State estimation of networked nonlinear systems with aperiodic sampled delayed measurement.

Area of Science:

• Control Systems Engineering
• Nonlinear System Analysis
• Networked Systems

Background:

• Remote state estimation is crucial for networked nonlinear systems.
• Aperiodic sampled delayed measurements pose significant challenges to observer design.
• Existing methods often struggle with the complexities of non-affine nonlinearities and time delays.

Purpose of the Study:

• To develop a novel observer for remote state estimation of networked nonlinear systems.
• To address the challenges posed by aperiodic sampled delayed measurements.
• To ensure the stability and convergence of the state estimation.

Main Methods:

• Design of a novel sampled-data non-affine nonlinear observer (SNNO).
• Decomposition of the observer into continuous-time and auxiliary variable components.
• Application of trajectory-based stability theory to prove input-to-state stability.
• Analysis of the relationship between convergence rate and sampling/delay periods.

Main Results:

• The proposed SNNO effectively compensates for output estimation errors caused by sampled delayed measurements.
• Input-to-state stability of the observer is rigorously proven.
• A new theoretical tool is introduced to analyze the convergence rate concerning sampling and delay parameters.
• Simulations demonstrate the observer's performance and superiority over existing methods.

Conclusions:

• The developed SNNO provides a robust solution for remote state estimation in challenging networked nonlinear systems.
• The auxiliary variable compensation scheme offers a novel approach to handling sampled delayed data.
• The theoretical framework enhances understanding of observer dynamics under time-varying conditions.