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Get Free AccessIn this paper, the decentralized optimal control problem is addressed for a class of large-scale systems subject to injection attacks. All subsystem matrices are considered to be unavailable to the designer. A model-free decentralized sliding mode control (SMC) scheme for each subsystem is designed via just utilizing its own state information and the known bounds of the interconnections and the injection attacks. Moreover, the adaptive dynamic programming (ADP) approach is incorporated to deal with the infinite horizon optimal control problem for the sliding mode dynamics, which is equivalent to the solution of a set of parallel algebraic Riccati equations. Furthermore, a novel parallel policy iteration algorithm is developed to implement the proposed decentralized SMC scheme without using all subsystems dynamics matrices. Specifically, it is shown that during the whole policy iteration steps, the reachability of each sliding variable and the stability of each sliding mode dynamics are guaranteed simultaneously by the online updating decentralized SMC scheme. Finally, the applicability of the proposed novel ADP-based decentralized SMC strategy is illustrated by a two-machine power system subject to three different injection attacks.
Jun Song, Longyang Huang, Hamid Reza Karimi, Yugang Niu, Jiale Zhou (2020). ADP-Based Security Decentralized Sliding Mode Control for Partially Unknown Large-Scale Systems Under Injection Attacks. IEEE Transactions on Circuits and Systems I Regular Papers, 67(12), pp. 5290-5301, DOI: 10.1109/tcsi.2020.3014253.
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Type
Article
Year
2020
Authors
5
Datasets
0
Total Files
0
Language
English
Journal
IEEE Transactions on Circuits and Systems I Regular Papers
DOI
10.1109/tcsi.2020.3014253
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