Data-driven MPC of descriptor systems: A case study for power networks. Issue 30 (2022)

• Record Type: Journal Article
• Authors: Schmitz, Philipp; Engelmann, Alexander; Faulwasser, Timm; Worthmann, Karl
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 30(2022)
• Page Start: 359
• Page End: 364
• Publication Date: 2022
• Subjects: Data-driven control -- descriptor systems -- MPC -- Willems' fundamental lemma -- optimal control -- power-swing equations -- power systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.11.079
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Recently, data-driven predictive control of linear systems has received wide-spread research attention. It hinges on the fundamental lemma by Willems et al. In a previous paper, we have shown how this framework can be applied to predictive control of linear time-invariant descriptor systems. In the present paper, we present a case study wherein we apply data-driven predictive control to a discrete-time descriptor model obtained by discretization of the power-swing equations for a nine-bus system. Our results show the efficacy of the proposed control scheme and they underpin the prospect of the data-driven framework for control of descriptor systems.