Prescribed performance adaptive neural event-triggered control for switched nonlinear cyber-physical systems under...

Area of Science:

• Control Systems Engineering
• Nonlinear System Analysis
• Artificial Intelligence in Control

Background:

• Switched nonlinear systems are susceptible to external disturbances and deception attacks, compromising their stability and performance.
• Existing control strategies often struggle to simultaneously address unknown disturbances, performance bounds, and communication load reduction.

Purpose of the Study:

• To design an adaptive neural event-triggered control scheme for switched nonlinear systems.
• To mitigate the impact of unknown disturbances and deception attacks.
• To ensure prescribed performance bounds within finite time and reduce communication load.

Main Methods:

• Utilizing a switched nonlinear disturbance observer to estimate and compensate for unknown disturbances.
• Introducing a prescribed performance function to enforce finite-time performance bounds.
• Developing a dynamic event-triggered mechanism to optimize communication efficiency.

Main Results:

• The proposed control scheme guarantees that all signals within the closed-loop system remain bounded.
• Complete elimination of Zeno behavior is theoretically proven.
• Numerical simulations validate the effectiveness and robustness of the designed control strategy.

Conclusions:

• The adaptive neural event-triggered control scheme effectively addresses disturbances and attacks in switched nonlinear systems.
• The method achieves prescribed performance within finite time while minimizing communication overhead.
• The findings offer a robust solution for enhancing the reliability of complex control systems.