Reliable impulsive synchronization for fuzzy neural networks with mixed controllers

Area of Science:

• * Computational Intelligence
• * Control Systems Engineering
• * Nonlinear Dynamics

Background:

• * Master-slave fuzzy neural networks (FNNs) are crucial for complex system modeling.
• * Achieving synchronization in FNNs is challenging due to factors like actuator failures.
• * Direct state information access in master-slave configurations is often limited.

Purpose of the Study:

• * To investigate the synchronization of master-slave FNNs with random actuator failures.
• * To develop an efficient control strategy that minimizes communication and controller burdens.
• * To ensure robust synchronization even when the master FNN's state is not directly observable.

Main Methods:

• * Design of a simultaneously impulsive driven strategy for communication and control.
• * Development of a mixed controller combining observer-based and static control.
• * Application of Lyapunov stability theory to derive synchronization conditions.
• * Controller gain calculation based on derived theoretical results.

Main Results:

• * Sufficient conditions for achieving synchronization in MS FNNs under actuator failure were established.
• * A novel impulsive control strategy effectively reduced communication and controller loads.
• * The designed mixed controller ensured robust synchronization.
• * Numerical simulations validated the effectiveness of the proposed method.

Conclusions:

• * The proposed impulsive control strategy is effective for synchronizing MS FNNs with actuator failures.
• * The method offers a practical approach to reduce system resource demands.
• * The findings contribute to the advancement of robust control techniques for complex neural network systems.