4.7 Article

Cooperative output regulation for a network of parabolic systems with varying parameters

Journal

AUTOMATICA
Volume 125, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2020.109446

Keywords

Parabolic systems; Multi-agent systems; Cooperative output regulation; Backstepping; Boundary control

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This paper addresses the cooperative output regulation problem for a network of agents with different dynamics described by parabolic PDEs. A networked controller is designed to achieve output synchronization for identical finite-dimensional reference models, and local output feedback regulators are designed for the parabolic agents to solve the cooperative output regulation problem. The results show that exponential convergence is ensured for the resulting multi-agent system.
This paper is concerned with the cooperative output regulation problem for a network of agents with different dynamics described by parabolic PDEs subject to spatially- and time-varying parameters. Firstly, a networked controller is designed achieving output synchronization for identical finite-dimensional reference models, which deliver the state of the global reference model required for the synchronization to the parabolic agents. The latter can be subject to local disturbances acting in-domain, on all boundaries and on the anti-collocated output to be controlled. The cooperative output regulation problem is solved by designing local output feedback regulators for the parabolic agents. This requires the solution of time-varying regulator equations and the design of disturbance observers for parabolic systems with spatially- and time-varying coefficients. For this, a systematic backstepping approach is provided and it is shown that cooperative output regulation with exponential convergence is ensured for the resulting multi-agent system. The results of the paper are applied to the cooperative output regulation of a heterogeneous network of four parabolic agents in the presence of local disturbances. (C) 2020 Elsevier Ltd. All rights reserved.

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