Adaptive Synchronization of Time Delay Chaotic Systems with Uncertain and Unknown Parameters via Aperiodically Intermittent Control

Yuan-gan, Wang; Yang, Liping; Huang, Hong

doi:10.1007/s12555-019-0035-3

Cited by 16 publications

(9 citation statements)

References 38 publications

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“…In the literature, the subject of neuronal synchronization, using the FHN model, has been intensively examined as a potential application in cognitive engineering 1,20,28,47,57 . Researchers have developed adaptive 20,41 , nonlinear 28 , robust control 23 , neuralnetwork-, fuzzy 74 , and observer-based control schemes 63 to study the synchronization phenomenon in FHN www.nature.com/scientificreports/ neurons under external electrical stimulations. However, these conventional methodologies were developed for two or three coupled neurons and cannot guarantee synchronization of distant neurons if used for synchronizing the activity of networks of neurons because the mathematical models ignore the time delays arising from the separation between coupled neurons, and hence cannot synchronize distant FHN neurons.…”

Section: Discussionmentioning

confidence: 99%

“…Among these mathematical models, the FHN neuron model, developed by Fitzhugh 36 and Nagumo et al 37 , has been used as a primary tool for investigating neuronal synchronization problems 20 , 28 , 38 , 39 . Different schemes, such as the backstepping design method 40 , adaptive synchronization method 41 , linear and nonlinear feedback synchronization method 28 , sliding mode control method 20 , time-delay feedback approach 42 , and impulsive synchronization method 43 , have been proposed to achieve synchronization in chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

“…Jia et al investigated the influences and effects of time-delays on the dynamics of a coupled FHN neuronal system 58 . Wang et al studied the synchronization phenomenon in the time-delayed chaotic system with unknown and uncertain parameters and developed an intermittent adaptive control scheme to guarantee synchronization between neurons 41 . Ibrahim and Jung developed an adaptive control methodology to guarantee synchronization between a bidirectional ring-structured FHN neuronal network under the effect of external electrical stimulation with single- and dual-state gap junctions 1 .…”

Section: Introductionmentioning

confidence: 99%

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Lag synchronization of coupled time-delayed FitzHugh–Nagumo neural networks via feedback control

Ibrahim

Kamran

Mannan

et al. 2021

Sci Rep

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Synchronization plays a significant role in information transfer and decision-making by neurons and brain neural networks. The development of control strategies for synchronizing a network of chaotic neurons with time delays, different direction-dependent coupling (unidirectional and bidirectional), and noise, particularly under external disturbances, is an essential and very challenging task. Researchers have extensively studied the synchronization mechanism of two coupled time-delayed neurons with bidirectional coupling and without incorporating the effect of noise, but not for time-delayed neural networks. To overcome these limitations, this study investigates the synchronization problem in a network of coupled FitzHugh–Nagumo (FHN) neurons by incorporating time delays, different direction-dependent coupling (unidirectional and bidirectional), noise, and ionic and external disturbances in the mathematical models. More specifically, this study investigates the synchronization of time-delayed unidirectional and bidirectional ring-structured FHN neuronal systems with and without external noise. Different gap junctions and delay parameters are used to incorporate time-delay dynamics in both neuronal networks. We also investigate the influence of the time delays between connected neurons on synchronization conditions. Further, to ensure the synchronization of the time-delayed FHN neuronal networks, different adaptive control laws are proposed for both unidirectional and bidirectional neuronal networks. In addition, necessary and sufficient conditions to achieve synchronization are provided by employing the Lyapunov stability theory. The results of numerical simulations conducted for different-sized multiple networks of time-delayed FHN neurons verify the effectiveness of the proposed adaptive control schemes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lag synchronization of coupled time-delayed FitzHugh–Nagumo neural networks via feedback control

Ibrahim

Kamran

Mannan

et al. 2021

Sci Rep

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“…The state estimation error is defined as x x x    . From (10), ( 11), (13), and ( 14), its derivative is calculated by…”

Section: A Nonlinear Unknown Input Observermentioning

confidence: 99%

“…In detail, the hydraulic system always exists parametric uncertainties, external disturbance, and unmodeled nonlinearities as well as faults [1]- [4]. In order to improve the system performance, the disturbance is not only needfully suppressed but also compensated by assisted techniques such as extended state observer [5], [6], neural network (NN) approximators [7]- [10] fuzzy logic system (FLS) [11], time-delay estimation (TDE) [12], [13], etc. In a certain way, the disturbances or uncertainties can be considered as faults, which seriously affect system performance and safety [14].…”

Section: Introductionmentioning

confidence: 99%

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Phan

Dao

et al. 2021

IEEE Access

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In this paper, a novel adaptive fault-tolerant controller is proposed for a typical electrohydraulic rotary actuator in the presence of disturbances, internal leakage fault, and sensor fault simultaneously. To construct the suggested controller, a nonlinear unknown input observer is developed to effectively identify the sensor fault, which is unaffected by not only internal leakage fault but also mismatched disturbances/uncertainties. Furthermore, a radial basis function neural network is designed to compensate for the mismatched disturbances/uncertainties caused by payload variation and unknown friction nonlinearities. Besides, an adaptive law based on the projection mapping function is applied to tackle the effect of the internal leakage fault. The integration of the above-mentioned techniques into the adaptive backstepping terminal sliding mode is investigated to obtain high tracking performance, robustness as well as fast convergence. The stability of the closed-loop system is proven by the Lyapunov theory. Finally, the capability and effectiveness of the proposed approach are validated via simulation results under various faulty scenarios.

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Self-Evolving Interval Type-2 Fuzzy Neural Network Design for the Synchronization of Chaotic Systems

Lin

et al. 2022

Soft Computing for Data Analytics, Classification Model, and Control

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Adaptive Synchronization of Time Delay Chaotic Systems with Uncertain and Unknown Parameters via Aperiodically Intermittent Control

Cited by 16 publications

References 38 publications

Lag synchronization of coupled time-delayed FitzHugh–Nagumo neural networks via feedback control

Lag synchronization of coupled time-delayed FitzHugh–Nagumo neural networks via feedback control

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Self-Evolving Interval Type-2 Fuzzy Neural Network Design for the Synchronization of Chaotic Systems

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