Event-Based Variance-Constrained ${\mathcal {H}}_{\infty }$ Filtering for Stochastic Parameter Systems Over Sensor
IEEE Transactions on Cybernetics
|March 14, 2017
Summary
This study develops an event-triggered distributed filter for discrete time-varying systems with missing sensor data. The filter ensures performance and variance constraints, reducing communication load in sensor networks.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Stochastic Processes
Background:
- Distributed filtering is crucial for sensor networks.
- Successive missing measurements and communication burden are key challenges.
- Stochastic parameter systems require robust filtering solutions.
Purpose of the Study:
- Design a time-varying filter for discrete time-varying stochastic parameter systems.
- Address successive missing measurements and error variance constraints.
- Incorporate an event-triggered mechanism to optimize communication.
Main Methods:
- Modeling successive missing measurements using Bernoulli distribution.
- Employing an event-triggered mechanism for data transmission.
- Utilizing stochastic analysis techniques to derive filter conditions.
Main Results:
- Sufficient conditions for filter existence are established.
- Time-varying filter gain matrices are explicitly characterized.
- The developed strategy guarantees performance and variance constraints.
Conclusions:
- The event-triggered distributed filter effectively handles missing data and communication constraints.
- The proposed design ensures system stability and performance requirements.
- Numerical simulations validate the effectiveness of the developed strategy.
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