Synchronization for stochastic semi-Markov jump neural networks with dynamic event-triggered scheme

Cao, Dianguo; Jin, Yujing; Qi, Wenhai

doi:10.1016/j.jfranklin.2021.07.058

Cited by 9 publications

(7 citation statements)

References 53 publications

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“…If compared to traditional time-triggered communication, an important feature of the event triggering approach is its potential to reduce redundant transmissions while maintaining assured system performance. In recent years, event-triggered strategies for stochastic models have received increased attention, with numerous notable results reported in the literature [30][31][32][33]. It should be noted, however, that many proven conclusions pertaining to event-triggering techniques are in the framework of continuous-time and discrete-time systems [34].…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Vadivel

Hammachukiattikul

Zhu

et al. 2022

Asian Journal of Control

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This article investigates the event-triggered synchronization problem of stochastic neural networks under passivity and passification cases. For saving communication resources, an event-triggered approach is engaged in the design of synchronization for the delayed stochastic neural networks. To decrease network trouble, an event-triggered scheme is suggested between the sampler and communication network. A nonfragile event-triggered controller is intended to guarantee the finite-time stability of the subsequent closed-loop system. By applying the Lyapunov-Krasovkii functional (LKF) and the novel integral inequalities, a stability criteria for an interval-time varying delay error system ensure the designed controller can fulfill the necessities of passivity and passification performance. The desired control gain and event-triggered parameters are then found based on the linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the benefits and validity of the desired control law.

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Section: Introductionmentioning

confidence: 99%

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Vadivel

Hammachukiattikul

Zhu

et al. 2022

Asian Journal of Control

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“…e inputs drive the operation of the system, and the outputs are the effects observed on the system. A parallel input/ output is established between the neural model and the physical system [12][13][14]28]. e ANN is given as follows:…”

Section: Artificial Neural Network Designmentioning

confidence: 99%

“…One neural network was applied to reconstruct the approximations of unknown nonlinearities [26,27]. An adaptive neural network was designed, by combining its general online estimation ability with an adaptive backstepping design framework, and the probabilistic asymptotic tracking control was realized [28].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Feed-Forward and Feedback Control Design and Convergence Analysis

Chen

Liu

2022

Mathematical Problems in Engineering

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A feed-forward and feedback control scheme based on artificial neural network (ANN) and iterative learning control is proposed. Iterative learning control and ANN are combined as a feed-forward controller, which makes the output track the desired trajectory. Feedback control is introduced to reduce the effect of disturbances. To combine the feed-forward controller and the feedback controller, the ANN is employed to simulate the plant. Since the ANN can update the weights online, it is always consistent with the plant. The convergence and robustness of the system are analyzed, and the simulation shows the feasibility of the proposed control scheme.

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“…So far, many remarkable achievements have been made in the design of ETC strategies. [11][12][13][14] Readers are recommended to read the following review papers [15][16][17] for more details.…”

Section: Introductionmentioning

confidence: 99%

“…For ETC strategies, signal transmission is conducted only when some user‐defined conditions are met. So far, many remarkable achievements have been made in the design of ETC strategies 11‐14 . Readers are recommended to read the following review papers 15‐17 for more details.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered controller design from the control input perspective

Xing

Lin

Wang

et al. 2022

Intl J Robust & Nonlinear

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In this article, we propose a novel event‐triggered control (ETC) method from the control input perspective. Different from existing results in which event conditions are designed based on the variation of system states, this method designs event conditions from the control input perspective, that is, based on the control input variations. Through this method, it is shown that for nonlinear systems, the commonly used input‐to‐state stable assumption can be removed. For linear systems, this method is shown equivalent to conventional results established based on state signals. Moreover, it provides flexibility in designing different event conditions for uncertain nonlinear systems. The effectiveness of the proposed method is demonstrated through two simulation examples.

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Synchronization for stochastic semi-Markov jump neural networks with dynamic event-triggered scheme

Cited by 9 publications

References 53 publications

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Artificial Neural Network-Based Feed-Forward and Feedback Control Design and Convergence Analysis

Event‐triggered controller design from the control input perspective

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