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Learning-Based Model-Free Adaptive Control for Nonlinear Discrete-Time Networked Control Systems Under Hybrid Cyber

Fanghui Li, Zhongsheng Hou

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    This study introduces a learning-based model-free adaptive control (LMFAC) for unknown nonlinear networked control systems (NCSs) under cyber attacks. The novel approach ensures system performance and tracking error boundedness despite data loss or deception.

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    Area of Science:

    • Control Engineering
    • Cybersecurity
    • Systems Science

    Background:

    • Networked control systems (NCSs) face vulnerabilities from hybrid cyber attacks like denial-of-service (DoS) and deception attacks.
    • Existing model-based control frameworks require precise system mathematical models, which are often unavailable for unknown nonlinear systems.

    Purpose of the Study:

    • To develop a data-driven adaptive control strategy for unknown nonlinear discrete-time NCSs facing hybrid cyber attacks.
    • To enhance system performance and ensure stability under conditions of signal absence or inauthenticity in feedback channels.

    Main Methods:

    • Utilized dynamic linearization technology to establish data-driven models from I/O information, bypassing the need for system identification.
    • Designed a learning-based model-free adaptive control (LMFAC) scheme considering maximum DoS attack intervals.
    • Employed the contraction mapping principle for rigorous mathematical proof of tracking error boundedness.

    Main Results:

    • Successfully established equivalent dynamic linearized data models using only input-output data.
    • Developed an adaptive control scheme that tunes signal attenuation based on predicted DoS attack intervals.
    • Demonstrated the boundedness of tracking error and the effectiveness of the pure data-driven LMFAC approach through simulations.

    Conclusions:

    • The proposed learning-based model-free adaptive control (LMFAC) offers a robust solution for NCSs under hybrid cyber attacks.
    • This pure data-driven method effectively handles unknown nonlinear dynamics and cyber-attack-induced signal uncertainties.
    • The approach provides a viable alternative to traditional model-based control in challenging NCS environments.