Nonfragile State Estimation of Quantized Complex Networks With Switching Topologies

Area of Science:

• Control Systems Engineering
• Networked Systems
• Estimation Theory

Background:

• Complex networks exhibit dynamic behavior with switching topologies.
• Quantization effects introduce uncertainties in signal transmission.
• Nonfragility is crucial for robust estimator performance.

Purpose of the Study:

• To develop a nonfragile H-infinity estimation method for complex networks.
• To address challenges posed by switching topologies and quantization.
• To ensure robust stability and performance of the estimation error.

Main Methods:

• Modeling network dynamics with random switching and sojourn probabilities.
• Transforming quantization errors into sector-bounded uncertainties.
• Introducing random uncertainties into estimator parameters for nonfragility.
• Utilizing linear matrix inequality (LMI) for condition derivation.

Main Results:

• A sufficient condition for stochastic stability of the estimation error is derived.
• The proposed method guarantees H-infinity performance with a specified index.
• The approach accounts for multiple operation modes of quantizers and estimators.

Conclusions:

• The developed method effectively handles nonfragile H-infinity estimation in complex networks.
• The LMI approach provides a systematic way to ensure stability and performance.
• A numerical example validates the proposed estimation technique.