Abstract:This paper deals with the problem of robust H ∞ synchronization of chaotic Lur'e systems with time-varying delays via sampled-data control. In order to make full use of the information about sampling intervals, nonlinear functions and time-varying delays, an improved Lyapunov-Krasovskii (L-K) functional is introduced. Based on reciprocal convex combination technique, sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to ensure the asymptotic synchronization of the considered Lur'e … Show more
“…9,[46][47][48][49][50][51][52][53] In this paper, we will use a three-layer multiple-input multiple-output (MIMO) neural network, which can be seen in Figure 1.…”
Section: B Description Of the Neural Networkmentioning
In this paper, we consider the control problem of a class of uncertain fractionalorder chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results. C 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license
“…9,[46][47][48][49][50][51][52][53] In this paper, we will use a three-layer multiple-input multiple-output (MIMO) neural network, which can be seen in Figure 1.…”
Section: B Description Of the Neural Networkmentioning
In this paper, we consider the control problem of a class of uncertain fractionalorder chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results. C 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license
“…A lot of works have been given on this theme because of its possible application in many fields such as communications, information processing [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. For example, Liu et al [19] discussed robust synchronization of uncertain complex networks by using impulsive control.…”
In this paper, an adaptive neural network (NN) synchronization controller is designed for two identical strict-feedback chaotic systems (SFCSs) subject to dead-zone input. The dead-zone models together with the system uncertainties are approximated by NNs. The dynamic surface control (DSC) approach is applied in the synchronization controller design, and the traditional problem of "explosion of complexity" that usually occurs in the backstepping design can be avoided. The proposed synchronization method guarantees the synchronization errors tend to an arbitrarily small region. Finally, this paper presents two simulation examples to confirm the effectiveness and the robustness of the proposed control method.
“…[2][3][4][5][6][7] Recently, fractional-order calculus has received a particular interest from physicists and engineers because it has some interesting and special properties compared with the integer-order one. [8][9][10][11][12][13][14][15][16][17][18][19][20] In reality, lots of actual systems can be described by fractional-order differential equations due to their unusual properties. On the other hand, fractional-order calculus has been employed to establish the system models in many domains, for example, biophysics, physics, engineering, aerodynamics, blood flow phenomena, biology, control theory, electron-analytical chemistry.…”
In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.
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