PROJECT TITLE :

Fault Detection Filtering for Nonlinear Switched Stochastic Systems

ABSTRACT:

During this note, the fault detection filtering problem is solved for nonlinear switched stochastic system within the T-S fuzzy framework. Our attention is targeted on the construction of a sturdy fault detection technique to the nonlinear switched system with Brownian motion. Based on observer-primarily based fault detection fuzzy filter as a residual generator, the proposed fault detection is formulated as a fuzzy filtering drawback. By the use of the average dwell time technique and the piecewise Lyapunov operate technique, the fuzzy-parameter-dependent fault detection filters are designed that guarantee the resulted error system to be mean-square exponential stable with a weighted $mathcal H_infty$ error performance. Then, the corresponding solvability condition for the fault detection fuzzy filter is additionally established by the linearization procedure technique. Finally, simulation has been presented to indicate the effectiveness of the proposed fault detection technique.