Synchronization of Complex Dynamical Networks on Time Scales via Pinning Control

Fang-di, Kong; Sun, Jian-Ping

doi:10.1155/2021/5544063

Cited by 4 publications

(2 citation statements)

References 45 publications

(52 reference statements)

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“…In [24], through the pinning part of the network nodes with simple aperiodic intermittent controllers, the addressed dynamical networks can achieve global quasi-synchronization. In [25], the pinning synchronization strategy, which combines main characteristics of time scales and main parameters of the pinningcontrolled network, is established. In [26] and [27], the pinning control is introduced into the MGs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Secondary Control Strategy Based on <italic>Q</italic>-learning and Pinning Control for Droop-controlled Microgrids

Liu

Shen

Zhang

et al. 2022

Journal of Modern Power Systems and Clean Energy

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A distributed secondary control (DSC) strategy that combines Q-learning and pinning control is originally proposed to achieve a fully optimal DSC for droop-controlled microgrids (MGs). It takes advantages of cross-fusion of the two algorithms to realize the high efficiency and self-adaptive control in MGs. It has the following advantages. Firstly, it adopts the advantages of reinforcement learning in autonomous learning control and intelligent decision-making, driving the action value of pinning control for feedback adaptive correction. Secondly, only a small part of points selected as pinned points needs to be controlled and pre-learned, hence the actual control problem is transformed into a synchronous tracking problem and the installation number of controllers is further reduced. Thirdly, the pinning matrix can be modified to adapt to plugand-play operation under the distributed control architecture. Finally, the effectiveness and versatility of the proposed strategy are demonstrated with a typical droop-controlled MG model.

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Section: Introductionmentioning

confidence: 99%

Distributed Secondary Control Strategy Based on <italic>Q</italic>-learning and Pinning Control for Droop-controlled Microgrids

Liu

Shen

Zhang

et al. 2022

Journal of Modern Power Systems and Clean Energy

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“…Among all kinds of networks, biological neural network is a persistent and hot topic. In the past decade, a lot of studies have been devoted to the synchronization of all kinds of neural networks under different conditions (Li et al 2003 ; Cao and Lu 2006 ; Yu et al 2009 ; Ehrich et al 2013 ; Fan et al 2018 ; Kong and Sun 2021 ; Wouapi et al 2020 ) because of studies have shown that Parkinson’s, epilepsy and other brain deficits are caused by the damage to the ability of neurons to synchronize with their neighbors. As shown in Njitacke et al ( 2021 ), the synchronization of neurons under different structures and parameters were conducted according to the state estimation error system and the control strategy.…”

Section: Introductionmentioning

confidence: 99%

Topology identification and dynamical pattern recognition for Hindmarsh–Rose neuron model via deterministic learning

Chen

Zeng

et al. 2022

Cogn Neurodyn

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Studies have shown that Parkinson’s, epilepsy and other brain deficits are closely related to the ability of neurons to synchronize with their neighbors. Therefore, the neurobiological mechanism and synchronization behavior of neurons has attracted much attention in recent years. In this contribution, it is numerically investigated the complex nonlinear behaviour of the Hindmarsh–Rose neuron system through the time responses, system bifurcation diagram and Lyapunov exponent under different system parameters. The system presents different and complex dynamic behaviors with the variation of parameter. Then, the identification of the nonlinear dynamics and topologies of the Hindmarsh–Rose neural networks under unknown dynamical environment is discussed. By using the deterministic learning algorithm, the unknown dynamics and topologies of the Hindmarsh–Rose system are locally accurately identified. Additionally, the identified system dynamics can be stored and represented in the form of constant neural networks due to the convergence of system parameters. Finally, based on the time-invariant representation of system dynamics, a fast dynamical pattern recognition method via system synchronization is constructed. The achievements of this work will provide more incentives and possibilities for biological experiments and medical treatment as well as other related clinical researches, such as the quantifying and explaining of neurobiological mechanism, early diagnosis, classification and control (treatment) of neurologic diseases, such as Parkinson’s and epilepsy. Simulations are included to verify the effectiveness of the proposed method.

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Model Following Adaptive Control for a Class of Complex Dynamic Networks Based on the Dynamics of Links

Wang²,

Liu³

2022

Int. J. Control Autom. Syst.

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Synchronization of Complex Dynamical Networks on Time Scales via Pinning Control

Cited by 4 publications

References 45 publications

Distributed Secondary Control Strategy Based on <italic>Q</italic>-learning and Pinning Control for Droop-controlled Microgrids

Distributed Secondary Control Strategy Based on <italic>Q</italic>-learning and Pinning Control for Droop-controlled Microgrids

Topology identification and dynamical pattern recognition for Hindmarsh–Rose neuron model via deterministic learning

Model Following Adaptive Control for a Class of Complex Dynamic Networks Based on the Dynamics of Links

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