Observer-Based Dissipativity Control for T-S Fuzzy Neural Networks With Distributed Time-Varying Delays

Area of Science:

• Control Theory
• Artificial Neural Networks
• Systems Engineering

Background:

• Takagi-Sugeno (T-S) fuzzy neural networks are widely used for modeling complex systems.
• Distributed time-varying delays present significant challenges in analyzing and controlling these networks.
• Ensuring global asymptotic stability and dissipativity is crucial for reliable system performance.

Purpose of the Study:

• To develop an observer-based dissipativity control strategy for T-S fuzzy neural networks with distributed time-varying delays.
• To model network channel delays using a distributed delay with its kernel.
• To guarantee global asymptotic stability and strict (Q, S,R) - α -dissipativity of the closed-loop system.

Main Methods:

• A novel Lyapunov-Krasovskii functional (LKF) is established, incorporating the kernel of the distributed delay.
• Delay-dependent reciprocally convex inequality is utilized to analyze stability and dissipativity.
• A new, less conservative set of linear matrix inequality (LMI) conditions is derived for controller design.

Main Results:

• The proposed observer-based controller ensures global asymptotic stability for the T-S fuzzy delayed model.
• The controller guarantees strict (Q, S,R) - α -dissipativity of the closed-loop system.
• Numerical simulations validate the effectiveness of the developed control strategy.

Conclusions:

• The proposed observer-based dissipativity control method effectively addresses challenges posed by distributed time-varying delays in T-S fuzzy neural networks.
• The novel LKF and LMI conditions provide a less conservative approach to controller design.
• The results contribute to the advancement of robust control techniques for complex dynamical systems.