A refined adaptive event-based resilient filtering for complex networks over switching topology

Area of Science:

• Control Systems
• Network Engineering
• Signal Processing

Background:

• Resilient filtering is crucial for discrete-time complex networks with dynamic switching topologies.
• Limited channel bandwidth necessitates efficient data transmission strategies.

Purpose of the Study:

• To develop an adaptive event-triggered scheme for resilient filtering in complex networks with switching topology.
• To address bandwidth limitations and uncertain network parameters.

Main Methods:

• A refined adaptive event-triggered scheme is derived, with thresholds dynamically adjusted by measurement change rates.
• A resilient filter with variable gain is proposed under switching topology and uncertain inner coupling.
• The minimal upper bound of estimation error covariance is analyzed.

Main Results:

• The adaptive scheme dynamically adjusts data transmission, balancing filtering performance and network energy consumption.
• Sufficient conditions for exponential boundedness in mean square of the estimation error are established.
• Simulations confirm the effectiveness of the proposed resilient filtering approach.

Conclusions:

• The proposed adaptive event-triggered scheme enhances resilient filtering performance in complex networks with switching topology.
• The method effectively manages bandwidth limitations and network uncertainties.
• This work contributes to robust control and estimation in dynamic network environments.