Adaptive Neural Network Boundary Security Consensus Control of Nonlinear Delayed Multiagent PDE Systems Under Hybrid...

Area of Science:

• Control Theory
• Systems Engineering
• Artificial Intelligence

Background:

• Multiagent systems (MASs) with partial differential equations (PDEs) present complex control challenges.
• Unknown boundary nonlinearities and hybrid attacks (deception, denial-of-service) compromise system security and stability.
• Existing control strategies often struggle with the combined effects of delays, nonlinearities, and sophisticated attacks.

Purpose of the Study:

• To develop a robust security consensus control strategy for nonlinear delayed MASs modeled by PDEs.
• To address unknown boundary nonlinearities and hybrid attacks simultaneously.
• To ensure practical exponential stability (PES) in the mean square under adversarial conditions.

Main Methods:

• A composite adaptive neural network boundary security consensus controller was designed.
• The controller integrates a consensus control component and an adaptive neural network for nonlinearity approximation.
• Lyapunov-based analysis and linear matrix inequality (LMI) techniques were employed to derive stability conditions.

Main Results:

• The proposed controller guarantees security consensus control in the mean square under hybrid attacks.
• Sufficient conditions for practical exponential stability (PES) in the mean square were established.
• Numerical simulations confirmed the controller's effectiveness on a leader-follower MAS.

Conclusions:

• The developed composite adaptive neural network controller effectively manages nonlinear delayed MASs with unknown boundary nonlinearities and hybrid attacks.
• The control strategy ensures system security and stability, achieving consensus in the mean square.
• This research offers a significant advancement in secure control for complex distributed systems.