Multi-sensor optimal H∞ fusion filters for delayed nonlinear intelligent systems based on a unified model

This paper is concerned with multi-sensor optimal H(∞) fusion filtering for a class of nonlinear intelligent systems with time delays. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these systems, such as neural networks and Takagi and Sugeno (T-S) fuzzy models. Based on the H(∞) performance analysis of this unified model using the linear matrix inequality (LMI) approach, centralized and distributed fusion filters are designed for multi-sensor time-delayed systems to guarantee the asymptotic stability of the fusion error systems and to reduce the influence of noise on the filtering error. The parameters of these filters are obtained by solving the eigenvalue problem (EVP). As most artificial neural networks or fuzzy systems with or without time delays can be described with this unified model, fusion filter design for these systems can be done in a unified way. Simulation examples are provided to illustrate the design procedure and effectiveness of the proposed approach.

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