Data-Driven Model-Free Adaptive Dynamic Programming Resilient Control for Nonlinear Networked Control Systems Under...

Area of Science:

• Computer Science
• Control Engineering
• Cybersecurity

Background:

• Existing denial-of-service (DoS) attack compensation methods struggle with dynamic scenarios.
• Model-free adaptive control lacks optimization and constant reference signal support.
• Networked control systems (NCS) require enhanced resilience against sophisticated cyber threats.

Purpose of the Study:

• To develop a data-driven adaptive dynamic programming (ADP) resilient control scheme for NCS under aperiodic DoS attacks.
• To address limitations of existing methods in handling time-varying reference signals and optimizing control.
• To improve tracking performance and reduce degradation during dynamic DoS attacks.

Main Methods:

• Implementation of an ADP method with a modified performance index for optimal controller derivation.
• Introduction of a dynamic penalty factor to accelerate error convergence.
• Design of a compensation mechanism using ADP and control increments for time-varying signals.

Main Results:

• The proposed ADP scheme achieves globally optimal control.
• Dynamic penalty factor enhances convergence speed.
• Compensation mechanism effectively reduces performance degradation under time-varying conditions.
• Theoretical proofs confirm error convergence.

Conclusions:

• The developed ADP-based resilient control scheme significantly enhances NCS security and performance against DoS attacks.
• The strategy offers superior tracking performance compared to existing methods, especially in dynamic attack scenarios.
• This approach provides a robust solution for securing NCS in the presence of aperiodic DoS threats.