Delay Compensation-Based State Estimation for Time-Varying Complex Networks With Incomplete Observations and...

Area of Science:

• Control Systems Engineering
• Network Science
• Signal Processing

Background:

• Discrete time-varying complex networks (DTVCNs) face challenges with network-induced incomplete observations (NIIOs) and dynamical bias.
• NIIOs encompass communication delays and fading observations, complicating accurate state estimation.
• Dynamical bias, modeled by a distinct equation, further degrades estimation performance.

Purpose of the Study:

• To develop a robust delay-compensation-based state estimation (DCBSE) method for DTVCNs.
• To address the challenges posed by NIIOs, including communication delays and fading observations.
• To mitigate the impact of dynamical bias on state estimation accuracy.

Main Methods:

• A predictive scheme is employed to counteract communication delays by utilizing predictive-based estimation mechanisms.
• A new distributed state estimation approach is presented for enhanced performance.
• A recursive method is designed for determining the estimator gain matrix.

Main Results:

• The proposed DCBSE method achieves a locally minimized upper bound for the estimation error covariance matrix.
• Performance evaluation criteria based on monotonicity are analytically derived.
• Experimental comparisons demonstrate the validity and superiority of the new DCBSE approach.

Conclusions:

• The developed DCBSE method effectively handles communication delays and dynamical bias in DTVCNs.
• The predictive scheme significantly improves state estimation in the presence of network-induced uncertainties.
• The study offers a valuable contribution to state estimation techniques for complex network systems.