Synchronization control is an important issue in complex networks (CNs), but the specific realization is generally influenced by many factors. In this article, by taking into accounts of the coupling effect and cyber attacks, we dedicate to design an efficient synchronization control approach for discrete-time CNs. Given that the coupling among nodes in CNs inevitably introduces the internode data exchange, round robin (RR) protocol is adopted to prevent data collision caused by the limitation of communication resources. Moreover, a probabilistic interval model is employed to capture the characteristics of the coupling delay, i.e., the delay of the internode data transmission. The deception attacks launched on the multichannel-enabled communication network between controllers and actuators of nodes in CNs are also considered. Then, a synchronization error model is established to describe the concerned synchronization control problem. By defining appropriate Lyapunov–Krsasovskii function, the sufficient conditions for the stability of the envisioned synchronization error system are obtained, and the design method for controllers is proposed subsequently. Simulations are finally conducted to demonstrate the validity of the work.

In this article, the global fixed-time stabilization problem is addressed for a class of switched uncertain nonlinear systems in$p$-normal form. Different from the existing results, the bounds of the unknown system parameters, including dead-zone parameters and control coefficients, are not required to be known. By combining the adding a power integrator technique with the common Lyapunov function method, a novel adaptive controller is proposed and a dynamic controller parameter is introduced to cope with the unknown system parameters. Besides, an effective common regulate rule is designed based on the improved fixed-time stability framework. Furthermore, it is shown that the controller parameter can be regulated online by the switching mechanism to compensate the unknown system parameters and the system states can converge to zero in fixed time under arbitrary switchings. The effectiveness of the proposed method is verified by a simulation example.

This article investigates the issue of observer-based security control for the interconnected semi-Markovian jump systems with completely unknown and uncertain bounded transition probabilities (TPs). Considering the limited bandwidth of communication network in each subsystem, an adaptive event-triggered mechanism (AETM) is developed to relieve more network burden than the conventional event-triggered mechanism (ETM), where the designed adaptive law can dynamically adjust the triggering threshold. In addition, two Bernoulli distributed variables are utilized to describe the influence of denial-of-service (DoS) attacks and false-data injection (FDI) attacks in the proposed observer-based security control strategy. Moreover, some sufficient criterions are derived for the stochastic stability with an$H_{\infty }$attenuation level of augmented systems. Meanwhile, the observer and controller gain matrices can be attained simultaneously with the help of linear matrix inequalities (LMIs). Finally, we provide a practical example to demonstrate the effectiveness of theoretical results.

For full-state constrained nonlinear systems with input saturation, this article studies the output-feedback tracking control under the condition that the states and external disturbances are both unmeasurable. A novel composite observer consisting of state observer and disturbance observer is designed to deal with the unmeasurable states and disturbances simultaneously. Distinct from the related literature, an auxiliary system with approximate coordinate transformation is used to attenuate the effects generated by input saturation. Then, using radial basis function neural networks (RBF NNs) and the barrier Lyapunov function (BLF), an opportune backstepping design procedure is given with employing the dynamic surface control (DSC) to avoid the problem of "explosion of complexity." Based on the given design procedure, an output-feedback controller is constructed and guarantees all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. It is shown that the tracking error is regulated by the saturated input error and design parameters without the violation of the state constraints. Finally, a simulation example of a robot arm is given to demonstrate the effectiveness of the proposed controller.

This article addresses the adaptive event-triggered nonfragile state estimation for the fractional-order complex networked systems subject to randomly occurring nonlinearities and adversarial network attacks, in which the order of fractional derivative operator satisfies$0 < p < 1$. To reduce unnecessary transmission burden as much as possible in an allowable range, an adaptive event-triggered scheme (AETS) is introduced to determine whether the data released by the sensor should be transmitted to the nonfragile state estimator. First, based on the considerations of the designed AETS and stochastic cyber-attacks, one constructs a newly fractional-order estimation error system model. Then, by employing the Lyapunov functional approach and the properties of Mittag–Leffler (M–L) functions, a sufficient condition is obtained to ensure the augmented error system stochastic mean-square stability; moreover, by making use of matrix’s singular value decomposition (SVD), the desired nonfragile state estimator is designed, and the estimator gains can be obtained by finding the feasible solutions of linear matrix inequality (LMI). Finally, a numerical example and Chua’s circuit model example are given to illustrate the feasibility of the designed nonfragile estimator.

This article investigates the observer-based finite-time H∞control problem for interconnected fuzzy systems with quantization and random network attacks, where two types of network attacks including denial-of-service (DoS) and fault data injection (FDI) attacks are considered. In order to achieve the occupancy reduction of network resources, the measured outputs of interconnected systems are quantized through a logarithmic quantizer before being transmitted by the communication channel. Combining the effects of quantization and random network attacks, an observer-based control model is first constructed. Then, based on Lyapunov functional approach and stochastic analysis technique, some sufficient conditions are presented such that the considered closed-loop interconnected system is stochastically finite-time bounded with a prescribed disturbance attenuation level, and the gain matrices of fuzzy observer and fuzzy controller can be found by solving an optimization algorithm with linear matrix inequalities constraints. Finally, two simulation examples are given to illustrate the validity of the proposed approach.

This article deals with H∞state estimation of neural networks with mixed delays. In order to make full use of delay information, novel delay-product Lyapunov-Krasovskii functional (LKF) by using parameterized delay interval is first constructed. Then, generalized free-weighting-matrix integral inequality is used to estimate the derivative of LKF to reduce the conservatism. Also, a more general activation function is further applied by combining with parameterized delay interval in order to obtain a more accurate estimator model. Finally, sufficient conditions are derived to confirm that the estimation error system is asymptotically stable with a prescribed H∞performance. Numerical examples are simulated to show the benefits of our proposed method.

This letter presents a hand-held complex large-scale scene reconstruction method with Kinect sensors based on surfel and video sequences. The feature point method simultaneous localization and mapping (SLAM) is employed to estimate the pose of the camera, and then bundle adjustment by combining 2-D and 3-D feature points is used to optimize camera pose. Also, the surfel model is employed to construct deformation maps for the fusion and optimization of point clouds, and finally, an accurate precise 3-D map can be obtained. The main contribution of this letter is that: 1) by using the SLAM method to obtain camera pose as the initial value of optimization, the problem of insufficient memory and low efficiency of the structure form motion method can be well solved; 2) sparsely textured regions can be reconstructed better by using bundle adjustment by combining 2-D and 3-D feature points; and 3) dense 3-D reconstruction of large scenes can be achieved, and the reconstructed 3-D models are more elaborate. Finally, experimental results show that this proposed method can be applied to a variety of complex large-scale scenes, and can obtain accurate precise 3-D model. This presented 3-D reconstruction method can be widely used in the fields of human-computer interaction, consumer electronics, and virtual reality.

This paper examines quantized stabilization for Takagi-Sugeno (T-S) fuzzy systems with a hybrid-triggered mechanism and stochastic cyber-attacks. A hybrid-triggered scheme, which is described by a Bernoulli variable, is adopted to mitigate the burden of the network. By taking the effect of the hybrid-triggered scheme and stochastic cyber-attacks into consideration, a mathematical model for a closed-loop control system with quantization is constructed. Theorems for main results are developed to guarantee the asymptotical stability of networked control systems by using Lyapunov stability theory and linear matrix inequality techniques. Based on the derived sufficient conditions in theorems, the controller gains are presented in an explicit form. Finally, two practical examples demonstrate the feasibility of designed algorithm.

As is well known, quantization is necessary for network control systems to reduce transmission congestions. In the paper, the exponential stabilization problem is investigated for a class of discrete-time switched cascade systems via Takagi-Sugeno (T-S) fuzzy quantized feedback control approach. The switched cascade nonlinear system is firstly modelled as a switched T-S fuzzy system. Based on mode-dependent average dwell time (ADT) method, a new delayed nonquadratic Lyapunov function is then constructed to design switched fuzzy controllers from the corresponding T-S fuzzy models so that the controller gains can be computed separately. Besides, the stabilization conditions can be given in terms of linear matrix inequalities (LMIs), which are easy to be checked by using recently developed algorithms in solving LMIs. Finally, a numerical example including two scenarios is considered to show the effectiveness of the proposed method.

In this brief, by employing an improved Lyapunov-Krasovskii functional (LKF) and combining the reciprocal convex technique with the convex one, a new sufficient condition is derived to guarantee a class of delayed neural networks (DNNs) to be globally asymptotically stable. Since some previously ignored terms can be considered during the estimation of the derivative of LKF, a less conservative stability criterion is derived in the forms of linear matrix inequalities, whose solvability heavily depends on the information of addressed DNNs. Finally, we demonstrate by two numerical examples that our results reduce the conservatism more efficiently than some currently used methods.

An adaptive threshold value (ATV) and two-orthogonal-orientation edge correlogram (TOEC) based algorithm is proposed for tracking moving object in real scenarios. The ATV is used to extract the object edges under illumination changes. To improve the object edges representation power, the TOEC encodes the edge orientation pair levels along two orthogonal directions explicitly. An entropy weighting-maximization scheme is presented to achieve the maximum likelihood estimation of the similar regions and scales. Experimental results show that the proposed approach is appealing with respect to the robustness in the scenarios of complex occlusions and illumination variations.

This paper is concerned with nonquadratic stabilization design problem for continuous-time nonlinear models in the Takagi-Sugeno (T-S) form obtained by sector nonlinearity approach. Most of the previous results found in the literature intended to establish global nonquadratic stabilization conditions which are hard to uphold due to the difficulty of handling time derivatives of the membership function. By changing the paradigm of global stabilization for something less restrictive, a local solution to overcome infeasible quadratic stabilization conditions is offered in this paper. It is shown that the derived local nonquadratic conditions actually lead to reasonable advantages over the existing quadratic approach, as well as some previous nonquadratic attempts. Moreover, conditions for the solvability of state feedback controller design given here are written in the form of linear matrix inequalities (LMIs) which can be efficiently solved by convex optimization techniques. Simulation examples are given to demonstrate the validity and applicability of the proposed approaches.

In this brief, based on Lyapunov-Krasovskii functional approach and appropriate integral inequality, a new sufficient condition is derived to guarantee the global stability for delayed neural networks with unbounded distributed delay, in which the improved delay-partitioning technique and general convex combination are employed. The LMI-based criterion heavily depends on both the upper and lower bounds on time delay and its derivative, which is different from the existent ones and has wider application fields than some present results. Finally, three numerical examples can illustrate the efficiency of the new method based on the reduced conservatism which can be achieved by thinning the delay interval.

This paper investigates robust exponential stability for discrete-time recurrent neural networks with both time-varying delay (0 < or = tau(m) < or = tau(k) < or = tau(M)) and distributed one. Through partitioning delay intervals [0, tau(m)] and [tau(m), tau(M)], respectively, and choosing an augmented Lyapunov-Krasovskii functional, the delay-dependent sufficient conditions are obtained by using free-weighting matrix and convex combination methods. These criteria are presented in terms of linear matrix inequalities (LMIs) and their feasibility can be easily checked by resorting to LMI in Matlab Toolbox in Ref. 1. The activation functions are not required to be differentiable or strictly monotonic, which generalizes those earlier forms. As an extension, we further consider the robust stability of discrete-time delayed Cohen-Grossberg neural networks. Finally, the effectiveness of the proposed results is further illustrated by three numerical examples in comparison with the reported ones.