This article proposes a novel adaptive predefined-time control scheme that guarantees global prescribed performance and exact tracking for a class of uncertain nonlinear systems characterized by nonstrict feedback dynamics, actuator faults, external disturbances, and sensor faults. Anovel switching feedback gain method is introduced to simultaneously handle unknown nonlinearities, disturbances, and actuator faults, while a tan-type error transformation and a new Lyapunov-like energy function are designed to ensure that all signals in the closed-loop systems remain globally bounded without requiring knowledge of control coefficients. In contrast to existing finite-time or asymptotic tracking control strategies, the proposed method ensures exact convergence to zero tracking error within a predefined time—independent of initial conditions—while strictly maintaining prescribed transient performance bounds. Simulation results validate the effectiveness of the proposed approach and its superiority over conventional methods, demonstrating faster convergence and smaller overshoot, thereby establishing a new benchmark for high-precision control of nonstrict feedback systems.

This paper is concerned with the mean-square synchronization issue of the coupled neural networks (CNNs) subject to deception attacks, where the saturation constraint on impulsive control signal is adequately taken into account. To compensate the effect of deception attacks, a hybrid event-triggered mechanism is developed and a minimum inter-event interval is introduced simultaneously to avoid the Zeno phenomena. Under deception attacks, sufficient conditions to guarantee the achievement of the mean-square exponentially synchronization for the CNNs are presented with a novel method combined with mathematical induction, polyhedral representation of saturation nonlinearity, proof by contradiction, which address the actuator saturation in discrete-time control signals effectively. Meanwhile, in consideration of different impulsive effects, three optimization problems are constructed for the sake of acquiring the maximum estimation of the domain of attraction, the feasible maximal impulsive interval and the admissible feasible minimum impulsive interval, respectively. Finally, two numerical simulations are carried out to illustrate the validity of the proposed theoretical analysis. Note to Practitioners—In the engineering and industry, the synchronization control of CNNs exists in many different fields, including image processing, fluid dynamics, and secure communication. The control signals and plant information are transmitted over communication network which is vulnerable to deception attacks. To save communication resources and compensate the effect of deception attacks, a hybrid event-triggered scheme is proposed. Simultaneously, the synthesis of impulse effects and actuator saturation are considered to make results more practical. In addition, three optimization problems are proposed to derive the maximum estimation of the domain of attraction, some related parameters selection of event-triggered scheme.

This paper studies the exponential synchronization issue of complex dynamic networks (CDNs) under dual-channel attacks by utilizing distributed delayed pinning impulsive control strategy. A dual-channel attack model with differentiated node attack probabilities is established by simultaneously considering replacement attacks in the sensor-to-controller channels and injection attacks in the controller-to-actuator channels. Within this approach, The novel concept of average delayed impulsive gains has been introduced to holistically measure the combined temporal influence of impulsive delays and gains across time. Furthermore, by use of the formula for the variation of parameters and applying comparison principle to hybrid delayed impulses, sufficient conditions for achieving the exponential synchronization of the networks are ultimately derived. Finally, numerical simulations verify the effectiveness and superiority of the proposed control strategy.

This article proposes a dynamic self-triggered distributed model predictive control algorithm for coupled nonlinear systems facing external disturbances and constraints on state and input variables. A dynamic self-triggered mechanism that combines the advantages of event-triggered and self-triggered strategies is designed to simultaneously reduce the frequencies of both sampling and solving optimization problems. Particularly, the triggering threshold is adaptively adjusted using a dynamic variable, which can effectively balance control performance and computational resources. Furthermore, through the construction of a two-model optimal control problem and the analysis of input-to-state practical stability for the overall system, a single-mode distributed model predictive control framework is established for each subsystem within the proposed algorithm, which enables a fully distributed implementation. Sufficient conditions for recursive feasibility and robust stability are investigated, and conservatism is reduced by eliminating the requirement for the system state to reach the terminal region in finite time. Finally, the effectiveness of the developed algorithm is validated through two numerical examples with comparisons.

This article considers the saturated distributed impulsive local exponential synchronization (LES) issue for directed coupled neural networks (CNNs) with proportional delay and distributed delay under the impulsive time window (ITW) scheme. By utilizing a modified compact convex hull representation of the saturation nonlinearity, the extended parameter variation formula, and the proportional delayed impulsive comparison principle, sufficient conditions for the local exponential synchronization (LES) of the CNNs subject to actuator saturation are obtained within the domain of attraction (DOA). Based on these conditions, an optimization problem constructed by transformed linear matrix inequality (LMI) constraints is formulated to determine the impulsive control gain for enlarging the estimation of DOA as much as possible with a predetermined exponential convergence rate. Ultimately, a numerical simulation is exhibited to illustrate the feasibility and validity of the theoretical results.

In AR/VR and other application scenarios, the quality of human free-view video reconstruction significantly impacts user experience. Current reconstruction methods often rely on images captured by multi-view cameras and struggle to handle deformations caused by human movement. To address this issue, we propose HFVR (Human Free-Viewpoint Reconstruction), which aims to reconstruct the free view of moving humans from monocular videos. The core concept of HFVR is to utilize the motion deformation field to connect the observation space and the canonical space to perform a 3D representation of humans in the canonical space for rendering from any perspective. We first introduce the motion deformation field to transform the human body in the observation space into a standard space, then model the human body in that standard space. Second, we design a 3D global perception module to use global structural information to address deformation accuracy issues. Finally, we construct a rendering-guided module based on a reference frame that effectively overcomes limitations due to a lack of multi-view information by combining 2D observation. Extensive experiments demonstrate that our method can render realistic free-viewpoint motion humans using monocular videos. Our method improves the LPIPS (Learned Perceptual Image Patch Similarity) metric by 9.89% compared to that of HumanNeRF, as well as by 45.28% compared to that of Neural Body.

This paper investigates the cosensus for a class of nonlinear multi-agent systems. As one of the boundless delays, proportional delay is introduced by considering the existence of tremendous amount of parallel information transfer paths with different axon sizes or lengths. To better reduce the complexity of analysis, three different parameter functions are constructed to deal with the proportional delayed systems. In view of the limited bandwidth and fully distributed architecture of agents in most practical applications, the distributed edge-based self-triggering impulsive (DEBSTI) controller is elaborately designed to eliminate the continuous monitoring, high control costs and global information transmission. Sufficient conditions are derived for achieving the consensus globally and exponentially by utilizing the methods of parameter variation, comparison principle, and average impulsive interval. In addition, the Zeno behavior could be avoided. Furthermore, a larger value range for the impulsive-effect-related parameter taking is obtained by considering the diverse functions of the impulses. Correspondingly, different convergence rates based on different ranges are calculated precisely. Finally, twenty one-link manipulators actuated by twenty DC motors are discussed in numerical simulation to demonstrate the effectiveness of the proposed results.Note to Practitioners—Nonlinear multi-agent systems could be applied to simulate large-scale systems like UAV formation and one-link manipulator with revolute joints. By considering the continuous monitoring, high control costs and global information transmission, the DEBSTI controller is skillfully designed. In view of the tremendous amount of parallel information transfer paths with different axon sizes or lengths, the proportional delay is taken into consideration to make results more practical based on three different parameter functions. Moreover, different impulsive functions are discussed. In numerical simulation, the one-link manipulator with revolute joints actuated by a DC motor is modeled to reflect the industrial application scenarios.

This paper deals with prespecified-time bipartite consensus (PTBC) for leaderless and leader-following multi-agent systems (MASs) via intermittent control. A unified framework for realizing prespecified-time (PT) intermittent control is developed and analyzed theoretically. On the basis of the communication network with cooperative-competitive interaction, both PTBC of leaderless MAS and leader-following MAS are considered. And the distributed controllers rather than the centralized controllers are designed to realize PTBC of the corresponding controlled systems respectively. Further, it is shown that the designed controller is uniformly bounded even though the time-varying feedback gain in the designed controller tends to infinity as time tends to the settling time. Finally, some numerical examples on actual circuit systems are represented to substantiate the validity and application of our theoretical results.

In this article, the quasi-synchronization for a kind of coupled neural networks with time-varying delays is investigated via a novel event-triggered impulsive control approach. In view of the randomly occurring uncertainties (ROUs) in the communication channels, the global quasi-synchronization for the coupled neural networks within a given error bound is considered instead of discussing the complete synchronization. A kind of distributed event-triggered impulsive controllers is presented with considering the Bernoulli stochastic variables based on ROUs, which works at each event-triggered impulsive instant. According to the matrix measure method and the Lyapunov stability theorem, several sufficient conditions for the realization of the quasi-synchronization are successfully derived. Combining with the mathematical methodology with the formula of variation of parameters and the comparison principle for the impulsive systems with time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Meanwhile, the Zeno behaviors could be eliminated in the coupled neural network with the proposed event-triggered function. Finally, a numerical example is presented to prove the results of theoretical analysis.

Fixed-time synchronization of complex networks is investigated in this article. First, a completely novel lemma is introduced to prove the fixed-time stability of the equilibrium of a general ordinary differential system, which is less conservative and has a simpler form than those in the existing literature. Then, sufficient conditions are presented to realize synchronization of a complex network (with a target system) within a settling time via three different kinds of simple controllers. In general, controllers designed to achieve fixed-time stability consist of three terms and are discontinuous. However, in our mechanisms, the controllers only contain two terms or even one term and are continuous. Thus, our controllers are simpler and of more practical applicability. Finally, three examples are provided to illustrate the correctness and effectiveness of our results.

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the${e}$lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings.

This article is concerned with fixed-time synchronization of the nonlinearly coupled neural networks with discontinuous activation and mismatched parameters. First, a novel lemma is proposed to study fixed-time stability, which is less conservative than those in most existing results. Then, based on the new lemma, a discontinuous neural network with mismatched parameters will synchronize to the target state within a settling time via two kinds of unified and simple controllers. The settling time is theoretically estimated, which is independent of the initial values of the considered network. In particular, the estimated settling time is closer to the real synchronization time than those given in the existing literature. Finally, two numerical simulations are presented to illustrate the effectiveness and correctness of our results.

This article investigates adaptive control and exponential synchronization of a kind of derivative coupled complex dynamical networks (CDNs) with proportional delay. Based on impulsive control and adaptive pinning control protocols, sufficient criteria for achieving of the exponential synchronization on CDNs are obtained by jointly applying the proportional delayed impulsive comparison principle, the extended parameters variation formula and the definition of average impulsive interval. Meanwhile, suitable control gains for achieving adaptive synchronization are acquired according to the efficiently designed adaptive updating laws. In addition, the convergence velocity of the exponential synchronization is precisely estimated. Finally, one numerical simulation is presented to illustrate the validity of the adaptive pinning control protocols and theoretical results. By introducing the concept of impulsive distance for the first time, the example further explains the dynamic balance between the impulsive effects and the feedback control gains, which provides a method in controller designing.

This paper investigates the exponential synchronization of coupled Lur'e dynamical networks with multiple time-varying delays and derivative coupling. In order to synchronize the Lur'e dynamical networks to the corresponding Lur'e systems, we propose a kind of impulsive pinning control strategy, where different functions of impulsive effects are taken into account. Sufficient conditions are derived for the exponential synchronization of the derivative coupled Lur'e dynamical networks by jointly applying the contradiction proof method, the concept of an average impulsive interval, comparison principle, and the extended parameters variation formula. Simultaneously, the convergence rates of exponential synchronization are obtained according to the definition of the impulsive solution equation on different functions of impulsive effects. Furthermore, three numerical examples are presented to demonstrate the validity of the theoretical analysis and the control protocol.

This paper investigates the exponential synchronization of nonidentically coupled Lur'e dynamical networks with proportional delay. Since the heterogeneities existed in different Lur'e systems, quasi-synchronization rather than complete synchronization is thus discussed. Different from general time delay, the proportional delay is a type of unbounded time-varying delay, which tremendously increases the requirements on network synchronization. Based on distributed impulsive pinning control protocol and different roles that impulsive effects play, the criteria for quasi-synchronization of nonidentically coupled Lur'e dynamical networks are derived by jointly applying the delayed impulsive comparison principle, the extended formula for the variation of parameters, and the definition of an average impulsive interval. Moreover, synchronization errors for different impulsive effects with different functions are evaluated and simultaneously, the corresponding exponential convergence rates are obtained. In addition, three numerical examples are presented to illustrate the validity of the control scheme and the theoretical analysis.

This technical correspondence considers finite-time synchronization of dynamical networks by designing aperiodically intermittent pinning controllers with logarithmic quantization. The control scheme can greatly reduce control cost and save both communication channels and bandwidth. By using multiple Lyapunov functions and convex combination techniques, sufficient conditions formulated by a set of linear matrix inequalities are derived to guarantee that all the node systems are synchronized with an isolated trajectory in a finite settling time. Compared with existing results, the main characteristics of this paper are twofold: 1) quantized controller is used for finite-time synchronization and 2) the designed multiple Lyapunov functions are strictly decreasing. An optimal algorithm is proposed for the estimation of settling time. Numerical simulations are provided to demonstrate the effectiveness of the theoretical analysis.

This paper is concerned with the exponential synchronization issue of nonidentically coupled neural networks with time-varying delay. Due to the parameter mismatch phenomena existed in neural networks, the problem of quasi-synchronization is thus discussed by applying some impulsive control strategies. Based on the definition of average impulsive interval and the extended comparison principle for impulsive systems, some criteria for achieving the quasi-synchronization of neural networks are derived. More extensive ranges of impulsive effects are discussed so that impulse could either play an effective role or play an adverse role in the final network synchronization. In addition, according to the extended formula for the variation of parameters with time-varying delay, precisely exponential convergence rates and quasi-synchronization errors are obtained, respectively, in view of different types impulsive effects. Finally, some numerical simulations with different types of impulsive effects are presented to illustrate the effectiveness of theoretical analysis.

This paper investigates the problem of finite-time drive-response synchronization for a class of neural networks with discontinuous activations, time-varying discrete and infinite-time distributed delays, and mismatched parameters. In order to cope with the difficulties induced by discontinuous activations, time delays, as well as mismatched parameters simultaneously, new 1-norm-based analytical techniques are developed. Both state feedback and adaptive controllers with and without the sign function are designed. Based on differential inclusion theory and Lyapunov functional method, several sufficient conditions on the finite-time synchronization are obtained. Our results show that the controllers with a sign function can reduce the conservativeness of control gains and the controllers without a sign function can overcome the chattering phenomenon. Numerical examples are given to show the effectiveness of the theoretical analysis.

In this paper, the quasi-synchronization of a class of nonlinearly-coupled complex networks is studied. Different from the previous works, a distinguishing characteristic of this work is that the coupling matrix of nonlinear coupled networks is asymmetrical. The effect of time-varying delay in dynamical networks is also considered. By utilizing the aperiodically intermittent pinning control technique, some more general sufficient conditions to guarantee global quasi-synchronization are derived. Finally, a numerical simulation is presented to demonstrate the efficiency of the theoretical findings.

In this paper, the impact of the clustering exponent (α) on synchronizability, average shortest path length and navigability of small-world networks generated by one dimensional Kleinberg model is investigated. It could be seen from the analysis that the synchronizability becomes stronger as the clustering exponent decreases. And the navigability achieves peak at the neighborhood of α = 1, as well as the navigability becomes smaller as the clustering exponent increases. Moreover, the average path length of one dimensional Kleinberg small-world network decreases with respect to increasing clustering exponent. And this phenomenon is verified by numerical simulations on a network of Rossler oscillators. Then, it could be deduced from the phenomenon observed that compared with the low probabilities of longer distance of the edge-adding, the high probabilities of shorter distance of the edge-adding could achieve better synchronizability.