Simultaneous State and Unknown Input Estimation for Complex Networks With Redundant Channels Under Dynamic...

Area of Science:

• Control Systems Engineering
• Networked Systems
• Estimation Theory

Background:

• Complex networks (CNs) face challenges in state and unknown input estimation due to unreliable communication.
• Redundant channels and dynamic event-triggered mechanisms (ETMs) offer potential solutions for enhanced reliability and energy efficiency.

Purpose of the Study:

• To design a recursive estimator for simultaneous state and unknown input estimation in discrete time-varying CNs.
• To guarantee and minimize estimation error covariances under redundant channels and dynamic ETMs.

Main Methods:

• Modeling redundant channels using mutually independent Bernoulli distributed stochastic variables.
• Implementing a dynamic event-triggered transmission scheme for energy-efficient data reporting.
• Developing a recursive estimator by solving difference equations to compute optimal estimator gains.

Main Results:

• The proposed recursive estimator successfully guarantees and minimizes upper bounds on estimation error covariances.
• The estimator operates effectively in the presence of both dynamic event-triggered strategies and redundant channels.
• The method ensures reliable state and unknown input estimation even with stochastic channel behavior.

Conclusions:

• The developed estimator design method is effective for simultaneous state and unknown input estimation in complex networks.
• The integration of redundant channels and dynamic ETMs enhances estimation performance and reliability.
• The approach offers a practical solution for networked systems requiring robust estimation under communication constraints.