Adaptive performance control of switched nonlinear systems under false data injection attacks and input saturation...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Cybersecurity

Background:

• Switched nonlinear systems are vulnerable to false data injection (FDI) attacks.
• Implicit disturbances and input saturation challenge existing control and disturbance observer methods.
• Current prescribed performance control lacks effectiveness under input saturation.

Purpose of the Study:

• To develop an adaptive performance control strategy for switched nonlinear systems under FDI attacks, composite disturbances, and input saturation.
• To address limitations of existing disturbance observers in handling implicit disturbances.
• To overcome the ineffectiveness of conventional methods in the presence of input saturation.

Main Methods:

• A novel switched nonlinear disturbance observer using dynamic high gain to reconstruct implicit mismatched disturbances.
• An adaptive update law to compensate for matched disturbances.
• A modified fixed-time performance function to balance input saturation and tracking performance.
• Neural network approximation within backstepping control to mitigate FDI attack effects.

Main Results:

• The proposed disturbance observer effectively reconstructs implicit mismatched disturbances.
• The adaptive update law successfully compensates for matched disturbances.
• The modified performance function balances input saturation and tracking.
• Neural network approximation mitigates FDI attack impacts.
• Simulation results demonstrate effective tracking control.

Conclusions:

• The developed adaptive performance control strategy effectively manages switched nonlinear systems under FDI attacks, composite disturbances, and input saturation.
• The novel disturbance observer and adaptive control techniques enhance system resilience and performance.
• The strategy offers a flexible and robust solution for complex control scenarios.