Zonotopic Set-Membership Fusion Estimation for Complex Networks: A Buffer-Aided Strategy.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Estimation Theory

Background:

• Complex networks (CNs) face challenges in transmitting measurements over shared networks due to bandwidth limitations and intermittent data.
• Existing methods struggle with data loss and variable transmission intervals, impacting estimation accuracy.

Purpose of the Study:

• To design zonotopic set-membership fusion estimation (SMFE) algorithms for complex networks (CNs) with intermittent data transmissions.
• To enhance measurement utilization using buffered data and address transmission intervals exceeding sampling periods.
• To ensure the boundedness of estimation uncertainty in networked systems.

Main Methods:

• A batch processing method is proposed to concurrently process input data by iterating the state equation of the CN.
• Zonotopic set-membership estimation (SME) techniques are employed to design the SMFE algorithms.
• Sufficient criteria are established to guarantee uniform boundedness of output zonotope sizes.

Main Results:

• Novel SMFE algorithms are developed for CNs utilizing buffered, intermittently transmitted data.
• The proposed algorithms effectively handle transmission intervals larger than sampling periods.
• Guarantees for uniformly bounded zonotope sizes are established, ensuring estimation stability.

Conclusions:

• The developed SMFE algorithms enhance data utilization and estimation performance in complex networks with communication constraints.
• The findings provide a robust framework for state estimation in intermittently connected systems.
• Numerical examples validate the effectiveness and practical applicability of the proposed algorithms.