Synchronization of a delayed complex dynamical network with free coupling matrix

Lee, Tae Hee; Park, Ju H.; Jung, Ho-Youl; Lee, S. M.; Kwon, O’Dae

doi:10.1007/s11071-012-0328-z

Cited by 46 publications

(20 citation statements)

References 25 publications

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“…By the Schur complement, it follows from (38) that i = ⎡ ⎣ (11) i (12) i¯ (13) i * (22) i¯ (23) i * * ¯ (33)…”

Section: Theorem 2 Given Scalarsmentioning

confidence: 99%

“…Now, we will present a solution to ensure the non-fragile finite-time l 2 − l ∞ state estimator design problem for the augmented estimation error system (˜ ) based on Theorem 2. MSFTB with respect to (a 1 , a 2 , N, R, W) and satisfies a prescribed (11) i˜ (12) i˜ (13) i˜ (14) i * ˜ (22) i˜ (23) i˜ (24) …”

Section: Theorem 2 Given Scalarsmentioning

confidence: 99%

“…Under such an assumption, the measurement output collected by the sensor from plant (y s (k)) is equal to the expected measurement output of the plant (y(k) ), that is, y s (k) ≡ y(k). It is indeed convenient for the design of state estimator, however, it is often proved to be impossible in many situations, partly due to the fact that the networked-induced phenomena naturally exist in network-based control systems and partly due to the fact that many feedback device nonlinearities, such as sensor and actuator nonlinearity, arise frequently in feedback control systems [11,24,31]. Based on this consideration, the assumption was attempted to be removed in many recent studies, e.g., [24,30], where the time delays in communication channels were ignored.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non-fragile finite-timel2−l∞state estimation for discrete-time Markov jump neural networks with unreliable communication links

Shen

Chen

et al. 2015

Applied Mathematics and Computation

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“…By the Schur complement, it follows from (38) that i = ⎡ ⎣ (11) i (12) i¯ (13) i * (22) i¯ (23) i * * ¯ (33)…”

Section: Theorem 2 Given Scalarsmentioning

confidence: 99%

Section: Theorem 2 Given Scalarsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-fragile finite-timel2−l∞state estimation for discrete-time Markov jump neural networks with unreliable communication links

Shen

Chen

et al. 2015

Applied Mathematics and Computation

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“…In addition, the adaptive pinning control method, which is utilized to get the appropriate control gains effectively, has received considerable research attention. An adaptive pinning control method is proposed in [15] to synchronize for a delayed complex dynamical network with free coupling matrix. Besides these, there are many literatures to study adaptive pinning control problems of networks [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Pinning Adaptive Synchronization of Delayed Coupled Dynamical Networks via Periodically Intermittent Control

Liu

2017

Mathematical Problems in Engineering

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This paper investigates the exponential synchronization problem of delayed coupled dynamical networks by using adaptive pinning periodically intermittent control. Based on the Lyapunov method, by designing adaptive feedback controller, some sufficient conditions are presented to ensure the exponential synchronization of coupled dynamical networks with delayed coupling. Furthermore, a numerical example is given to demonstrate the validity of the theoretical results.

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“…The problem of master-slave synchronization for chaotic systems has arisen a great attention since the master-slave concept has been proposed for achieving the synchronization of coupled chaotic systems in [1][2][3][4]. This stems from its potential applications in secure communication, image processing, biological systems, chemical reaction, and information science (see e.g., [5][6][7]).…”

Section: Introductionmentioning

confidence: 99%

Further results on sampled-data control for master–slave synchronization of chaotic Lur’e systems with time delay

et al. 2015

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This paper is concerned with the problem of sampled-data control for master-slave synchronization of chaotic Lur'e systems with time delay. The sampling periods are assumed to be arbitrary but bounded. A new Lyapunov functional is constructed, in which the information on the nonlinear function and the actual sampling pattern have been taken fully into account. By employing the Lyapunov functional and a tighter bound technique to estimate the derivative of the Lyapunov functional, a less conservative exponential synchronization criterion is established by analyzing the corresponding synchronization error systems. Furthermore, the derived condition is employed to design a sampled-data controller. The desired controller gain matrix can be obtained by means of the linear matrix inequality approach. Simulations are provided to show the effectiveness and the advantages of the proposed approach.

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Synchronization of a delayed complex dynamical network with free coupling matrix

Cited by 46 publications

References 25 publications

Non-fragile finite-timel2−l∞state estimation for discrete-time Markov jump neural networks with unreliable communication links

Non-fragile finite-timel2−l∞state estimation for discrete-time Markov jump neural networks with unreliable communication links

Pinning Adaptive Synchronization of Delayed Coupled Dynamical Networks via Periodically Intermittent Control

Further results on sampled-data control for master–slave synchronization of chaotic Lur’e systems with time delay

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