Observer-based adaptive neural asynchronous <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si635.svg" display="inline" id="d1e579"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> Control for fuzzy Markov jump systems under FDI attacks-Reference-Cited by-同舟云学术

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Observer-based adaptive neural asynchronous H∞ Control for fuzzy Markov jump systems under FDI attacks

Published:2024-11 Issue:16 Volume:361 Page:107147
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Cao Xueyu^ORCID,Liu Shan,Cen Jian

Funder

Guangdong Provincial Natural Science Foundation

Basic and Applied Basic Research Foundation of Guangdong Province

Guangzhou Science and Technology Program Key Projects

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference44 articles.

1. A survey for deep reinforcement learning in Markovian cyber-physical systems: Common problems and solutions;Rupprecht;Neural Netw.,2022

2. G. Sheng, G. Gao, Research on the Attitude Control of Civil Quad-Rotor UAV Based on Fuzzy PID Control, in: 2019 Chinese Control and Decision Conference, CCDC, 2019, pp. 4566–4569, http://dx.doi.org/10.1109/CCDC.2019.8832855.

3. Hybrid event-triggered tracking control for unmanned autonomous helicopter under disturbance and deception attacks;Liao;ISA Trans.,2023

4. A switching event-triggered resilient control scheme for primary and secondary levels in AC microgrids;Shan;ISA Trans.,2022

5. Self-triggered finite-time control for discrete-time Markov jump systems;Wan;Inform. Sci.,2023

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