Fixed-Time Synchronization Control of Delayed Dynamical Complex Networks

Liu, Mei; Lu, Binglong; Li, Zhanfeng; Jiang, Haijun; Hu, Cheng

doi:10.3390/e23121610

Cited by 5 publications

(6 citation statements)

References 35 publications

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“…It remains an active research domain in nonlinear complex network dynamics theory. [3][4][5] In 1988, Chua and Yang introduced cellular neural networks (CNNs), 6 which were based on cells communicating directly with their nearest neighbors. Subsequently, shunting inhibitory cellular neural networks (SICNNs) were introduced by Bouzerdoum and Pinter in 1993.…”

Section: Introductionmentioning

confidence: 99%

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Robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks with feedback and adaptive control

Liu,

Pu,

Hua

et al. 2024

Adaptive Control & Signal

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This article addresses the robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks (FSICNNs) with delays by utilizing two different types of control strategies. First, a feedback controller is proposed to achieve fixed‐time synchronization of FSICNNs. Secondly, a novel adaptive controller is designed to guarantee fixed‐time synchronization of FSICNNs, automatically adjusting all control gains without the need for advanced settings. The use of differential inequality techniques and the Lyapunov function method yields several sufficient conditions to ensure fixed‐time synchronization for the considered FSICNNs. Finally, an example, along with its numerical simulation, is presented to demonstrate the validity of the proposed theoretical results.

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Section: Introductionmentioning

confidence: 99%

“…It has experienced prosperous development and extensive applications in various fields such as biology, physics, information processing, engineering techniques, and secure communication. It remains an active research domain in nonlinear complex network dynamics theory 3–5 …”

Section: Introductionmentioning

confidence: 99%

Robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks with feedback and adaptive control

Liu,

Pu,

Hua

et al. 2024

Adaptive Control & Signal

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“…Despite dynamic surface control methods [ 7 , 8 , 9 ], that can reduce computing efforts, various nonlinear factors such as time delays, external disturbances, and physical constraints are ubiquitous in real industrial scenarios [ 10 , 11 ], which may diminish the controlling precision of the PMSM systems. Researchers have proposed proportional integral derivative (PID) control [ 12 ], neural network (NN) [ 5 ], time delay control [ 13 , 14 ], disturbance observer (DO) [ 15 , 16 ], and constraint control [ 17 , 18 ] methods for different nonlinearities to reach satisfying control results. Hence, the key point is how to design an effective controller to address the various nonlinear uncertainties such as unknown functions, mismatched disturbance, state constraints, and time delays.…”

Section: Introductionmentioning

confidence: 99%

Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays

Zhang

et al. 2022

Entropy

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This paper suggests an adaptive funnel dynamic surface control method with a disturbance observer for the permanent magnet synchronous motor with time delays. An improved prescribed performance function is integrated with a modified funnel variable at the beginning of the controller design to coordinate the permanent magnet synchronous motor with the output constrained into an unconstrained one, which has a faster convergence rate than ordinary barrier Lyapunov functions. Then, the specific controller is devised by the dynamic surface control technique with first-order filters to the unconstrained system. Therein, a disturbance-observer and the radial basis function neural networks are introduced to estimate unmatched disturbances and multiple unknown nonlinearities, respectively. Several Lyapunov-Krasovskii functionals are constructed to make up for time delays, enhancing control performance. The first-order filters are implemented to overcome the “complexity explosion” caused by general backstepping methods. Additionally, the boundedness and binding ranges of all the signals are ensured through the detailed stability analysis. Ultimately, simulation results and comparison experiments confirm the superiority of the controller designed in this paper.

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“…The synchronization control strategy for complex networks has received significant attention [33][34][35]. In view of complex interconnection and huge network scale, it is tough to achieve the desired synchronized state through controlling all network nodes in practice applications.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Pinning Synchronization Control for T-S Fuzzy Discrete Complex Networks with Time-Varying Delays via Adaptive Event-Triggered Approach

Zhang

et al. 2022

Entropy

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This paper is concerned with the adaptive event-triggered finite-time pinning synchronization control problem for T-S fuzzy discrete complex networks (TSFDCNs) with time-varying delays. In order to accurately describe discrete dynamical behaviors, we build a general model of discrete complex networks via T-S fuzzy rules, which extends a continuous-time model in existing results. Based on an adaptive threshold and measurement errors, a discrete adaptive event-triggered approach (AETA) is introduced to govern signal transmission. With the hope of improving the resource utilization and reducing the update frequency, an event-based fuzzy pinning feedback control strategy is designed to control a small fraction of network nodes. Furthermore, by new Lyapunov–Krasovskii functionals and the finite-time analysis method, sufficient criteria are provided to guarantee the finite-time bounded stability of the closed-loop error system. Under an optimization condition and linear matrix inequality (LMI) constraints, the desired controller parameters with respect to minimum finite time are derived. Finally, several numerical examples are conducted to show the effectiveness of obtained theoretical results. For the same system, the average triggering rate of AETA is significantly lower than existing event-triggered mechanisms and the convergence rate of synchronization errors is also superior to other control strategies.

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Fixed-Time Synchronization Control of Delayed Dynamical Complex Networks

Cited by 5 publications

References 35 publications

Robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks with feedback and adaptive control

Robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks with feedback and adaptive control

Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays

Finite-Time Pinning Synchronization Control for T-S Fuzzy Discrete Complex Networks with Time-Varying Delays via Adaptive Event-Triggered Approach

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