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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.
Liming Wang,Guoshan Zhang 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.3
This paper is devoted to the perfect tracking problem of output consensus for a class of non-identical fractional order multi-agent systems (NIFOMASs), in which different agents have different and unknown fractional orders and dynamic functions. For the NIFOMASs including one leader agent and multiple follower agents, by designing the event-triggered mechanism along an iteration axis and introducing it into the iterative learning controller, an event-triggered iterative learning consensus protocol is proposed to reduce the number of controller update and to save the communication resource. By analyzing the convergence of learning process, the sufficient conditions are derived to guarantee that the output consensus tracking can be perfectly achieved over the finite time interval as the iteration step goes to infinity. Finally, three numerical examples are presented to demonstrate the effectiveness and wide application scope of the proposed control strategy.