Outer synchronization of partially coupled dynamical networks via pinning impulsive controllers

Lu, Jianquan; Ding, Chengdan; Lou, Jungang; Cao, Jinde

doi:10.1016/j.jfranklin.2015.08.016

Cited by 152 publications

(19 citation statements)

References 48 publications

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“…That is, the dynamical output feedback controller (3) and the system (5) are updated on different time intervals with the updated signals y(t k h) and Kx(t k h), respectively. Thus, it is difficult to derive the closed-loop system directly from (3) and (5). To obtain the closed-loop system, we divide the update interval of the system (5) using the update instant of the system (3).…”

Section: Event-triggered Output Feedback H ∞ Controlmentioning

confidence: 99%

“…Recently, networked control systems (NCSs) have received considerable attention and have been applied extensively in practice [1][2][3][4][5][6][7][8] because they carry several advantages, including cost effectiveness, simple installation, high reliability, and reduced weight and power requirements. They are becoming increasingly important in industrial process control.…”

Section: Introductionmentioning

confidence: 99%

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Event‐triggered output feedback H_∞ control for networked control systems

Shen

Zhang

et al. 2018

Intl J Robust & Nonlinear

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Summary This paper investigates event‐triggered output feedback H∞ control for a networked control system. Transmitted through a network under an event‐triggered scheme, the sample outputs of the plant are used to drive the dynamical output feedback controller to generate a new control signal in the discrete‐time domain. The discrete‐time control signals are also transmitted through the network to drive the plant. As a result of two types of transmission delays, the controlled plant and the dynamical output feedback controller are driven by the discrete‐time outputs and control signals at different instants of time. An interval decomposition method is introduced to place the controlled plant and the output feedback controller into the same updated time interval but with updated signals at different instants. Based on a proper Lyapunov‐Krasovskii functional, sufficient conditions are derived to ensure H∞ performance for the controlled plant. Finally, numerical simulations are used to demonstrate the practical utility of the proposed method.

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Section: Event-triggered Output Feedback H ∞ Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event‐triggered output feedback H_∞ control for networked control systems

Shen

Zhang

et al. 2018

Intl J Robust & Nonlinear

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“…In general, synchronization means that a system is designed to simulate the dynamic behavior of another system, that is, the state trajectory of two systems is finally identical. Therefore, many important results on synchronization have been obtained in the last few years [17][18][19][20], and various control strategies have been developed to design effective controllers for achieving synchronization, such as adaptive control [21], pinning control [22], feedback control [23], impulsive control [24][25][26][27], and intermittent control [28].…”

Section: Introductionmentioning

confidence: 99%

Exponential Synchronization of Neural Networks via Feedback Control in Complex Environment

Cao

et al. 2018

Complexity

Self Cite

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The problem of exponential synchronization for neural networks is investigated via feedback control in complex environment. By constructing suitable Lyapunov-Krasovskii functionals and applying the piecewise analytic method, some sufficient criteria for exponential synchronization of the addressed neural networks are established in terms of linear matrix inequalities (LMIs). The feedback control in complex environment includes the delayed aperiodically intermittent control and dynamic output feedback control. Moreover, the delayed aperiodically intermittent dynamic output feedback controller is designed based on the established LMIs. A numerical example and its numerical simulations are finally presented to show the effectiveness of obtained theoretical results.

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“…In order to guarantee synchronization between two chaotic systems can be realized, several control schemes have been proposed, such as feedback control, adaptive control, intermittent control and impulsive control [12][13][14][15][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Intermittent control, as a discontinuous control method, has been adopted in engineering fields in view of its convenient implementation in engineering control [33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Adaptive exponential lag synchronization for neural networks with mixed delays via intermittent control

Zhou

Cai

2018

Adv Differ Equ

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In this paper, the problem of exponential lag synchronization for a class of neural networks with mixed delays including discrete and distributed delays is investigated via adaptive intermittent control. Based on piecewise analytic method, some sufficient conditions for globally exponential lag synchronization are established through constructing a piecewise continuous auxiliary function. It is noted that both the control periods and the control widths in our adaptive intermittent control strategy are allowed to be nonidentical, which extends the scope of application of periodically intermittent control with fixed both control period and control width employed widely in previous works. Moreover, it is shown that the derived globally exponential lag synchronization criteria are related to the control rates rather than the control periods, which facilitates the choice of the control periods for practical problems. Finally, a numerical example is given to illustrate the correctness of the obtained theoretical results.

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Outer synchronization of partially coupled dynamical networks via pinning impulsive controllers

Cited by 152 publications

References 48 publications

Event‐triggered output feedback H_∞ control for networked control systems

Event‐triggered output feedback H_∞ control for networked control systems

Exponential Synchronization of Neural Networks via Feedback Control in Complex Environment

Adaptive exponential lag synchronization for neural networks with mixed delays via intermittent control

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Outer synchronization of partially coupled dynamical networks via pinning impulsive controllers

Cited by 152 publications

References 48 publications

Event‐triggered output feedback H∞ control for networked control systems

Event‐triggered output feedback H∞ control for networked control systems

Exponential Synchronization of Neural Networks via Feedback Control in Complex Environment

Adaptive exponential lag synchronization for neural networks with mixed delays via intermittent control

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Product

Resources

About

Event‐triggered output feedback H_∞ control for networked control systems

Event‐triggered output feedback H_∞ control for networked control systems