Composite Anti-Disturbance Control for Nonlinear Hidden Markov Jump Systems Under Replay Attacks: A Dynamic...

Area of Science:

• Control Engineering
• Systems Science
• Cybersecurity

Background:

• Nonlinear hidden Markov jump systems are susceptible to external disturbances and cyberattacks like replay attacks.
• Existing control methods may not adequately address the combined challenges of system nonlinearities, state-jumping dynamics, and adversarial manipulations.

Purpose of the Study:

• To develop a composite anti-disturbance control strategy for interval type-2 Takagi-Sugeno fuzzy nonlinear hidden Markov jump systems under replay attacks.
• To design a robust control system that can maintain stability and performance despite disturbances and cyber threats.

Main Methods:

• Utilized interval type-2 Takagi-Sugeno fuzzy logic for system modeling.
• Employed dynamic output feedback control and a disturbance observer with nonparallel distribution compensation.
• Implemented a multisensor scheme with a detection mechanism to counter replay attacks.
• Applied Lyapunov stability theory to derive conditions for system stability.

Main Results:

• Sufficient conditions for ensuring the stability of the closed-loop system were established.
• The gains for the composite controller and disturbance observer were successfully determined.
• Simulations validated the effectiveness of the proposed method in compensating for disturbances and mitigating replay attacks.

Conclusions:

• The proposed composite anti-disturbance control method effectively enhances the robustness of nonlinear hidden Markov jump systems against combined disturbances and replay attacks.
• The developed technique demonstrates superior performance compared to the traditional H-infinity control method, offering a promising solution for secure and stable system operation.