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Order-dependent sampling control of uncertain fractional-order neural networks system
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In this paper, we address the asymptotic stability problem for the
fractional-order neural networks system with uncertainty based on
sampled-data control. First, considering the influence of uncertainty
and fractional-order on the system, a new sampled-data control scheme
with variable sampling period is designed. According to the input delay
approach, the dynamics of the considered fractional-order system is
modeled by a delay system. The main purpose of the problem addressed is
to design a sampled-data controller, such that the closed-loop
fractional-order system can guarantee the asymptotic stability. Then,
the fractional-order Razumishin theorem and linear matrix inequalities
(LMIs) are used to derive the stable conditions. The new delay-dependent
and order-dependent stability conditions are presented in the form of
LMIs. Furthermore, the sampling controller can be obtained to ensure the
stability and stabilization of fractional-order system. Finally, a
numerical example is given to demonstrate the effectiveness and
advantages of the proposed method.
Title: Order-dependent sampling control of uncertain fractional-order neural networks system
Description:
In this paper, we address the asymptotic stability problem for the
fractional-order neural networks system with uncertainty based on
sampled-data control.
First, considering the influence of uncertainty
and fractional-order on the system, a new sampled-data control scheme
with variable sampling period is designed.
According to the input delay
approach, the dynamics of the considered fractional-order system is
modeled by a delay system.
The main purpose of the problem addressed is
to design a sampled-data controller, such that the closed-loop
fractional-order system can guarantee the asymptotic stability.
Then,
the fractional-order Razumishin theorem and linear matrix inequalities
(LMIs) are used to derive the stable conditions.
The new delay-dependent
and order-dependent stability conditions are presented in the form of
LMIs.
Furthermore, the sampling controller can be obtained to ensure the
stability and stabilization of fractional-order system.
Finally, a
numerical example is given to demonstrate the effectiveness and
advantages of the proposed method.
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