Fuzzy Memory Controller Design Based-Machine Learning Algorithm and Stability Analysis for Nonlinear NCSs Under Asynchronous Cyber Attacks

doi:10.1109/TSMC.2023.3321611

Fuzzy Memory Controller Design Based-Machine Learning Algorithm and Stability Analysis for Nonlinear NCSs Under Asynchronous Cyber Attacks

2024-02-01

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DOI：10.1109/TSMC.2023.3321611

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This article addresses the issue of mismatched communication delay (CD) in dual-channel nonlinear networked control systems (NCSs) resulting from the quality of service (QoS) mechanism's queue management. The focus is on the importance of ensuring communication security in NCSs, particularly in the presence of asynchronous cyber attacks (ACAs). First, improved Lyapunov-Krasovskii functions (LKFs) are constructed, taking into account sampling signals, CDs, and nonlinearities in the system. Additionally, a novel looped-functional approach is introduced to reduce the initial constraint of the criterion. Then, the control algorithm's performance is optimized by achieving a tighter upper bound on the integral term and applying quadratic scaling. To ensure stability and security under ACAs, a fuzzy memory sample-data controller (MSAC) is proposed. This controller leverages a machine learning algorithm to address the optimization problem of data sampling period selection, thereby minimizing resource usage and operating efficiently within the system's limited bandwidth. Finally, numerical simulations are conducted using the dynamic equations of the inverted pendulum system (IPS) to validate the practicality of the proposed theoretical approach.

Keywords:

Cyber attacks

Lyapunov-Krasovskii function (LKF)

machine learning

networked control system (NCS)

stability analysis

Fuzzy Memory Controller Design Based-Machine Learning Algorithm and Stability Analysis for Nonlinear NCSs Under Asynchronous Cyber Attacks

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