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This study designs a non-fragile state estimator for nonlinear complex networks with random couplings and time delays. The method ensures estimation error boundedness despite estimator gain variations, minimizing the ultimate bound.

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Area of Science:

  • Control Theory
  • Networked Systems
  • Nonlinear Dynamics

Background:

  • Complex networks are susceptible to random couplings and time delays.
  • Binary encoding schemes (BESs) are used for data transmission, introducing potential bit errors.
  • Non-fragile state estimation is crucial for maintaining performance under parameter uncertainties.

Purpose of the Study:

  • Design a non-fragile state estimator for nonlinear complex networks with random couplings and time delays.
  • Ensure exponential ultimate boundedness in mean square for estimation error dynamics despite estimator gain perturbations.
  • Minimize the ultimate bound of the estimation error.

Main Methods:

  • Utilizing stochastic analysis and matrix inequality processing.
  • Representing random couplings using Kronecker delta functions and Markov chains.
  • Solving a constrained optimization problem involving linear matrix inequalities to determine estimator gains.

Main Results:

  • A sufficient condition is derived to guarantee the designed estimator's performance.
  • The estimator gains are obtained by solving a linear matrix inequality-constrained optimization problem.
  • Simulation examples validate the effectiveness of the proposed non-fragile estimator design.

Conclusions:

  • The proposed method effectively designs a non-fragile state estimator for complex networks under challenging conditions.
  • The approach guarantees robust estimation performance against estimator gain variations.
  • The methodology provides a framework for designing reliable state estimators in networked systems.