Event‐triggered finite‐time quantized synchronization of uncertain delayed neural networks

Zhang, Yingqi; Li, Xiao; Liu, Caixia

doi:10.1002/oca.2915

Cited by 6 publications

(4 citation statements)

References 35 publications

(68 reference statements)

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“…According to the quantization error characteristics of the uniform quantizer in (19), the absolute quantization error of the paralleled system can be obtained by combining with (22) as follow…”

Section: Stability Analysis Of the Uniform Quantization Systemmentioning

confidence: 99%

“…where O(h 3 ) is the third-order infinitesimal of h. Similarly, the quantized LSMC surface in (22) can also be discretized by ZOH as…”

Section: A Discrete Design Of the Uniform Quantization Systemmentioning

confidence: 99%

“…However, the method designed in the literature has high requirements for the controller design and the trigger strategy, and the discretization effect of the system is not considered for applications of digital chip control. In the literature [22], a novel finite-time synchronization design is developed for uncertain delayed neural networks with event-triggered mechanisms and input quantizations. However, the method proposed in this literature requires high computational resources, and the quantization processing mechanism of the system is also overly dependent on event triggering.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sliding Mode Control for Parallel DC–DC Converter Network Systems With Uniform Quantization and Discretization Effects

Wei,

Zhang,

Wang

2023

IEEE Access

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In this paper, a uniform quantization design method for the parallel DC-DC converter network control system (NCS) is proposed in combination with sliding mode control (SMC) strategy to solve the problem that the performance of the system is poor due to the inability to process long signals in time. Taking the parallel buck converter as an example, firstly, the master-slave current sharing SM controller of the parallel buck converter is designed. Then, the NCS containing the designed SM controller is uniformly quantized in the continuous domain, and the stability performance of the system is analyzed according to the quantization error. Considering the application of the parallel buck converter in the actual digital control system, the discretization effect of the uniform quantization SMC system is studied by using a zero-order hold (ZOH) and the stability conditions of the discrete quantization system are given. Finally, the state trajectories and output performances of the system under different parameters in the continuous domain and discrete domain are analyzed by simulations, and the correctness and reliability of the uniform quantization SMC system designed in this paper are further verified by experiments.INDEX TERMS parallel DC/DC converters, sliding mode control, network signal transmission, uniform quantizer, discretization effect.

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Section: Stability Analysis Of the Uniform Quantization Systemmentioning

confidence: 99%

“…where O(h 3 ) is the third-order infinitesimal of h. Similarly, the quantized LSMC surface in (22) can also be discretized by ZOH as…”

Section: A Discrete Design Of the Uniform Quantization Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sliding Mode Control for Parallel DC–DC Converter Network Systems With Uniform Quantization and Discretization Effects

Wei,

Zhang,

Wang

2023

IEEE Access

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“…Since NNs have such complicated properties, they may be utilized in a wide variety of contexts, with the specific applications being determined by the dynamic properties of the linked networks, notably in terms of synchronization [17]. At present, the synchronization of QMJTDNNs is still a challenging research field, and the related research is scarce [24,25]. Furthermore, the synchronization of NNs has been recognized as a key research topic due to the complex and dynamic behavior of coupled nodes.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Saravanan¹,

Vadivel

Gunasekaran

2023

Mathematics

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In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

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Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties

Karnan,

Nagamani

2023

Chaos, Solitons & Fractals

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Event‐triggered finite‐time quantized synchronization of uncertain delayed neural networks

Cited by 6 publications

References 35 publications

Sliding Mode Control for Parallel DC–DC Converter Network Systems With Uniform Quantization and Discretization Effects

Sliding Mode Control for Parallel DC–DC Converter Network Systems With Uniform Quantization and Discretization Effects

Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties

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