Improved dynamic output feedback control for T-S fuzzy systems against hybrid cyber-attacks via neural network method

Huiyan Zhang, Zixian Chen, Hao Sun and Rathinasamy Sakthivel

Chaos, Solitons & Fractals, 2025, vol. 195, issue C

Abstract: This paper proposes a novel neural network-based dynamic output feedback controller (NN-DOFC) for nonlinear systems subject to hybrid cyber-attacks. The nonlinear terms are modeled using Takagi–Sugeno fuzzy inference rules, and the NN-DOFC is introduced to ensure that the closed-loop system achieves asymptotic stability while satisfying the (X,Y,Z)-dissipative property. The conventional DOFC’s gains are integrated as partial weights within the NN-DOFC framework, indicating that the traditional approach can be considered a specific instance of the proposed NN-DOFC. The three-layer fully connected feedforward neural network architecture extends the capabilities of the traditional DOFC. Additionally, a Bernoulli process is employed to model the hybrid cyber-attacks, including denial-of-service (DoS) and deception attacks. Then, the values of the controller are partially obtained by solving linear matrix inequalities and partially optimized using a genetic algorithm. Finally, by comparing the stabilization effects of the traditional DOFC with the proposed NN-DOFC, the effectiveness of the latter is demonstrated.

Keywords: Neural network-based dynamic output feedback controller; Hybrid cyber-attacks; Dissipative property (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:195:y:2025:i:c:s0960077925002486

DOI: 10.1016/j.chaos.2025.116235

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