l₂-l_∞ state estimation for delayed artificial neural networks under high-rate communication channels with Round-Robin...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• State estimation is crucial for monitoring complex systems like artificial neural networks.
• Delayed systems and communication constraints pose significant challenges in accurate state estimation.
• High-rate communication channels with protocols like Round-Robin (RR) introduce specific scheduling dynamics.

Purpose of the Study:

• To design an l2-l∞ state estimator for delayed artificial neural networks.
• To ensure exponential stability of the estimation error dynamics.
• To meet the l2-l∞ performance constraint under high-rate channels and RR protocol.

Main Methods:

• Utilizing a Round-Robin (RR) protocol for sensor data transmission scheduling.
• Developing sufficient conditions for the existence of the l2-l∞ state estimator.
• Solving matrix inequalities to determine the estimator gains.

Main Results:

• Sufficient conditions guaranteeing the existence of the l2-l∞ state estimator were established.
• The estimator gains were successfully derived through matrix inequality solutions.
• Numerical examples validated the effectiveness of the proposed state estimation scheme.

Conclusions:

• The developed l2-l∞ state estimation scheme is effective for delayed artificial neural networks under specific communication constraints.
• The method ensures both stability and performance bounds for the estimation error.
• This work contributes to robust state estimation in networked control systems.