-identical dynamic agents. Local subsystems represented as autonomous dynamic agents are assumed to share a set of structural properties, (controllability) indices. Our approach relies on the solution of certain model-matching type problems using local state-feedback and state/input-matrix transformations that map local dynamics to a target system, selected to minimise joint control effort. By adapting well-established distributed LQR control design methodologies to our framework, the stabilisation problem of networks of non-identical dynamic agents is solved. The applicability of our approach is illustrated via a synchronisation example of eleven harmonic oscillators with non-identical dynamics communicating over a connected graph.

Publication Type:	Article
Additional Information:	© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Publisher Keywords:	Heterogeneous dynamics, model-matching, controllability indices, distributed control, distributed LQR
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments:	School of Science & Technology > Engineering

Preview

Text - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview

Export

Downloads