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Non-fragile Robust Finite-time H∞ Control for Nonlinear Stochastic Itô Systems Using Neural Network
Zhiguo Yan,Guoshan Zhang,Jiankui Wang 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.5
This paper deals with the problem of non-fragile robust finite-time H∞ control for a class of uncertain nonlinear stochastic Itô systems via neural network. First, applying multi-layer feedback neural networks, the nonlinearity is approximated by linear differential inclusion (LDI) under state-space representation. Then, a sufficient condition is proposed for the existence of non-fragile state feedback finite-time H∞ controller in terms of matrix inequalities. Furthermore, the problem of non-fragile robust finite-time H∞ control is reduced to the optimization problem involving linear matrix inequalities (LMIs), and the detailed solving algorithm is given for the restricted LMIs. Finally, an example is given to illustrate the effectiveness of the proposed method.
Preparation and effect evaluation of rigid polyurethane flame retardant modified by graphene
Jianlian Liu,Binghuan Wang,Kui Zi,Jiankui Yu,Peisuo Li 한국탄소학회 2023 Carbon Letters Vol.33 No.7
In this study, we investigate the impact of Isophorone diisocyanate functionalized graphene oxide (IPDI-GO) on the flame retardancy of rigid polyurethane foam (RPUF). IPDI-GO was synthesized and introduced into the RPUF matrix. The flame retardancy of RPUF was significantly enhanced by the incorporation of IPDI-GO, as evidenced by a reduction in peak heat release rate (PHRR) by 25% and total smoke production (TSP) by 15% in comparison to pure RPUF when IPDI-GO was incorporated at 3 wt%. Scanning electron microscopy (SEM) revealed that IPDI-GO contributed to the formation of a compact, continuous char layer on the RPUF surface. This study underscores the potential of IPDI-GO as a promising flame retardant additive for RPUF.