Synchronization of IT2 stochastic fuzzy complex dynamical networks with time-varying delay via fuzzy pinning control

In this paper, the problem of synchronization on interval type-2 (IT2) stochastic fuzzy complex dynamical networks (CDNs) with time-varying delay via fuzzy pinning control is fully studied. Firstly, a more general complex network model is considered, which involves the time-varying delay, IT2 fuzzy and stochastic effects. More specifically, IT2 fuzzy model, as a meaningful fuzzy scheme, is investigated for the first time in CDNs. Then, with the aid of Lyapunov stability theory and stochastic analysis technique, some new sufficient criteria are established to ensure synchronization of the addressed systems. Moreover, on basis of the parallel-distributed compensation (PDC) scheme, two effective fuzzy pinning control protocols are proposed to achieve the synchronization. Finally, a numerical example is performed to illustrate the effectiveness and superiority of the derived theoretical results.     

Synchronization of multiplex networks with stochastic perturbations via pinning adaptive control

This paper investigates the synchronization problems for the multiplex networks with both inter-layer and intra-layer couplings subject to the stochastic perturbations. In particular, the topologies of all layers are not the same, so the model can represent a class of multiplex networks. To synchronize the multiplex networks onto the trajectory of a virtual leader, a pinning adaptive protocol is proposed and some pinning criteria are derived for guaranteeing complete synchronization. Furthermore, when the results are extended to the systems with time delays, the pinning adaptive strategy is still proved to be effective. Finally, a two-layer network and a three-layer network are selected for numerical simulations to illustrate the theoretical results.     

Synchronization of stochastic discrete-time complex networks with partial mixed impulsive effects

In this paper, the synchronization problem is studied for a class of stochastic discrete-time complex networks with partial mixed impulsive effects. The involving impulsive effects, called partial mixed impulses, can be regarded as local and time-varying impulses, which means that impulses are not only injected into a fraction of nodes in networks but also contain synchronizing and desynchronizing impulses at the same time. In order to handle this case, several mathematical techniques are proposed to tackle mixed impulsive effects in discrete-time dynamical systems. Based on the variation of parameters formula, several sufficient criteria are derived to ensure that synchronization of the addressed networks can be achieved in mean square. The obtained criteria not only rely on the strengths of mixed impulses and the impulsive intervals, but also can reduce conservativeness. Finally, a numerical example is presented to show the effectiveness of our results for neural networks.     

Fixed-time pinning synchronization for delayed complex networks under completely intermittent control

The present study investigates the fixed-time synchronization issue for delayed complex networks under intermittent pinning control. Different from some existing semi-intermittent controllers for finite/fixed-time synchronization, our pinning controller is designed in a complete intermittent way. In order to address the encountered theoretical analysis difficulties, a new differential inequality lemma is developed, which is suitable for the fixed-time synchronization studies under periodic or aperiodic complete intermittent control. Then, by using Lyapunov theory and pinning control approach, sufficient conditions are proposed which can guarantee the aperiodically completely intermittent-controlled delayed complex networks realizing fixed-time pinning synchronization. Moreover, the settling time is explicitly estimated, which is irrelevant to the initial values of our network systems. Additionally, as a special case, the scenario of periodic complete intermittent control is also discussed. At last, some simulation examples are utilized to confirm our theoretical outcomes.     

Model-free discrete-time fractional fuzzy control of robotic manipulators

Robots are commonly used to perform repetitive or dangerous tasks, and these systems integrate different components that increase the complexity of their dynamic model. Thus, in order to ensure an acceptable tracking performance, the implementation of robust control strategies should be considered. This paper proposes a discrete-time model-free-implementation controller, which relies on the well-known structural properties of mechanical systems, but it does not consider any knowledge on the values of the system parameters. Besides, a fuzzy logic formulation is considered to compensate external disturbances. The outputs of the fuzzy inference system are adapted online to improve the closed-loop response. Finally, representatives simulation and experiments are analyzed to demonstrated the feasibility of the proposed approach.     

A survey on global pinning synchronization of complex networks

In practice, it is almost impossible to directly add a controller on each node in a complex dynamical network due to the high control cost and the difficulty of practical implementation, especially for large-scale networks. In order to address this issue, a pinning control strategy is introduced as a feasible alternative. The objective of this paper is first to recall some recent advancements in global pinning synchronization of complex networks with general communication topologies. A systematic review is presented thoroughly from the following aspects, including modeling, network topologies, control methodologies, theoretical analysis methods, and pinned node selection and localization schemes (pinning strategies). Fully distributed adaptive laws are proposed subsequently for the coupling strength as well as pinning control gains, and sufficient conditions are obtained to synchronize and pin a general complex network to a preassigned trajectory. Moreover, some open problems and future works in the field are also discussed.     

Recursive state estimation based-on the outputs of partial nodes for discrete-time stochastic complex networks with switched topology

In this paper, the state estimation problem is studied for a class of discrete-time stochastic complex networks with switched topology. In the network under consideration, we assume that measurement outputs can be got from only partial nodes, besides, the switching rule of this network is characterized by a sequence of Bernoulli random variables. The aim of the presented estimation problem is to develop a recursive estimator based on the framework of extended Kalman filter (EKF), such that the upper bound for the filtering error convariance is optimized. In order to address the nonlinear functions, the Taylor series expansion is utilized and the high-order terms of linearization errors are expressed in an exact way. Furthermore, by solving two Ricatti-like difference equations, the gain matrix can be acquired at each time instant. It is shown that the filtering error is bounded in mean square under some conditions with the aid of stochastic analysis techniques. A numerical example is given to demonstrate the validity of the proposed estimator.     

Delay-dependent cluster synchronization of time-varying complex dynamical networks with noise via delayed pinning impulsive control

This paper investigates the problem of cluster synchronization of complex dynamical networks with noise and time-varying delays by using a delayed pinning impulsive control scheme. Different from the traditional impulsive control schemes without the effects of input delays, it designs a pinning impulsive control scheme to successfully address the aforementioned problem subject to impulsive input delays. By employing a time-dependent Lyapunov function and the mathematical induction, some novel criteria are established to guarantee the cluster synchronization of the noisy complex networks, revealing the closed relationship between the synchronization performance and the related factors, including the impulsive input delays, the number of the pinned nodes, the frequency and strength of the impulsive control, and the noisy perturbations. Some numerical examples and computer simulations are presented to illustrate the effectiveness of the theoretical results.     

Dissipativity of discrete-time BAM stochastic neural networks with Markovian switching and impulses

This paper addresses the problem of global exponential dissipativity for a class of uncertain discrete-time BAM stochastic neural networks with time-varying delays, Markovian jumping and impulses. By constructing a proper Lyapunov–Krasovskii functional and combining with linear matrix inequality (LMI) technique, several sufficient conditions are derived for verifying the global exponential dissipativity in the mean square of such stochastic discrete-time BAM neural networks. The derived conditions are established in terms of linear matrix inequalities, which can be easily solved by some available software packages. One important feature presented in our paper is that without employing model transformation and free-weighting matrices our obtained result leads to less conservatism. Additionally, three numerical examples with simulation results are provided to show the effectiveness and usefulness of the obtained result.     

Aperiodically intermittent control for synchronization of discrete-time delayed neural networks

This paper is concerned with the aperiodically intermittent control (AIC) for the synchronization of discrete-time neural networks with time delay. The synchronization is analyzed by the piecewise Lyapunov function approach and the piecewise Lyapunov–Krasovskii functional approach, respectively. The average activation time ratio of AIC is estimated, which is more general and less conservative than the minimum activation time ratio. Finally, a numerical example is exploited and detailed comparisons are presented to demonstrate the effectiveness and less conservativeness of the obtained results.     

Exponential synchronization for coupled complex networks with time-varying delays and stochastic perturbations via impulsive control

This paper is concerned with the problem of exponential synchronization of coupled complex networks with time-varying delays and stochastic perturbations (CCNTDSP). Different from previous works, both the internal time-varying delay and the coupling time-varying delay are taken into account in the network model. Meanwhile, an impulsive controller is designed to realize exponential synchronization in mean square of CCNTDSP. Combining the Lyapunov method with Kirchhoff’s Matrix Tree Theorem, some sufficient criteria are obtained to guarantee exponential synchronization in mean square of CCNTDSP. Furthermore, we apply the theoretical results to study exponential synchronization of stochastic coupled oscillators with the internal time-varying delay and the coupling time-varying delay. And a synchronization criterion is also obtained. Finally, two numerical examples are given to demonstrate the effectiveness and feasibility of our theoretical results and the superiority of impulsive control.     

Observability of Boolean control networks with stochastic disturbances

In this paper, the observability problem of Boolean control networks (BCNs) with stochastic disturbances is investigated via two kinds of control schemes: deterministic control and state feedback control. Firstly, based on the proposed indicator matrix, a simplified system of the original augmented Boolean system is constructed. Based on the analysis of the auxiliary system, observability of the original BCN is converted to determine whether an observable set can be reached from another unobservable set. After that, some necessary and sufficient conditions are obtained to judge the observability of BCNs. At the same time, two algorithms are proposed for designing these two types of control sequences. Finally, numerical simulations are also provided to demonstrate feasibility of the theoretical results.     

Stabilization of Boolean control networks with stochastic impulses

This paper studies the stabilization problem of Boolean control networks with stochastic impulses, where stochastic impulses model is described as a series of possible regulatory models with corresponding probabilities. The stochastic impulses model makes the research more realistic. The global stabilization problem is trying to drive all states to reach the predefined target with probability 1. A necessary and sufficient condition is presented to judge whether a given system is globally stabilizable. Meanwhile, an algorithm is proposed to stabilize the given system by designing a state feedback controller and different impulses strategies. As an extension, these results are applied to analyze the global stabilization to a fixed state of probability Boolean control networks with stochastic impulses. Finally, two examples are given to demonstrate the effectiveness of the obtained results.     

Impulse-based coupling synchronization of multiple discrete-time memristor-based neural networks with stochastic perturbations and mixed delays

The global synchronization problem of multiple discrete-time memristor-based neural networks (DTMNNs) with stochastic perturbations and mixed delays is studied under impulse-based coupling control, where the coupling control only occurs at discrete impulse times. The impulse-based coupling control will further reduce the communication bandwidth for multiple DTMNNs to achieve coupling synchronization. We construct an array of multiple DTMNNs with stochastic perturbations and mixed delays and propose a novel impulse-based coupling control scheme. By utilizing Lyapunov–Krasovskii functional technique, schur complement technique and linear matrix inequality (LMI) method, some sufficient synchronization conditions depending on stochastic perturbations and mixed delays are established. At the end of this paper, a numerical example is given and the effectiveness of the impulse-based coupling control is illustrated by using MATLAB programming.     

Cluster synchronization in community networks with nonidentical nodes via edge-based adaptive pinning control

This paper investigates cluster synchronization in community networks with nonidentical nodes. Several effective strategies to enhance the coupling weights are designed. For the first time, adaptive enhancing factor method combined with edge-based pinning control is adopted to achieve synchronization. Furthermore, distributed adaptive pinning control scheme is adopted based on the local information of node dynamics. Noticeably, only the coupling weights of spanning trees in each community are tuned, which are low-cost and more practicable. Based on Lyapunov stability theory, some sufficient conditions for cluster synchronization are derived. Numerical simulations are provided to verify the effectiveness of the theoretical results.     

On robust detection of discrete-time stochastic signals

The problem of designing robust systems for the detection of stochastic signals in noise is considered for the large-sample-size, small-signal case. By applying two previously-established models for the detection of stochastic signals, known results for the robust detection of deterministic signals are extended on a limited basis to the stochastic- signal case. The proposed detectors are seen to be robust over a class of possible noise statistics, based on a Huber-Tukey mixture model, which contains noises characterized by heavy-tailed probability density functions. In addition, numerical results are presented which verify the robustness property of the proposed detectors over wider classes of noise mixtures.     

Terminal iterative learning control for discrete-time nonlinear systems based on neural networks

The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms.     

Hopf bifurcation control for delayed complex networks

In this paper, we consider the problem of Hopf bifurcation control for a complex network model with time delays. We know that for the system without control, as the positive gain parameter of the system passes a critical point, Hopf bifurcation occurs. To control the Hopf bifurcation, a time-delayed feedback controller is proposed to delay the onset of an inherent bifurcation when such bifurcation is undesired. Furthermore, we can also change the stability and direction of bifurcating periodic solutions by choosing appropriate control parameters. Numerical simulation results confirm that the new feedback controller using time delay is efficient in controlling Hopf bifurcation.     

Existence of periodic solution for discrete-time cellular neural networks with complex deviating arguments and impulses

A class of discrete-time cellular neural networks with complex deviating arguments and impulses are considered. Sufficient conditions for the existence of periodic solution are obtained by using contraction theorem and inequality techniques. The results of this paper are new. An example is employed to illustrate our results.