International Journal of Control, Automation and Systems 2012; 10(5): 873-882
Published online September 30, 2012
https://doi.org/10.1007/s12555-012-0502-6
© The International Journal of Control, Automation, and Systems
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.
Keywords Finite-time, H∞ control, linear matrix inequalities, neural networks, stochastic systems.
International Journal of Control, Automation and Systems 2012; 10(5): 873-882
Published online October 1, 2012 https://doi.org/10.1007/s12555-012-0502-6
Copyright © The International Journal of Control, Automation, and Systems.
Zhiguo Yan, Guoshan Zhang*, and Jiankui Wang
Tianjin University
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.
Keywords: Finite-time, H&infin, control, linear matrix inequalities, neural networks, stochastic systems.
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