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Protocol-based state estimation for delayed Markovian jumping neural networks.

Jiahui Li1, Hongli Dong1, Zidong Wang2

  • 1Institute of Complex Systems and Advanced Control, Northeast Petroleum University, Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing 163318, China.

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Summary
This summary is machine-generated.

This study develops a state estimator for Markovian jumping neural networks (MJNNs) with sensor nonlinearities and time delays under Round-Robin scheduling. The proposed method ensures bounded estimation error, enhancing system reliability.

Keywords:
Exponentially ultimately bounded estimatorMarkovian jumping neural networksMode-dependent time delaysRound-Robin protocolSensor nonlinearities

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Area of Science:

  • Control Systems Engineering
  • Neural Networks
  • Stochastic Systems

Background:

  • Markovian jumping neural networks (MJNNs) present challenges in state estimation due to parameter uncertainties and time delays.
  • Sensor nonlinearities and stochastic disturbances further complicate accurate state monitoring.
  • The Round-Robin (RR) scheduling mechanism is increasingly used for resource management in networked systems.

Purpose of the Study:

  • To design a state estimator for MJNNs with sensor nonlinearities, mode-dependent time delays, and stochastic disturbances.
  • To address the challenges posed by the Round-Robin (RR) scheduling mechanism on state estimation.
  • To ensure the estimation error is exponentially ultimately bounded in the mean square.

Main Methods:

  • Utilizing the Lyapunov stability theory and stochastic analysis techniques.
  • Employing the update matrix method to handle periodic time-delays introduced by the RR protocol.
  • Formulating the estimator gain matrices as a convex optimization problem.

Main Results:

  • Sufficient conditions for the existence of a state estimator were established.
  • The proposed estimator guarantees exponential ultimate boundedness of the estimation error in the mean square.
  • The effectiveness of the estimator design was validated through a numerical simulation.

Conclusions:

  • The developed state estimator effectively addresses complex dynamics in MJNNs under RR scheduling.
  • The methodology provides a robust framework for state estimation in systems with nonlinearities, delays, and stochasticity.
  • The results contribute to improved reliability and performance of networked control systems.