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Sampled‐data filter design for large‐scale interconnected systems with sensor fault and missing measurements
Author(s) -
Sakthivel Rathinasamy,
Sweetha Senthilrathnam,
Tharanidharan Vasudevan,
Harshavarthini Shanmugam
Publication year - 2021
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3217
Subject(s) - control theory (sociology) , filter (signal processing) , filter design , set (abstract data type) , computer science , exponential stability , stability (learning theory) , scale (ratio) , mathematics , nonlinear system , control (management) , artificial intelligence , machine learning , computer vision , programming language , physics , quantum mechanics
Summary This article focuses on a decentralized sampled‐data filter design for a class of large‐scale interconnected systems. Precisely in the addressed system, the inevitable factors such as missing measurements, time‐varying delays, randomly occurring uncertainties, and impulsive effects are taken into consideration. Also, we incorporated the gain perturbations and sensor faults in the proposed filter design. Furthermore, a new set of sufficient criterion has been derived by choosing an appropriate Lyapunov‐Krasovskii functional that ensures the asymptotic stability of the resulting augmented filtering error system with the prescribed mixed H ∞ and passive performance index. Specifically, the corresponding filter gain matrices are derived by solving the developed sufficient criterion formulated in terms of linear matrix inequalities. The effectiveness of the proposed filter design technique are then exemplified by two numerical examples with simulations.