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This research paper primarily focuses on the synchronization of a specific class of multi‐weighted coupled neural networks (MWCNNs) with switching topology, employing two adaptive control methods. In complex environments and under the influence of communication disturbances, the topology structures of coupled neural networks with multiple weights inevitably undergo time‐varying changes. To address this, we propose a novel type of MWCNNs with switching topologies. To ensure synchronization within this network, we develop sufficient conditions based on Lyapunov functional and inequality techniques. These conditions guarantee the achievement of synchronization. Moreover, we address the synchronization problem by employing node‐based and edge‐based adaptive controllers. Finally, we provide a numerical example to demonstrate the effectiveness of the obtained results. This example serves as empirical evidence showcasing the successful application of the proposed synchronization approach in practical scenarios. [ABSTRACT FROM AUTHOR]

Irregular and complex behavior in the financial system can disrupt stability and smooth economic growth. It causes randomness within the system, generating chaos; hindering synchronization behaviour. Achieving smooth and rapid synchronization between two coupled hyperchaotic finance (HF) systems with lessened fluctuation of input and output signals is vital for continuing financial stability and fostering economic growth, a challenge addressed in this article. The paper proposes a novel time-efficient nonlinear control (TENLC) technique and investigates HF systems synchronization using the drive-response system (DRS) arrangement. The proposed TENLC strategy realizes fast and smooth synchronization behaviour between two coupled HF systems, reducing closed-loop state-variable trajectory oscillations. The controller is designed to retain the nonlinear components within the closed-loop system and does not depend on the system's parameters, simplifying the design and analysis process. The Lyapunov stability technique confirms the closed-loop's global stability at the origin. Proofs of mathematical analysis and computer-based simulation results validate the theoretical findings, showing that the presented TENLC strategy converges the state error trajectories to zero in a short transient time with lessened fluctuations for all signals. The comparative computer-based simulation analysis confirms that the presented TENLC approach outperforms other synchronization control techniques. [ABSTRACT FROM AUTHOR]

Though extensively studied, the longstanding pinning synchronization problem of dynamical networks with coupling delay has not been well solved until now. In this paper, we further investigate this problem. By proposing a system of functional differential inequalities, we derive synchronization criteria for dynamical networks with coupling delay under linear pinning control, where the threshold of the admissible delay and the control gain threshold are estimated. Since the estimated control gain threshold could be very large when the delay draws close to the delay threshold, we also use the adaptive pinning control scheme to avoid control gain estimation. Pinning synchronization criteria of networks under adaptive control are derived and the delay threshold is given. This is the first time that general coupling delay has been addressed in the field of pinning control. Finally, two numerical examples are presented to validate the theoretical results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Based on Takagi-Sugeno(T-S) fuzzy models, this paper investigates practical finite-time(PFET) synchronization of complex networks with a linear coupling and two different kinds of nonlinear couplings, including nonlinear relative state coupling and nonlinear absolute state coupling. A new stability lemma is established based on different time intervals. Two kinds of controllers are designed including semi-intermittent state feedback control and semi-intermittent adaptive control. As a result, with the help of new stability lemma and control schemes, the goal of PFET synchronization is realized via Lyapunov functionals. Eventually, simulation experiments are presented to verify our new results. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive global terminal sliding‐mode control is developed for a nonlinear bilateral teleoperation system with time‐varying delays, external disturbances, and uncertainties. By selecting a time‐varying sliding‐mode manifold, a finite‐time sliding‐mode control algorithm is proposed to guarantee the suppression of the reaching phase associated with the conventional sliding‐mode control. This implies that the robustness is guaranteed throughout to entire response of the system. Meanwhile, the chattering phenomenon and the energy consumption are significantly reduced. Furthermore, in order to enhance the tracking performance in practical situations, adaptive tuning laws are developed to estimate the unknown upper bounds of the lumped uncertainties. Hence, the proposed approach has several merits, namely, fast response, accurate synchronization, and strong robustness with respect to large uncertainties and disturbances. The stability of the system in closed‐loop and the boundedness on the internal dynamics are proved by Lyapunov stability theory. Illustrative simulations were conducted to show the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Based on the framework of signed graphs, this paper investigates the finite‐time (FET) bipartite synchronization of Takagi‐Sugeno (T–S) fuzzy coupled neural networks with output coupling and state‐dependent parameters. Adaptive control schemes with quantizer are designed, and generalized norm is considered. Based on the framework of bipartite synchronization and FET synchronization, several sufficient conditions are established to realize the goal of synchronization by using Lyapunov functional method. Moreover, some numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

This research investigates the edge-based asymptotic synchronization of delayed complex dynamical networks with reaction–diffusions and by an edge-based adaptive pinning control technique. Sufficient conditions for reaction–diffusion networks to realize synchronization are provided by Green's formula, Wirtinger inequality, inequality analysis techniques, and contradiction methods. The results show that network synchronization can be achieved by pinning any edge of the network (the choice of edge is arbitrary), which greatly reduces the difficulty of control. Lastly, a series of numerical examples illustrating the theoretical findings is provided. [ABSTRACT FROM AUTHOR]

• A preassigned finite-time attitude synchronization controller is proposed for multiple rigid spacecraft systems by using two designed barrier Lyapunov functions. • The convergence time of the designed controller can be preassigned according to different space task requirements and it is independent of the initial condition of the system states and control parameters. • The steady-state error of the closed-loop system can be limited to a certain range by the appropriate choice of the controller parameters. This paper investigates the preassigned finite-time attitude synchronization control for the spacecraft formation with a dynamic virtual leader under an undirected connected graph. Each follower is described by a rigid spacecraft model subjects to parameter uncertainty, actuator saturation, and the disturbance. First, two fixed-time sliding mode estimators are introduced to estimate the reference attitude variable and the reference angular velocity, respectively. Then, by selecting the attitude errors as the system states, an attitude tracking error system for the spacecraft formation is established. An extended state observer is applied for computing the total disturbance in the attitude tracking error system. Based on the estimations of the extended state observer and two sliding mode estimators, a novel distributed controller is proposed to force the states of the established attitude tracking error system converge to a small region in preassigned finite time by using two modified time-varying barrier Lyapunov functions. The practical preassigned finite-time stability of the resulting closed-loop system is proven by Lyapunov stability theory. Finally, some simulations are provided to illustrate the effectiveness and advantages of the developed preassigned finite-time attitude synchronization control scheme. [ABSTRACT FROM AUTHOR]

In order to enhance the synchronization of welding robot arms and improve welding quality, this study proposes a fuzzy PID-based improved deviation coupling multi-axis synchronous control method. Firstly, in response to the intricacies inherent in the compensation mechanism of the deviation coupling control structure and the substantial volume of system computation, the integration of average speed and sub-average speed is proposed to optimize the speed compensator. This integration aims to mitigate speed synchronization errors, minimize synchronization adjustment time, and elevate overall system synchronization performance. Moreover, the fuzzy PID algorithm is employed to design the controller to realize the single-motor adaptive control, leading to improvement in both system stability and dynamic response performance. Finally, a simulation model for six-axis synchronization control and an experimental platform were developed. Both simulation and experimental results demonstrate that the improved deviation coupling control method exhibits superior synchronization performance. The proposed multi-axis synchronous control method effectively heightens the synchronous performance of the six-degrees-of-freedom robotic arm. [ABSTRACT FROM AUTHOR]

In this paper, the stochastic Takagi–Sugeno fuzzy recurrent neural networks (STSFRNNS) with distributed delay is established based on the Takagi–Sugeno (TS) model and the fixed time synchronization problem is investigated. In order to synchronize the networks, we design two kinds of controllers: a feedback controller and an adaptive controller. Then, we obtain the synchronization criteria in a fixed time by combining the Lyapunov method and the related inequality theory of the stochastic differential equation and calculate the stabilization time for the STSFRNNS. In addition, to verify the authenticity of the theoretical results, we use MATLABR2023A to carry out numerical simulation. [ABSTRACT FROM AUTHOR]

This article focuses on the synchronization problem of delayed chaotic neural networks via adaptive impulsive control. An adaptive impulsive gain law in a discrete-time framework is designed. The delay is handled skillfully by using the Lyapunov–Razumikhin method. To improve the flexibility of impulsive control, an event-triggered impulsive strategy to determine when the impulsive instant happens is designed. Additionally, it is proved that the event-triggered impulsive sequence cannot result in the occurrence of Zeno behavior. Some criteria are derived to guarantee synchronization for delayed chaotic neural networks. Eventually, an illustrative example is presented to empirically validate the effectiveness of the suggested strategy. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the fixed‐time anti‐synchronization and synchronization fault‐tolerant control problem is considered for two identical Liu‐Chen‐Liu chaotic systems with uncertain parameters. With the help of fixed‐time stability theory and adaptive backstepping method, we propose two novel adaptive controllers. In the control scheme proposed in this paper, the adaptive backstepping technique is used to deal with the unknown parameters contained in the chaotic systems. Besides, the piecewise functional method is used to solve the singularity problem in the controller design process. The designed controllers achieve anti‐synchronization and synchronization of two identical Liu‐Chen‐Liu chaotic systems within the preset time interval. Simulation results are conducted to show the effectiveness of two proposed adaptive controllers. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the adaptive synchronization of complex spatio‐temporal networks modeled by semi‐linear hyperbolic partial differential equations (CSTNSLHPDEs) as well as considering time‐invariant and time‐varying delays in a one‐dimensional space. Firstly, a distributed adaptive controller is proposed, where different nodes are with different adaptive gains. Secondly, four cases, CSTNSLHPDEs with time‐invariant delays and one single weight, with time‐invariant delays and multi‐weights, with time‐varying delays and one single weight, and with time‐varying delays and multi‐weights, are successively analyzed, and synchronization conditions of these four cases are obtained by using the proposed distributed adaptive controller. In the end, examples illustrate the effectiveness of the proposed distributed adaptive controller. [ABSTRACT FROM AUTHOR]

This research explores the synchronization issue of leader–follower systems with multiple nonlinear agents, which operate under input saturation constraints. Each follower operates under a spectrum of unknown dynamic nonlinear systems with non-strict feedback. Additionally, due to the fact that the agents may be geographically dispersed or have different communication capabilities, only a subset of followers has direct communication with the leader. Compared to linear systems, nonlinear systems can provide a more detailed description of real-world physical models. However, input saturation is present in most real systems, due to various factors such as limited system energy and the physical constraints of the actuators. An auxiliary system of Nth order is introduced to counteract the impact of input saturation, which is then employed to create a collaborative controller. Due to the powerful capability of fuzzy logic systems in simulating complex nonlinear relationships, they are deployed to approximate the enigmatic nonlinear functions intrinsic to the systems. A distributed adaptive fuzzy state feedback controller is designed by approximating the derivative of the virtual controller by filters. The proposed controller ensures the synchronization of all follower outputs with the leader output in the communication graph. It is shown that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around the origin. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A dual‐motor servo mechanism is a high‐order and strong‐coupling system with unknown nonlinearity, which brings challenges to controller design to realize high‐performance tracking and synchronization. This article proposes a finite‐time command filtered control strategy to address this problem. In tracking control design, finite‐time command filters are adopted to obtain the derivatives of virtual controllers, and the "explosion of complexity" problem in backstepping is thus solved. An improved compensation system is designed to reduce filtering errors. The tracking control is developed based on the combination of finite‐time control and command filtered approach. Moreover, to deal with unknown nonlinearities and uncertainties, fast finite‐time extended state observers are developed to observe lumped disturbances for the load and motor, respectively. In synchronization control design, two opposite control actions are developed using the speed difference to force two motors to rotate synchronously. The finite‐time convergence of the tracking and synchronization errors is proved. The efficiency of the proposed control strategy is verified via experiments conducted on a dual‐motor servo turntable. [ABSTRACT FROM AUTHOR]

This paper is concerned with the synchronization of stochastic uncertain complex dynamic networks with time‐varying delays. In contrast to existing synchronization network models, the current study considers both internal time‐varying delays and coupling time‐varying delays. By analyzing the two factors (i.e. time‐varying delays and uncertainties) on the synchronization performance of stochastic uncertain complex dynamic networks, a new inverse optimal adaptive control is proposed. The analysis tool developed in this paper is based on Lyapunov technique, algebraic graph theory and inverse optimality, which does not need to solve Hamilton‐Jacobi‐Bellman equation. Some sufficient conditions are established for the synchronization of stochastic uncertain complex dynamic networks with time‐varying delays. To demonstrate the effectiveness of the present approach, simulation results are illustrated. In this paper, we consider the synchronization of stochastic uncertain complex dynamic networks with time‐varying delays. Different from the existing synchronization of the networks model, we take the internal time‐varying delays and the coupling time‐varying delays into account. By analyzing the two factors (i.e. time‐varying delays and uncertainties) on the synchronization performance of stochastic uncertain complex dynamic networks, a novel inverse optimal adaptive control is proposed. The analysis tool developed in this paper is based on Lyapunov technique, algebraic graph theory and inverse optimality, which does not need to solve Hamilton‐Jacobi‐Bellman equation. Some sufficient conditions are established for the synchronization of stochastic uncertain complex dynamic networks with time‐varying delays. [ABSTRACT FROM AUTHOR]

The fully distributed output leader‐follower event‐triggered resilient control algorithm is proposed for heterogeneous multi‐agent systems (MASs) under communication link faults and constraints. An estimation of the information of the leader is achieved by designing a dynamic event‐triggered observer with resilience to link faults without using the eigenvalues of the global matrix. Zeno behavior is also excluded under the dynamic triggering mechanism. Considering that the state information of the follower is not available, a distributed output leader‐follower control law is further designed to achieve asymptotically convergence of the leader‐follower consensus error, and only the output information of followers is required. Finally, the availability of the theoretical results is shown in two numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to achieve finite-time synchronization (FTS) in a certain class of fractional-order complex-valued neural networks (CVNNs) containing reaction-diffusion terms. The proposed method uses intermittent control and provides a theoretical analysis to establish criteria for achieving FTS. This is achieved through new Lyapunov functions based on the proposed system, deriving inequalities in the complex domain. To realize FTS, the study designs complex-valued intermittent controllers for the targeted CVNNs relying solely on the information obtained from the controlled nodes. Moreover, an adaptive controller is introduced to effectively regulate the control gain, and the FTS of CVNNs is analyzed. The effectiveness of the proposed control strategies and derived results is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

The article investigated the stability of a two‐lane car‐following model with lateral friction on the basis of cluster synchronization theory of complex network. By using the Lyapunov stability theory and designing the appropriate controller, the two‐lane car‐following model with lateral friction is quickly stabilized and the stability condition of the model is obtained. Besides, based on the adaptive H∞$$ {H}_{\infty } $$ cluster synchronization theory for complex networks with external disturbances, the stability of two‐lane car‐following model with lateral friction is studied when the vehicles is subjected to random external disturbance. Finally, the numerical simulation is carried out by using Matlab simulation technology, the results show that the two‐lane car‐following model with lateral friction is rapidly stabilizing and congestion phenomenon is effectively alleviated under the controller designed. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time projective synchronization (FTPS) problem of fractionalorder quaternion-valued fuzzy memristor neural networks (FOQVFMNNs) is studied. Through establishing a feedback controller with signed functions and an adaptive controller, sufficient conditions for FTPS for FOQVFMNNs are obtained. Furthermore, the synchronization establishment time is calculated. Finally, the practicability of the conclusions is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

The authors of this paper used an adaptive-sliding mode controller (A-SMC) to synchronise two fractional-order hyperchaotic (FOHC) Lu systems in the presence of external disturbance and bounded parametric uncertainty with unknown bounds. They employed the Riemann–Liouville fractional order and defined a new fractional-order sliding surface for the FOHC Lu system to determine a proper active control. Additionally, they utilised adaptive laws to estimate uncertainty bounds and unknown disturbance signals. The authors proved the stability of the closed-loop control system using the Lyapunov theory. Simulation results in MATLAB demonstrated the desirable performance of this method in the presence of disturbance and parametric uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive terminal sliding-mode control (ATSMC) method is proposed for the synchronization of uncertain fractional-order chaotic systems with disturbances. According to the sliding-mode control theory, a non-singular sliding surface is constructed. To overcome the chattering problem of ATSMC, a smooth term is used in the controller. In order to reduce the estimation error of an uncertain parameter, adaptive laws are designed to adjust the amplitude of the continuous function. Based on the Lyapunov stability theory, a stability analysis of the error system is performed to ensure that the tracking error eventually converges to the origin. The effectiveness and applicability of the proposed control strategy are verified using the simulation results. [ABSTRACT FROM AUTHOR]

Asymptotic synchronization requires continuous external control of the system, which is unrealistic considering the cost of control. Adaptive control methods have strong robustness to uncertainties such as disturbances and unknowns. On the other hand, for finite-time synchronization, if the initial value of the system is unknown, the synchronization time of the finite-time synchronization cannot be estimated. This paper explores the finite-time adaptive synchronization (FTAS) and fixed-time synchronization (FDTS) of fractional-order memristive cellular neural networks (FMCNNs) with time-varying delays (TVD). Utilizing the properties and principles of fractional order, we introduce a novel lemma. Based on this lemma and various analysis techniques, we establish new criteria to guarantee FTAS and FDTS of FMCNNs with TVD through the implementation of a delay-dependent feedback controller and fractional-order adaptive controller. Additionally, we estimate the upper bound of the synchronization setting time. Finally, numerical simulations are conducted to confirm the validity of the finite-time and fixed-time stability theorems. [ABSTRACT FROM AUTHOR]

The asymptotic synchronization of quaternion-valued delayed neural networks with impulses and inertia is studied in this article. Firstly, a convergence result on piecewise differentiable functions is developed, which is a generalization of the Barbalat lemma and provides a powerful tool for the convergence analysis of discontinuous systems. To achieve synchronization, a constant gain-based control scheme and an adaptive gain-based control strategy are directly proposed for response quaternion-valued models. In the convergence analysis, a direct analysis method is developed to discuss the synchronization without using the separation technique or reduced-order transformation. In particular, some Lyapunov functionals, composed of the state variables and their derivatives, are directly constructed and some synchronization criteria represented by matrix inequalities are obtained based on quaternion theory. Some numerical results are shown to further confirm the theoretical analysis. [ABSTRACT FROM AUTHOR]

The issue of stochastic fixed-time synchronization (SFxS) between two nonlinear chaotic finance systems (CFSs) in noisy environment is addressed by this investigation. Two novel strategies, including the adaptive and fixed-time control, are integrated organically, which steers the CFSs to realize synchronization within a fixed-time, and meanwhile the controlling gains can be updated by the designed adaptive law. Particularly, the designed adaptive fixed-time controlling (AFC) scheme is differentiable, so that the chattering phenomenon can be alleviated and even eliminated effectively. Furthermore, by the stochastic Lyapunov stability analysis, the sufficient criterion is derived for realizing the SFxS of CFSs, and the upper-bound expectation of settling-time (UET) is estimated as well. At last, the numerical experiments are arranged to verify the correctness and feasibility of analytical results, the influence of noisy perturbation on the convergence rate is uncovered, and the interesting similar Dragon–King events are observed. [ABSTRACT FROM AUTHOR]

Summary: This express brief is concerned with the adaptive and optimal regulation control for fractional linear multi‐agent systems (MASs). Firstly, the adaptive control strategy is applied to address the regulation issue for fractional linear MASs with time varying edges. Under the designed adaptive control protocols with adaptive edge‐dependent synchronization gain, the regulation condition is achieved by constructing a Lyapunov functional with the edges. Secondly, the optimal control approach is used to solve the regulation issue for fractional linear MASs with constant edges. The optimization gain is chosen by minimizing an appropriate performance function. Moreover, the synchronization objectives are also achieved based on the proposed the regulation conditions. Finally, the correctness of the theoretical analysis and the feasibility of the designed controllers are verified by two numerical examples. [ABSTRACT FROM AUTHOR]

This article discusses the adaptive output synchronization problem of coupled fractional-order memristive reaction-diffusion neural networks (CFOMRDNNs) with multiple output couplings or multiple output derivative couplings. Firstly, by using Lyapunov functional and inequality techniques, an adaptive output synchronization criterion for CFOMRDNNs with multiple output couplings is proposed. Then, an adaptive controller is designed for ensuring the output synchronization of CFOMRDNNs with multiple output derivative couplings. Finally, two numerical examples are given to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Cargo transfer vessels (CTVs) are designed to transfer cargo from a floating production storage and offloading (FPSO) unit into conventional tankers. The dynamic positioning system allows the CTV to maintain a safe position relative to the FPSO unit using a flexible cargo transmission pipe, and the CTV tows the tanker during operating conditions. The operation mode can be considered a synchronization tracking control problem. In this paper, a synchronization control strategy is presented based on the virtual leader–follower configuration and an adaptive backstepping control method. The position and heading of the following vessel are proven to be able to globally exponentially converge to the virtual ship by the contraction theorem. Then, the optimization problem of the desired thrust command from the controller is solved through an improved firefly algorithm, which fully considers the physical characteristics of the azimuth thruster and the thrust forbidden zone caused by hydrodynamic interference. To validate the effectiveness of the presented synchronous following strategy and thrust allocation algorithm, a scale model experiment is carried out under a sea state of 4 in a seakeeping basin. The experimental results show that the CTV can effectively maintain a safe distance of 100 m with a maximum deviation of 3.78 m and an average deviation of only 0.99 m in the wave heading 180°, which effectively verifies that the control strategy proposed in this paper can achieve safe and cooperative operation between the CTV and the FPSO unit. To verify the advantages of the SAF algorithm in the thrust allocation, the SQP algorithm and PSO algorithm are used to compare the experimental results. The SAF algorithm outperforms the SQP and PSO algorithms in longitudinal and lateral forces, with the R-squared (R2) values of 0.9996 (yaw moment), 0.9878 (sway force), and 0.9596 (surge force) for the actual thrusts and control commands in the wave heading 180°. The experimental results can provide technical support to improve the safe operation of CTVs. [ABSTRACT FROM AUTHOR]

In this paper, by introducing memristive connections and inertial term into quaternion‐valued neural networks, the quaternion‐valued inertial memristive neural networks (QVIMNNs) is proposed. Based on the adaptive control strategy, the synchronization problem of the QVIMNNs is analyzed in this work. Two different assumption for activation functions are considered in this work; new criteria for global synchronization are then derived by designing novel adaptive controllers and update laws. Lastly, numerical examples are given to verify the results obtained in this work. [ABSTRACT FROM AUTHOR]

The synchronization of multi-weighted delayed coupled Cohen–Grossberg neural networks (CCGNNs) is considered in this paper. On the one hand, by designing a general controller and an aperiodically intermittent controller, respectively, several adequate conditions for reaching finite-time synchronization of the considered network are obtained, and the settling time is given. On the other hand, the pinning synchronization of the delayed CCGNNs with multiple weights is considered, and some corresponding criteria of pinning synchronization are derived by a general adaptive control scheme and an adaptive aperiodically intermittent control scheme, respectively. Finally, two illustrative examples including numerical simulations are presented to show the correctness of our results. [ABSTRACT FROM AUTHOR]

This paper investigates the synchronization problem for master‐slave stochastic memristive neural networks (SMNNs) with time‐delay via event‐triggered impulsive control (ETIC). Firstly, a novel ETIC method is designed for SMNNs to realize quasi‐synchronization in mean square sense. Different from time‐triggered impulsive control (TTIC) in the existing results on SMNNs, the proposed event‐triggered method determines impulsive instants based on the real‐time system states, which can reduce redundant impulses. Compared with the existing ETIC for deterministic memristive neural networks (MNNs), the proposed event‐triggered mechanism (ETM) can exclude Zeno behavior completely by adding the waiting time, when stochastic disturbances occur in MNNs. Secondly, if the master SMNNs have nonzero control inputs, an adaptive‐impulsive hybrid controller with ETM is designed to realize almost surely exponential synchronization. Under the ETM, the number of transmitted events is reduced, then control resources can be saved. Zeno behavior can be excluded in almost sure sense. Ultimately, simulations are presented to illustrate the validity of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, fixed‐time synchronization of nonlinear stochastic coupling multilayer neural networks is studied. The neural subnets in the multilayer networks are delay Cohen–Grossberg neural networks (DCGNNs). To overcome uncertain factors, we designed an adaptive delay‐dependent controller in synchronization. To describe constraints of communication and other related problems in networks, which are due to limitations for bit rates and bandwidths in communication channels, an adaptive fixed‐time control strategy is purposed by introducing quantization signal input. A theoretical framework about fixed‐time synchronization in multilayer delay Cohen–Grossberg neural networks (MDCGNNs) is established. We find that fixed settling time is related to the scale of MDCGNNs, characteristics of the designed controller parameters, and level of quantization. Finally, the effective of the theoretical framework is validated in an example. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for identifying unknown switching topology in a complex dynamical network. The setup is divided into two components: a primary drive network and a specialized response network equipped with switched topology observers. Each class of observers is dedicated to tracking a specific topology structure. The updating law for these observers is dynamically adjusted based on the operational status of the corresponding topology in the drive network—active if engaged and dormant if not. The sufficient conditions for successful identification are obtained by employing adaptive synchronization control and the Lyapunov function method. In particular, this paper abandons the generally used assumption of linear independence and adopts an easily verifiable condition for accurate identification. The result shows that the proposed identification method is applicable for any finite switching periods. By employing the chaotic Lü system and the Lorenz system as the local dynamics of the networks, numerical examples demonstrate the effectiveness of the proposed topology identification method. [ABSTRACT FROM AUTHOR]

In this paper, a synchronization controller for chaotic master-slave systems is presented based on the q-analogue of the Bernstein-Schurer-Stancu operators. q-analogue of the Bernstein-Schurer-Stancu operators is employed to approximate uncertainties due to their universal approximation property. The coefficients of polynomials are considered free parameters and will be adjusted by the adaptive rules extracted from the stability analysis. Additionally, the controller is designed based on the presumption that the synchronization error rate is unavailable. The controller is applied on a masterslave system using Duffing-Holmes oscillators. The results are compared with the Radial Basis Function Neural Networks (RBFNN). Simulation results and comparisons show that the Bernstein-Schurer-Stancu operator in q-analogue is efficient in uncertainty approximation; needless, the system states for constructing the regressor vector and can be a good alternative for neural networks. [ABSTRACT FROM AUTHOR]

In this article, we are interested in the problem of adaptive synchronization of a voltage source converter with a possibly weak grid with unknown angle and frequency. To guarantee a suitable synchronization with the angle of the three‐phase grid voltage we design an adaptive observer for such a signal requiring measurements only at the point of common coupling. Then we propose an alternative certainty‐equivalent, adaptive phase‐locked loop that ensures the angle estimation error goes to zero for almost all initial conditions. Although well‐known, for the sake of completeness, we also present a PI controller with feedforward action that ensures the converter currents converge to an arbitrary desired value. Relevance of the theoretical results and their robustness to variation of the grid parameters are thoroughly discussed and validated in the challenging scenario of a converter connected to a grid with low short‐circuit‐ratio. [ABSTRACT FROM AUTHOR]

In this paper, the global asymptotic stability and projective lag synchronization of second‐order competitive neural networks with mixed time‐varying delays and uncertainties are studied without converting the original system into the usual first‐order system. Firstly, according to the Lyapunov functional method, inequality technique and the designed adaptive control strategy, the algebraic criteria of stability, and projective lag synchronization are derived by adjusting the control gain parameters in the controller. The obtained sufficient conditions are simple and easy to verify. Different from the traditional feedback controller, the adaptive controller can adjust its characteristics according to the system model, and external disturbance makes the system have better control performance. Besides, unlike the existing ones, this stability and synchronization problem is directly analyzed by constructing some new Lyapunov functionals with state variables and state variable derivatives. Finally, the effectiveness and practicability of the results are verified by numerical examples. [ABSTRACT FROM AUTHOR]

The paper presents a grid-connected microgrid with a photovoltaic system and a battery as a storage element. The optimal design and control of storage elements and power quality improvement are enhanced using sigmoid-function-based variable step size (SFB-VSS) adaptive LMS control. The DC-link voltage and battery current are enhanced using an ILA-optimization-based PI controller. Comparative analysis shows that an ILA-optimized PI controller improves battery stress and DC-link voltage fluctuations, enhancing overall system stability. The relative percentage error of Vdc is only 0.5714% for ILA-optimized values as compared to GA, PSO, and manually tuned PI gains which are 0.857%, 1.14285%, and 0.86%, respectively. ILA-optimized parameters also enhance battery current, reducing stress on the battery. The system was studied under various dynamic conditions, achieving power balance in all conditions. The system has the capability of seamless transfer of control from GC mode to SA mode when the grid is disconnected. The proposed VSC control shows better performance in steady-state and dynamic conditions, maintaining a THD under 5%, which follows IEEE standard 519, and providing better DC offset rejection, fewer oscillations in the weight component of the load, and better convergence. The proposed control also enhances the frequency of the grid, ensuring a smooth transition between modes. The system is simulated in the MATLAB Simulink environment, and all the optimization techniques were carried out offline. [ABSTRACT FROM AUTHOR]

This paper is concerned with complete synchronization for discrete-time fractional-order fuzzy neural networks (DFFNNs) with time-varying delays. First, three original equalities and two Caputo σ -difference inequalities are established based on theory of discrete-time fractional Calculus. Next, a novel discrete-time adaptive controller with time-varying delay is designed, by virtue of 1-norm Lyapunov function and newly established lemmas herein as well as inequality techniques and contradiction method, some judgement conditions are derived to guarantee complete synchronization for the explored DFFNNs. Benefitting from discrete-time adaptive control strategy and our analysis method, the conservatism of the derived synchronization criteria is reduced. Ultimately, the effectiveness of our theoretical results and secure communication scheme are demonstrated through two numerical examples. • We establish three original equalities, which enrich theory of discrete-time fractional Calculus. • Two Caputo σ -difference inequalities are introduced and proved herein. The first one is b c ∇ σ μ (f (t) − ϱ) 2 ⩽ 2 (f (t) − ϱ) b c ∇ σ μ f (t) , which can be applied to adaptive control-based asymptotic synchronization of nonlinear systems depicted by Caputo σ -difference equations. The second inequality is b c ∇ σ μ | f (t) | ⩽ s i g n (f (t)) b c ∇ σ μ f (t) , which plays a central role in building the relationship between 1-norm Lyapunov function and Caputo σ -difference of state variable. • A universal theoretical framework analyzing adaptive control-based complete synchronization for DFNNs is developed for the first time. • Our results are more general, which can be extended to DFNNs without fuzzy logic or DFNNs without delay. • A secure communication scheme for DFFNNTDs is proposed based on complete synchronization method. [ABSTRACT FROM AUTHOR]

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The cluster synchronization was investigated by adaptive pinning control for coupled neural networks (CNNs) with stochastic disturbances. In CNNs, nodes can only synchronize with the corresponding isolated nodes in the same cluster, and the synchronization status of nodes is different in different clusters. Then, adaptive pinning controllers are set on each cluster to realize cluster synchronization, which can select appropriate pinning nodes and eliminate the unknown feedback gain. In addition, for nonlinear functions in CNNs, an NCF (acceptable nonlinear coupling function class) method is used to approximate nonlinear functions to linear functions. On account of the adaptive pinning control strategy, the sufficient condition of mean-square exponential stability for CNNs is given by using M-matrix technique. Finally, the effectiveness of cluster synchronization under the adaptive pinning control are verified by two simulations. [ABSTRACT FROM AUTHOR]

In this work, we consider an adaptive control method, which is simpler and generalized to obtain some conditions on the parameters for hyperchaotic models determined by using a Lyapunov direct method. Further, an adaptive controller for synchronization is designed by using Lyapunov functions by which the deriving system and the response system can realize adaptive modified function projective synchronization up to scaling matrix. Numerical simulation of each system is discussed in detail with graphical results. The graphical results are presented in detail in order to validate the theoretical results. These results in this article generalize and improve the corresponding results of the recent works. [ABSTRACT FROM AUTHOR]

Over the past years, many chaos-based secure communication system algorithms have been published for the encryption of different types of data. The problem with these algorithms is either the high complexity that makes them difficult to be implemented or the lack of the high security that prevents them from being applicable in real applications. In this paper, we propose a new highly-secured chaos-based communication system that is based on adaptive control synchronization. The implementation process of the proposed system is detailed including the oscillator dynamic equations, the control laws derivation, the numerical solutions, and the Matlab/Simulink representation. To show the efficiency of the proposed system, its security is analyzed using different security measures. This proofs that the carrier signal is unpredictable by the intruder in both time and frequency domains. The experimental results and the show that the proposed system outperforms the different related and recent work in addition to its stability and robustness to the variation of initial conditions. Finally, we introduce an application on voice encryption to demonstrate the effectiveness of our system. Then We compare the security results of our system with different implementations of recent and related work. [ABSTRACT FROM AUTHOR]

This paper aims to address finite-time cluster synchronization (FTCS) issues for fractional-order fully complex-valued dynamical networks (FFCVDNs) with time delay. To compensate for the limited application of one controller, the delay-dependent and delay-independent adaptive controllers with regard to quadratic and absolute-valued norms are developed, respectively. Based on the finite-time stability theorem and auxiliary inequality techniques, detailed Lyapunov analysis is provided to ensure that FFCVDNs can achieve FTCS, and the settling times (STs) are estimated on the basis of system and control parameters characterized by system models to decrease the conservativeness of the existing results. Finally, simulation examples are provided to verify the correctness of theoretical analysis. [ABSTRACT FROM AUTHOR]

This work aims to achieve cluster synchronization of a complex network by some pinning control strategies. Firstly, the network not only is affected by the reaction–diffusion and the directed coupling phenomena, but also is disturbed by the stochastic noise and Markovian switching. Secondly, switched constant gain pinning, centralized and decentralized adaptive pinning are proposed respectively to realize the cluster synchronization of the considered network. In these adaptive pinning controllers, the control gain and coupling strength can been adjusted automatically while only a part of the nodes are controlled. Thirdly, the target state of cluster synchronization is taken as the average state related to the directed topology of all nodes in the same cluster, and does not need to be given separately as an isolated node. Finally, to verify the theoretical results, some simulations of directed coupled reaction–diffusion neural networks with stochastic noise and Markovian switching are given. [ABSTRACT FROM AUTHOR]

Summary: In this article, the global and exponential synchronization of Lur'e networks with hybrid time‐varying delays is investigated. For sake of saving control costs, a novel impulsive adaptive controller is presented, where the adaptive feedback control input is utilized to compensate the control effects of the impulsive signal. Meanwhile, suitable control gains are obtained according to the designing on adaptive updating laws. In view of the definition of average impulsive interval, the generalized comparison principle and the formula of parameter variation, sufficient conditions for achieving global and exponential synchronization of Lur'e dynamical networks are eventually derived. Two relevant numerical simulations are provided to illustrate the effectiveness of theoretical analysis and the control scheme. [ABSTRACT FROM AUTHOR]

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