Sequential Fusion Estimation for Multirate Complex Networks With Uniform Quantization: A Zonotopic Set-Membership...

Area of Science:

• Control Systems Engineering
• Signal Processing
• Networked Systems

Background:

• Multirate complex networks (MRCNs) present challenges in estimation due to varying sensor sampling periods.
• Unknown-yet-bounded (UYB) noises and uniform quantization further complicate accurate state estimation.
• Sequential fusion estimation is crucial for distributed systems with asynchronous and quantized data.

Purpose of the Study:

• To design a sequential set-membership estimator for MRCNs.
• To ensure estimation error is confined within a minimum F-radius zonotope under UYB noises, multirate sampling, and quantization.
• To achieve guaranteed performance bounds for the estimation error.

Main Methods:

• Transforming MRCNs into single-rate systems using virtual measurements.
• Utilizing zonotope properties to derive error bounds after each measurement update.
• Optimizing sequential estimator gain matrices by minimizing zonotope F-radii.

Main Results:

• The proposed method effectively confines estimation errors within zonotopes at each time instant.
• Uniform boundedness of the estimation error's F-radius is analyzed.
• Sufficient conditions for guaranteed uniform upper/lower bounds of the estimation error are derived.

Conclusions:

• The developed sequential fusion estimation method is effective for MRCNs with quantized measurements and UYB noises.
• The approach provides a robust framework for handling multirate sampling and quantization effects.
• The illustrated example validates the practical applicability of the proposed estimation strategy.