Asynchronous Partially Mode-Dependent Filtering of Network-Based MSRSNSs With Quantized Measurement.

Area of Science:

• Control Systems Engineering
• Nonlinear Systems Theory
• Networked Systems

Background:

• Networked systems often exhibit Markov switching behavior.
• Quantized measurements introduce challenges in filtering performance.
• Asynchronous mode-dependent filtering is crucial for robust control.

Purpose of the Study:

• To develop a novel partially mode-dependent filter (PMDF) for Markov switching repeated scalar nonlinear systems (MSRSNSs).
• To address filtering issues caused by asynchronous mode switching and quantized measurements.
• To guarantee the existence of the PMDF and ensure l2-l∞ performance.

Main Methods:

• A novel partially mode-dependent filter (PMDF) is designed.
• Random signal transmission of filter modes is modeled using a Bernoulli sequence.
• A diagonally dominant-type Lyapunov functional (DDTLF) is utilized to derive sufficient conditions.

Main Results:

• Sufficient conditions for the existence of the PMDF are established.
• The l2-l∞ performance index is derived for the filtering error.
• The developed theoretical results are validated using an economic example.

Conclusions:

• The proposed PMDF effectively handles asynchronous mode switching in MSRSNSs with quantized measurements.
• The derived conditions ensure guaranteed l2-l∞ performance.
• The approach is practical and applicable to real-world economic systems.