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Event-Triggered State Estimation for Complex Networks With Mixed Time Delays via Sampled Data Information: The
IEEE Transactions on Cybernetics
|January 14, 2015
Summary
This study introduces an event-triggered state estimation method for complex networks with mixed time delays. The proposed scheme reduces network traffic by transmitting data only when an event condition is met, ensuring bounded estimation errors.
Area of Science:
- Control Systems Engineering
- Networked Systems Analysis
- Stochastic Systems Theory
Background:
- Complex networks are susceptible to performance degradation due to mixed time delays.
- Traditional state estimation methods can lead to excessive network traffic.
- Event-triggered communication strategies offer a solution for efficient data transmission.
Purpose of the Study:
- To develop an event-triggered state estimation approach for complex networks with mixed time delays.
- To design a state estimator that minimizes network load.
- To ensure the estimation error remains ultimately bounded in mean square.
Main Methods:
- Utilizing Lyapunov theory and stochastic analysis to establish stability conditions.
- Designing a novel event-triggered transmission scheme based on a defined condition.
- Formulating the estimator design as a convex optimization problem.
Main Results:
- Sufficient conditions are derived to guarantee the ultimate boundedness of the estimation error in mean square.
- The proposed event-triggered scheme effectively reduces data transmission.
- Estimator gain matrices are obtained through solving a convex problem.
Conclusions:
- The developed event-triggered state estimation method is effective for complex networks with mixed time delays.
- The proposed approach enhances efficiency by minimizing network traffic.
- The results are validated through a numerical example demonstrating the estimator's performance.
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