Asynchronous boundary stabilization for T-S fuzzy Markov jump delay reaction-diffusion neural networks

This paper deals with the exponential boundary stabilization for a class of Markov jump reaction-diffusion neural networks (MJRDNNs) with mixed time-varying delays, which is described by T-S fuzzy model. It is assumed that observed modes in boundary controller are not synchronized with the system modes. Based on a hidden Markov model (HMM), a novel asynchronous boundary control law is developed by using observed modes. Compared with the existing control strategies for distributed parameter systems, the asynchronous boundary control scheme can not only save the cost of the controller installation, but also bring less conservativeness. A delay-dependent sufficient condition to guarantee the exponentially mean square stability is established for T-S fuzzy MJRDNNs with mixed time-varying delays by constructing a Lyapunov functional and utilizing the vector-value Wirtinger-type inequality. Meanwhile, in order to get the designing scheme of the boundary controller, an equivalent LMI-based sufficient criterion is established. In the end, the effectiveness of the proposed results is illustrated by simulation examples.     

Networked load frequency control of multi-area uncertain power systems via adaptive event-triggered communication scheme

Load frequency control of power systems is a very important approach to keep stability and security. Unfortunately, the traditional load frequency control is not effective because of the introduction of communication networks in multi-area power systems. In order to overcome this difficulty, sampling-based load frequency control for multi-area power systems is studied via an event-triggered detector. Unlike published works, an adaptive law for event-triggered scheme is given. Since multi-area power systems with event-triggered scheme are hybrid systems, there are a lot of challenges for analysing load frequency control problem. Some lemmas and a new Lyapunov function are developed to overcome these challenges. The obtained stability and stabilization criteria can provide a tradeoff to balance the required communication resources and the desired control performance. Numerical examples verify effectiveness of the obtained results.     

Synchronization control of neutral-type neural networks with sampled-data via adaptive event-triggered communication scheme

This paper addresses the problem of synchronization control of neutral-type neural networks with sampled-data, where sampled data will be over a communication network before received by controller. Generally, the communication network is with a bandwidth-limited communication channel. To reduce network burden, an event-triggered scheme is designed between the sampler and communication network. A weak synchronization conditions are derived by using our proposed integral inequality. Finally, a numerical example is given to illustrate the effectiveness and advantage of the proposed results.     

Load frequency composite control for multi-region interconnected power systems

This paper investigates a composite controller for load frequency control (LFC) in multi-region interconnected power systems via sliding mode observer design. State observers (SOs) and disturbance observers (DOs) are implied for the LFC based on the load variations with communication delays and quantization output measurements. A nonlinear integral sliding surface combined with a composite controller is developed to optimize control performance. Moreover, a three-area power system model is used to demonstrate the effectiveness of the proposed scheme in the illustrative example, confirming that frequency deviations can be rejected despite delays, uncertainties, and quantization during transmission.     

Improved exponential stability analysis for delayed recurrent neural networks

In this paper, we investigate the problem of global exponential stability analysis for a class of delayed recurrent neural networks. This class includes Hopfield neural networks and cellular neural networks with interval time-delays. Improved exponential stability condition is derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed stability criteria are delay dependent and characterized by linear matrix inequalities (LMIs). The developed results are less conservative than previous published ones in the literature, which are illustrated by representative numerical examples.     

Stabilization criteria for neutral time delay systems with saturating actuators

This paper discusses the problems of delay-dependent stability and stabilization of neutral saturating actuator systems with constant or time-varying delays. The problems of stabilization for neutral saturating actuator system with time-varying delay and parameter from the presented results, the condition obtained here does not need derivative information of the delay time and thus can be used to analyze the stabilization problem for a class of saturating actuator systems with time-varying delay, which is bounded but arbitrarily fast time-varying. Using the model transformation and quasi-convex optimization problem, we derive delay-dependent conditions for the stability of systems in terms of the linear matrix inequality. The stabilization conditions are formulated as linear matrix inequalities (LMIs) which can be solved by convex optimization algorithm. Moreover, the stability criteria are extended to design a stabilizing state feedback controller. Numerical examples show that the results obtained in this paper significantly improve the estimate of stability limit over some existing results reported previously in the literature.     

Observer-based quantized control for discrete-time switched systems with infinitely distributed delay

This paper studies the globally almost surely exponential stabilization of discrete-time switched systems (DSSs) with infinitely distributed delay. On account of the limitation of communication resources in the actual environment, a novel class of observer-based quantized control scheme is designed that incorporates the quantization of three kinds of signals: the measurement output, the state of observer, and the measurement output of observer. By employing S-procedure and some matrix inequality techniques, an algorithm is given to design the controller parameters. To reduce the conservativeness of the obtained results, new multiple Lyapunov–Krasovskii functionals (LKFs) with negative terms are proposed to deal with the infinitely distributed delay and mode-dependent average dwell time (MDADT) switching based on transition probability (TP) is introduced to study the stabilization of DSSs with both stable and unstable modes. It is worth highlighting that the improved stabilization conditions for DSSs can release the restriction on the length of dwell time (DT) of stable and unstable subsystems. Finally, a simulation example is presented to demonstrate the validity of the proposed method.     

Simultaneous exponential stabilization for stochastic port-controlled Hamiltonian systems with actuator saturations,fading channels and delays

This paper is concerned with the simultaneous exponential stabilization problem for a set of stochastic port-controlled Hamiltonian (PCH) systems. Due to the limited bandwidth of the channels, the phenomena of fading channels and transmission delays which are described by a time-varying stochastic model always occur in the communication channels from the controller to the actuator. Meanwhile, actuator saturation constraint is taken into account. On the basis of dissipative Hamiltonian structural and saturating actuator properties, those stochastic PCH systems are combined to generate an augmented system. By utilizing the stochastic analysis theory, sufficient criterions are given for the simultaneous stabilization controller design ensuring that the closed-loop system is simultaneously exponentially mean-square stable (SEMSS). For the case that there exist external disturbances in the systems, some results on stability analysis and controller design are given. The developed controller design scheme is proved by a three-helicopter model simulation example.     

Event-triggered gain-scheduling dissipative synchronization control for switched neural networks under state-dependent switching

The dissipative synchronization problem of delayed Markov jump switched neural networks (MJSNNs) under state-dependent switching by the event-triggered gain-scheduling control scheme is studied in this paper. By the introduction of a Markov jump model, which is used to depict the random variation wherein the connection of MJSNNs, the issues we study can take more generality. Via constructing suitable Lyapunov–Krasovskii functionals (LKFs) and applying some matrix inequality scaling methods, sufficient conditions for dissipative synchronization of delayed MJSNN are established. According to such criteria, the event-triggered gain-scheduling control scheme is adopted to design a controller with less terminal communication costs. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.     

Finite-time stabilization of fractional-order fuzzy quaternion-valued BAM neural networks via direct quaternion approach

This paper investigates fractional-order fuzzy quaternion-valued BAM neural networks (FOFQBAMNNs) without decomposition. By virtue of a novel contraction mapping, the existence and uniqueness of the equilibrium point is yielded. Furthermore, according to some basic knowledge on fractional calculus, inequality techniques of fuzzy logic and reduction to absurdity, some criteria are yielded to guarantee finite-time stabilization of FOFQBAMNNs via original quaternion-valued controllers, and the settling times of corresponding finite-time stabilization are derived. Finally, the feasibility of our obtained theoretical results is illustrated by some numerical simulations.     

Average consensus in networks of dynamic agents with uncertain topologies and time-varying delays

In this paper, we study average consensus problem in networks of dynamic agents with uncertain topologies as well as time-varying communication delays. By using the linear matrix inequality method, we establish several sufficient conditions for average consensus in the existence of both uncertainties and delays. Several linear matrix inequality conditions are presented to determine the allowable upper bounds of time-varying communication delays and uncertainties. Numerical examples are worked out to illustrate the theoretical results.     

Robust asymptotic stability of interval fractional-order nonlinear systems with time-delay

This paper studies the global asymptotic stability of a class of interval fractional-order (FO) nonlinear systems with time-delay. First, a new lemma for the Caputo fractional derivative is presented. It extends the FO Lyapunov direct method allowing the stability analysis and synthesis of FO nonlinear systems with time-delay. Second, by employing FO Razumikhin theorem, a new delay-independent stability criterion, in the form of linear matrix inequality is established for ensuring that a system is globally asymptotically stable. It is shown that the new criterion is simple, easy to use and valid for the FO or integer-order interval neural networks with time-delay. Finally, the feasibility and effectiveness of the proposed scheme are tested with a numerical example.     

Finite-time stochastic stabilization for BAM neural networks with uncertainties

This paper is concerned with the finite-time stabilization for a class of stochastic BAM neural networks with parameter uncertainties. Compared with the previous references, a continuous stabilizator is designed for stabilizing the states of stochastic BAM neural networks in finite time. Based on the finite-time stability theorem of stochastic nonlinear systems, several sufficient conditions are proposed for guaranteeing the finite-time stability of the controlled neural networks in probability. Meanwhile, the gains of the finite-time controller could be designed by solving some linear matrix inequalities. Furthermore, for the stochastic BAM neural networks with uncertain parameters, the problem of robust finite-time stabilization could also be ensured as well. Finally, two numerical examples are given to illustrate the effectiveness of the obtained theoretical results.     

Existence and exponential stability of almost periodic solution for neutral delay BAM neural networks with time-varying delays in leakage terms

This paper is concerned with a class of neutral delay BAM neural networks with time-varying delays in leakage terms. Some sufficient conditions are established to ensure the existence and exponential stability for such class of neural networks by employing the exponential dichotomy of linear differential equations, fixed point theorems and differential inequality techniques. An example is provided to show the effectiveness of the theoretical results. The results of this paper are completely new and complementary to the previously known results.     

Non-fragile sampled-data stabilization analysis for linear systems with probabilistic time-varying delays

This paper deals with the problem of non-fragile sampled-data stabilization analysis for a class of linear systems with probabilistic time-varying delays via new double integral inequality approach. Based on the auxiliary function-based integral inequality (AFBII) and with the help of some mathematical approaches, a new double integral inequality (NDII) is developed. Then, to demonstrate the merits of the proposed inequality, an appropriate Lyapunov–Krasovskii functional (LKF) is constructed with some augmented delay-dependent terms. By employing integral inequalities, an enhanced stability criterion for the concerned system model is derived in terms of linear matrix inequalities (LMIs). Finally, three benchmark illustrative examples are given to validate the effectiveness and advantages of the proposed results.     

New delay dependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays

This paper is concerned with the stability analysis problem for a class of delayed stochastic recurrent neural networks with both discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to ensure the global, robust asymptotic stability for the addressed system in the mean square. The conditions obtained here are expressed in terms of LMIs whose feasibility can be checked easily by MATLAB LMI Control toolbox. In addition, two numerical examples with comparative results are given to justify the obtained stability results.     

A new approach for constrained chaos synchronization with application to secure data communication

In this paper, a new technique is introduced for chaos secure data communication. In this approach, in addition to the usually used techniques for data encryption, the concept of carrier encryption is introduced to increase the security level of the secure communication scheme. To fulfill this objective, at the transmitting end, two chaotic oscillators are coupled, and a set of inequality time dependent constraints with time dependent bounds is imposed on the generated chaotic signals. Moreover, to increase system complexity and its security level, the imposed set of constraints and their bounds are allowed to be changeable from one time period to another during the transmission process. As a result, the patterns of the generated chaotic signals are completely changed and the chaotic oscillator is completely encrypted. At the receiving end, the newly developed Constrained Smoothed Regularized Least Square (CSRLS) observer is used to synchronize the received constrained chaotic signals and hence retrieve the transmitted data. Using such an approach, the quality of the received information, measured by the Bit Error Rate (BER), is highly improved due to the superior performance of the developed CSRLS observer. The stability of the observer is analyzed, and simulation results are presented to show the efficiency and effectiveness of the proposed secure communication scheme.     

Stochastic stability and extended dissipativity analysis for delayed neural networks with markovian jump via novel integral inequality

This paper studies the stochastic stability and extended dissipativity analysis for delayed Markovian jump neural networks (MJNNs) with partly unknown transition rates (PUTRs) using novel integral inequality. A new double integral inequality with augmented vector is introduced through inequality technique and the zero-valued equality approach, which can more efficiently estimate the derivative of the triple integral inequality. Next, an augmented Lyapunov-Krasovskii functional (LKF) with delay-product-type (DPT) is constructed. Besides, with the introduced integral inequality, the augmented LKF and some other analytical techniques, some less conservative extended dissipation conditions are obtained in the form of linear matrix inequality (LMI). Finally, several examples are provided to illustrate the effectiveness of the obtained results.     

State quantized sampled-data control design for complex-valued memristive neural networks

In this paper, several resultful control schemes based on data quantization are proposed for complex-valued memristive neural networks (CVMNNs). Firstly, considering the finite communication resources and the interference of failures to the system, a state quantized sampled-data controller (SQSDC) is designed for CVMNNs. Next, taking the interference of gain fluctuations into account, a non-fragile sampled-data control (SDC) law is proposed for CVMNNs in the framework of data quantification. In order to full capture more inner sampling information, a newly Lyapunov-Krasovskii function (LKF) is constructed on the basis of the proposed triple integral inequality. After that, in the framework of taking full advantage of the property of Bessel-Legendre inequality, a time-dependent discontinuous LKF (TDDLKF) is proposed for CVMNNs with SQSDC. Based on the useful LKF, several stability criteria are established. Finally, the numerical simulations are provided to substantiate the validity and less conservatism of the proposed schemes.     

A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology

In this paper, a robust adaptive control scheme is proposed for the leader following control of a class of fractional-order multi-agent systems (FMAS). The asymptotic stability is shown by a linear matrix inequality (LMI) approach. The nonlinear dynamics of the agents are assumed to be unknown. Moreover, the communication topology among the agents is assumed to be unknown and time-varying. A deep general type-2 fuzzy system (DGT2FS) using restricted Boltzmann machine (RMB) and contrastive divergence (CD) learning algorithm is proposed to estimate uncertainties. The simulation studies presented indicate that the proposed control method results in good performance under time-varying topology, unknown dynamics and external disturbances. The effectiveness of the proposed DGT2FS is verified also on modeling problems with high dimensional real-world data sets.