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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Control Systems: Applications01:25

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Transfer Function in Control Systems01:21

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The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
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    Summary
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    This study introduces a resilient control framework using fuzzy models and Q-learning for cyber-physical systems (CPSs) facing attacks. The method enhances security and performance by adaptively optimizing control strategies against cyber-physical threats.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Cyber-physical systems (CPSs) are vulnerable to sensor and actuator attacks, especially under communication constraints.
    • Nonlinear descriptor systems require robust control strategies to maintain stability and performance during cyber-physical threats.
    • Existing security frameworks often struggle with adaptive control and optimization under complex attack scenarios.

    Purpose of the Study:

    • To propose a resilient control framework for nonlinear descriptor cyber-physical systems under communication constraints and cyber-physical attacks.
    • To develop an adaptive fuzzy sliding-mode observer (SMO) for estimating compromised system states with mismatched premise variables.
    • To design a sliding-mode controller (SMC) that ensures closed-loop admissibility and sliding surface reachability, optimized using the secretary bird optimization algorithm (SBOA).

    Main Methods:

    • Integration of Takagi-Sugeno (T-S) fuzzy models with a Q-learning-based event-triggered mechanism (ETM).
    • Development of an adaptive fuzzy sliding-mode observer (SMO) and a sliding-mode controller (SMC).
    • Application of the secretary bird optimization algorithm (SBOA) for optimizing controller and observer gains to address nonconvex optimization problems.

    Main Results:

    • The proposed framework effectively balances operational efficiency with robust protection against cyber-physical threats.
    • The adaptive fuzzy SMO accurately estimates compromised system states, even with mismatched premise variables.
    • Simulations on a truck-trailer system validated the approach's efficacy in maintaining system stability and performance under various attack scenarios.

    Conclusions:

    • The developed resilient control framework significantly enhances the security of nonlinear cyber-physical systems in networked environments.
    • The integration of fuzzy logic, Q-learning, and sliding-mode control, optimized by SBOA, provides a novel solution for adaptive security.
    • This work contributes to securing CPSs against sophisticated cyber-physical attacks, ensuring reliable system operation.