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H ∞ control of memristive neural networks with aperiodic sampling and actuator saturation
Author(s) -
Wang Jinling,
Jiang Haijun,
Ma Tianlong,
Hu Cheng
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4068
Subject(s) - aperiodic graph , control theory (sociology) , exponential stability , artificial neural network , mathematics , sampling (signal processing) , controller (irrigation) , actuator , computer science , control (management) , nonlinear system , detector , physics , combinatorics , agronomy , biology , machine learning , telecommunications , artificial intelligence , quantum mechanics
Summary The H ∞ control problem for memristive neural networks with aperiodic sampling and actuator saturation is considered in this paper. A novel approach that is combined with the discrete‐time Lyapunov theorem and sampled‐data system is proposed to cope with the aperiodic sampling problem. On the basis of such method and choosing a polyhedral set, sufficient conditions to determine the ellipsoidal region of asymptotic stability and exponential stability for the estimation error system are obtained through a saturating sampled‐data control. Furthermore, H ∞ performance index of memristive neural networks with disturbance is also analyzed, whereas the observer and controller gains are calculated from stability conditions of linear matrix inequalities. Finally, the effectiveness of the theoretical results is illustrated through the numerical examples.