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This article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources. In this context, a new memory stochastic state feedback controller is proposed to stabilize the closed‐loop networked control system. A new randomly occurring deception attacks model is employed to deal with the security problem of NCSs. Sufficient stability conditions are derived based on a suitable Lyapunov‐Krasovskii functional (LKF). The designed methodology is proposed in terms of linear matrix inequality to synthesize both event‐triggered parameters and controller gains, and to reduce the conservatism of the system some integral lemma are exploited to bind the time derivative of the LKF. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed method which provides a maximal upper bound value of the network‐induced delay and less transmitted packet regarding the maximal value delay obtained in other works, so less conservatism results are obtained, compared to previous ones in the literature. This article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources.

Recurrent faults in sensors, actuators, processes, or systems often arise in complex installations, typically accompanied by measurement noise or external disturbances. To minimize damage to machines or manufacturing processes and reduce downtime, predictive maintenance, fault diagnostics, and continuous monitoring are vital. Fault detection and diagnosis (FDD) is a key technology for precise fault diagnosis, and observer-based FDD is widely recognized and utilized for fault identification. However, conventional methods may not always provide optimal results. The application of artificial intelligence techniques has become necessary for improved accuracy in these scenarios. This paper proposes the use of the Quantum Black Hole Algorithm (QBHA), a recent advancement in optimization algorithms, to design an observer for FDD. The QBHA is employed to optimize the observer's gain values, ensuring enhanced fault detection capabilities. The observer gain adjustment in the proposed FDD observer design is crucial for reducing calculation errors and improving simulation accuracy. The effectiveness of the suggested method is demonstrated through simulation results on a coupled tank system, showing that the QBHA leads to superior performance in comparison to traditional techniques.