Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
A delay-dependent approach to robust H∞ filtering for uncertain discrete-time state-delayed systems
IEEE Transactions on Signal Processing
Robust H∞ control for uncertain discrete-time-delay fuzzy systems via output feedback controllers
IEEE Transactions on Fuzzy Systems
Robust Fuzzy Filter Design for a Class of Nonlinear Stochastic Systems
IEEE Transactions on Fuzzy Systems
Optimal recursive estimation with uncertain observation
IEEE Transactions on Information Theory
Delayed Standard Neural Network Models for Control Systems
IEEE Transactions on Neural Networks
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This paper is concerned with the problem of multi-sensor optimal H"~ fusion filtering for a class of discrete-time stochastic intelligent systems with missing measurements and time delays. This discrete-time intelligent system model, which is composed of a linear dynamic system and a bounded static nonlinear operator, presents a unified description of delayed or non-delayed intelligent systems composed of neural networks and Takagi and Sugeno (T-S) fuzzy models, Lur'e systems, and linear systems. The missing measurements from multi-sensors are described by a binary switching sequence that obeys a conditional probability distribution. We aim to design both centralized and distributed fusion filters such that, for all possible missing observations, the fusion error is globally asymptotically stable in the mean square, and the prescribed H"~ performance constraint is satisfied. By employing the Lyapunov-Krasovskii functional method with the stochastic analysis approach, several delay-independent criteria, which are in the form of linear matrix inequalities (LMIs), are established to ensure the existence of the desired multi-sensor H"~ fusion filters. An optimization problem is subsequently formulated by optimizing the H"~ filtering performances, which is described as the eigenvalue problem (EVP). Finally, simulation examples are provided to illustrate the design procedure and expected performance.