Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 67, Issue 2, Pages 754-766Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3056582
Keywords
Asymptotic stability; convex functions; data models; iterative learning control; iterative methods; multiagent systems; networked control systems; nonlinear dynamical systems; optimization; sampling methods
Funding
- German-Israeli Foundation for Scientific Research and Development
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In this article, we develop a data-based controller design framework for diffusively coupled systems, ensuring convergence to an F-neighborhood of the desired formation. The controller consists of a fixed controller with adjustable gain on each edge. By utilizing passivity theory and network optimization, we not only prove the existence of a gain that achieves the desired formation control goal, but also present a data-based method to determine an upper bound for this gain.
In this article, we develop a data-based controller design framework for diffusively coupled systems with guaranteed convergence to an F-neighborhood of the desired formation. The controller is composed of a fixed controller with an adjustable gain on each edge. Via passivity theory and network optimization, we not only prove that there exists a gain attaining the desired formation control goal, but we present a data-based method to find an upper bound on this gain. Furthermore, by allowing for additional experiments, the conservatism of the upper bound can be reduced via iterative sampling schemes. The introduced scheme is based on the assumption of passive systems, which we relax by discussing different methods for estimating the systems' passivity shortage, as well as applying transformations passivizing them. Finally, we illustrate the developed model-free cooperative control scheme with a case study.
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