Remote State Estimation for Nonlinear Systems via a Fading Channel: A Risk-Sensitive Approach

Area of Science:

• Control Systems Engineering
• Signal Processing
• Nonlinear Dynamics

Background:

• Remote state estimation is crucial for networked systems, but faces challenges like packet loss over communication channels.
• Fading channels and intermittent measurements in nonlinear systems complicate accurate state estimation.
• Existing methods often struggle with nonlinear dynamics and communication uncertainties.

Purpose of the Study:

• To develop a robust nonlinear estimator for systems experiencing packet loss.
• To minimize an exponential cost criterion using a risk-sensitive (RS) approach.
• To ensure estimator stability and improve estimation accuracy in challenging communication environments.

Main Methods:

• Formulation of the estimation problem using the risk-sensitive (RS) approach.
• Derivation of a closed-form nonlinear RS estimator via the reference measure method.
• Extension of contraction analysis to establish estimator stability conditions for nonlinear systems.
• Design of a novel cost function to handle linearization errors as model uncertainties.

Main Results:

• A closed-form nonlinear risk-sensitive estimator is derived.
• Stability conditions for the nonlinear estimator are established.
• The proposed estimator demonstrates superior performance compared to nonlinear minimum mean square error methods.
• The novel cost function effectively counteracts linearization errors.

Conclusions:

• The developed nonlinear RS estimator provides a robust solution for state estimation with packet loss.
• The method offers improved estimation accuracy and stability for nonlinear systems over fading channels.
• This work advances the field of remote estimation in the presence of communication uncertainties.