Outlier-Resistant State Estimation for Singularly Perturbed Complex Networks With Nonhomogeneous Sojourn
IEEE Transactions on Cybernetics
|December 1, 2022
Summary
This study presents an outlier-resistant state estimator for singularly perturbed complex networks (SPCNs). The novel method ensures robust estimation despite random coupling strengths and varying sojourn probabilities.
Area of Science:
- Control Theory
- Network Science
- Systems Engineering
Background:
- Singularly perturbed complex networks (SPCNs) exhibit complex dynamics influenced by time-varying parameters.
- State estimation in such networks is challenging due to random coupling strengths and sojourn probabilities.
- Measurement outliers can significantly degrade the performance of traditional state estimators.
Purpose of the Study:
- To develop an outlier-resistant state estimation method for SPCNs.
- To account for time-varying sojourn probabilities and randomly occurring coupling strengths.
- To design a state estimator with adaptive saturation to mitigate outlier effects.
Main Methods:
- A novel switching law based on time-varying sojourn probabilities and a deterministic switching signal.
- Mode-dependent variables to model randomly occurring coupling strengths.
- A dynamic saturation function-based state estimator with adaptive saturation levels.
- Lyapunov theory and mode-dependent average dwell-time strategy for stability analysis.
Main Results:
- The proposed state estimator demonstrates outlier resistance.
- The resulting network dynamics are proven to be stochastic H∞ finite-time bounded.
- The adaptive saturation level effectively handles estimation errors.
Conclusions:
- The developed state estimation method is effective for SPCNs with complex dynamic behaviors.
- The approach provides robust and stable state estimation in the presence of outliers and parameter uncertainties.
- Simulation results validate the efficacy of the proposed estimator design.
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