Fully Distributed Event/Self-Triggered Bipartite Output Formation-Containment Tracking Control for Heterogeneous Multiagent Systems

This article considers the bipartite time-varying output formation-containment tracking control issue for general linear heterogeneous multiagent systems with multiple nonautonomous leaders, where the full states of agents are not available. Both cooperative interaction and antagonistic interaction between neighboring agents are taken into account. First, an observer is constructed using the output information to observe the state information. Then, based on the information between neighboring agents, an independent asynchronous fully distributed event-triggered bipartite compensator is put forward to estimate the convex hull spanned by the states of multiple leaders. Note that the compensator does not require to use of any global information. Subsequently, a formation-containment tracking control strategy based on the observer and compensator and an algorithm to determine its control parameters are given. The Zeno behavior is further proved to be excluded in any finite time. In addition, a novel self-triggered control strategy based only on the sampled information at triggering instants is also formulated, which avoids continuous communication among agents. Finally, a numerical example is given to validate the effectiveness and performance of the proposed control strategies.

H∞ Exponential Synchronization of Complex Networks: Aperiodic Sampled-Data-Based Event-Triggered Control

This article studies the H_∞ exponential synchronization problem for complex networks with quantized control input. An aperiodic sampled-data-based event-triggered scheme is introduced to reduce the network workload. Based on the discrete-time Lyapunov theorem, a new method is adopted to solve the sampled-data problem. In view of the aforementioned method, several sufficient conditions to ensure the H_∞ exponential synchronization are acquired. Numerical simulations show that the proposed control schemes can significantly reduce the amount of transmitted signals while preserving the desired system performance.

Data-Driven-Based Event-Triggered Control for Nonlinear CPSs Against Jamming Attacks

This brief considers the security control problem for nonlinear cyber-physical systems (CPSs) against jamming attacks. First, a novel event-based model-free adaptive control (MFAC) framework is established. Second, a multistep predictive compensation algorithm (PCA) is developed to make compensation for the lost data caused by jamming attacks, even consecutive attacks. Then, an event-triggering mechanism with the dead-zone operator is introduced in the adaptive controller, which can effectively save communication resources and reduce the calculation burden of the controller without affecting the control performance of systems. Moreover, the boundedness of the tracking error is ensured in the mean-square sense, and only the input/output (I/O) data are used in the whole design process. Finally, simulation comparisons are provided to show the effectiveness of our method.

Event-Triggered Stabilization of a Linear System With Model Uncertainty and i.i.d. Feedback Dropouts

This article investigates the input-to-state stability (ISS) of continuous-time networked control systems with model uncertainty and bounded noise based on event triggering. The feedback loop is closed over an unreliable digital communication network. Feedback packets suffer from network delay and may be dropped in an independent and identically distributed (i.i.d.) way, which may harm to the concerned stability. This article focuses on a Lyapunov-based event-triggered control design scheme with the consideration of i.i.d. packet dropouts. By designing a state-dependent event-triggering threshold and updating methods, it can still ensure ISS for the concerned multidimensional system in the presence of i.i.d. packet dropouts and model uncertainty without exhibiting the Zeno behavior. Simulations are done to verify the effectiveness of the achieved results.

Adaptive Fuzzy Output-Constrained Control for Nonlinear Stochastic Systems With Input Delay and Unknown Control Coefficients

This article considers an adaptive fuzzy control problem for nonstrict-feedback nonlinear stochastic systems, which contain input delay, output constraints, and unknown control coefficients, simultaneously. First, an original stochastic nonlinear mapping and the Pade approximation transformation techniques are developed to solve the symmetric output constraints and input delay. Then, an adaptive fuzzy controller is designed for the unknown nonlinear systems, in which the Nussbaum function is employed to deal with the unknown time-varying control coefficients. Tracking errors are ensured to converge to a small neighborhood around the origin, and the system output does not violate the predefined constrained conditions. All the signals of the closed-loop systems have proven to remain bounded in probability. Moreover, the asymmetric output-constrained control is also studied. Two simulation examples are provided to show the effectiveness of the developed method.

Transportation and swing reduction for double-pendulum tower cranes using partial enhanced-coupling nonlinear controller with initial saturation

Achieving both jib and trolley positioning and swing reduction become more difficult when tower cranes appear double-pendulum characteristic in dynamic. Moreover, in actual applications, the initial output value of actuators is usually physically constrained and the model uncertainties and external disturbances always exist. To solve these problems, a partial enhanced-coupling nonlinear controller with initial saturation is designed and analyzed by Lyapunov technique and LaSalle's invariance theorem. The control performance and strong robustness are severally verified by lots of simulations.

Bipartite Fixed-Time Output Consensus of Heterogeneous Linear Multiagent Systems

In this article, the bipartite fixed-time output consensus problem of heterogeneous linear multiagent systems (MASs) is investigated. First, a distributed bipartite fixed-time observer is proposed, by which the follower can estimate the leader's state. The estimate value is the same as the leader's state in modulus but may not in sign due to the existence of antagonistic interactions between agents. Then, an adaptive bipartite fixed-time observer is further proposed. It is fully distributed without involving any global information. This adaptive bipartite fixed-time observer can estimate not only the leader's system matrix but also the leader's state. Next, distributed nonlinear control laws are developed based on two observers, respectively, such that the bipartite fixed-time output consensus of heterogeneous linear MASs can be achieved. Moreover, the upper bound of the settling time is independent of initial states of agents. Finally, the examples are given to demonstrate the results.

Sufficient stabilizing bit rate conditions for an n-dimensional nonlinear system based on event triggering

This paper investigates the problem of stabilizing an n-dimensional nonlinear system whose feedback packets are transmitted via the digital network. In the presence of the bounded processing delay and network delay, the sampling time instant of a feedback packet is not the same as its receiving time instant. The larger difference between these time instants, the higher bit rate is required to stabilize the concerned system. Moreover, not only these delays, but also feedback dropouts and process noise will deteriorate the system performance. Thus we propose a model-based event-triggered control method to stabilize the system and save the occupied network bandwidth. Besides the transmission bits inside the feedback packets, their sampling time instants also convey the state information. The proposed event-triggered method can make full use of the feedback packets, especially their receiving time instant information. Compared with the periodic sampling method, our method performs better and requires lower stabilizing bit rates while the desired input-to-state stability can still be ensured. The obtained bit rate conditions only depend on the Lipschitz constant, the upper bounds of the delays and the dropout rate, and are independent of the bounded process noise.

A Novel Sliding Mode Control for a Class of Stochastic Polynomial Fuzzy Systems Based on SOS Method

In this paper, a novel robust controller for continuous stochastic polynomial fuzzy systems is investigated. The aim of the proposed method is to eliminate the restrictive assumptions that the local input matrix B_i must be uniform and the sliding mode surface proposed did not consider the stochastic perturbations, which are required in most existing results. At the same time, the proposed method could handle the system with matched external disturbances. First, a novel vector integral sliding mode surface (VISMS) is constructed according to the basis matrix [Formula: see text]. The sliding mode surface parameter matrix [Formula: see text] can be obtained through the provided sum of squares conditions. Second, by using an improved Lyapunov method and a new proposed lemma, a novel sliding mode control law is designed to keep the state of the closed-loop systems on the VISMS approximately since the initial time. Third, a practical example and a numerical one are provided to illustrate the validity of the proposed approach.

H∞ Consensus for Linear Heterogeneous Discrete-Time Multiagent Systems With Output Feedback Control

This paper studies the H_∞ consensus problem for linear heterogeneous discrete-time multiagent systems (MASs). For a special kind of nonlinear matrix inequality, a novel method is provided to make these inequalities turn into some equivalent conditions that can be solved by utilizing the LMI toolbox. According to this method, a necessary and sufficient condition of H_∞ consensus for linear heterogeneous discrete-time MAS with output feedback control scheme and a corresponding iterative algorithm are proposed, respectively. Moreover, by employing these results, a necessary and sufficient condition that makes a general linear system with one kind of constrained controller achieve H_∞ control is provided. Finally, two numerical examples are given to illustrate the validity of our schemes.

H∞ Consensus for Linear Heterogeneous Multiagent Systems Based on Event-Triggered Output Feedback Control Scheme

In this paper, the H_∞ consensus problem for linear heterogeneous multiagent systems (LHMAS) based on event-triggered output feedback control is investigated. Two novel event-triggered control schemes including nonperiodic and periodic event-triggered control approaches are provided to make the LHMAS with external unknown disturbance achieve H_∞ consensus. Therein, the nonperiodic event-triggered control method is designed by combining the event-triggered approach with time-triggered scheme, which can make the sampled instants be decided by the event-triggered condition and provide a fixed lower bound of sampled interval to avoid the Zeno-behavior. Then, based on this approach, the periodic event-triggered control scheme has a further improvement that only the information at fixed periodic interval is used for the event-triggered condition, which means that this method avoids the continuous-time information transfer. Besides, both schemes take output feedback control, which means that only the output information of each agent and leader is needed in this paper. Finally, a numerical example is given to support our results.

Exponential Synchronization for Delayed Dynamical Networks via Intermittent Control: Dealing With Actuator Saturations

Over the past two decades, the synchronization problem for dynamical networks has drawn significant attention due to its clear practical insight in biological systems, social networks, and neuroscience. In the case where a dynamical network cannot achieve the synchronization by itself, the feedback controller should be added to drive the network toward a desired orbit. On the other hand, the time delays may often occur in the nodes or the couplings of a dynamical network, and the existence of time delays may induce some undesirable dynamics or even instability. Moreover, in the course of implementing a feedback controller, the inevitable actuator limitations could downgrade the system performance and, in the worst case, destabilize the closed-loop dynamics. The main purpose of this paper is to consider the synchronization problem for a class of delayed dynamical networks with actuator saturations. Each node of the dynamical network is described by a nonlinear system with a time-varying delay and the intermittent control strategy is proposed. By using a combination of novel sector conditions, piecewise Lyapunov-like functionals and the switched system approach, delay-dependent sufficient conditions are first obtained under which the dynamical network is locally exponentially synchronized. Then, the explicit characterization of the controller gains is established by means of the feasibility of certain matrix inequalities. Furthermore, optimization problems are formulated in order to acquire a larger estimate of the set of initial conditions for the evolution of the error dynamics when designing the intermittent controller. Finally, two examples are given to show the benefits and effectiveness of the developed theoretical results.

Improved Stability and Stabilization Results for Stochastic Synchronization of Continuous-Time Semi-Markovian Jump Neural Networks With Time-Varying Delay

Continuous-time semi-Markovian jump neural networks (semi-MJNNs) are those MJNNs whose transition rates are not constant but depend on the random sojourn time. Addressing stochastic synchronization of semi-MJNNs with time-varying delay, an improved stochastic stability criterion is derived in this paper to guarantee stochastic synchronization of the response systems with the drive systems. This is achieved through constructing a semi-Markovian Lyapunov-Krasovskii functional together as well as making use of a novel integral inequality and the characteristics of cumulative distribution functions. Then, with a linearization procedure, controller synthesis is carried out for stochastic synchronization of the drive-response systems. The desired state-feedback controller gains can be determined by solving a linear matrix inequality-based optimization problem. Simulation studies are carried out to demonstrate the effectiveness and less conservatism of the presented approach.

Event-triggered fault detection for discrete-time T-S fuzzy systems

This paper is concerned with the design of piecewise fuzzy diagnostic observers for discrete-time T-S fuzzy systems under an event-triggered (ET) communication mechanism. Considering that the premise variables of the fuzzy diagnostic observer and the system may belong to different local space regions due to the introduction of ET mechanism, a partition method-based piecewise fuzzy diagnostic observer is designed to detect faults. The two-term approximation approach is introduced to approximate the time-varying delay. By transforming the augmented system into an input-output form consisting of two interconnected subsystems, the design condition of the piecewise fuzzy diagnostic observer is obtained by using the scaled small gain (SSG) theorem and a piecewise Lyapunov-Krasovskii functional. Furthermore, the L_∞/L₂ and L_∞ fault detection (FD) scheme is used to optimize the FD performance. Finally, two simulation examples are provided to show the efficiency of the proposed design method.

Global Asymptotic Stability and Stabilization of Neural Networks With General Noise

Neural networks (NNs) in the stochastic environment were widely modeled as stochastic differential equations, which were driven by white noise, such as Brown or Wiener process in the existing papers. However, they are not necessarily the best models to describe dynamic characters of NNs disturbed by nonwhite noise in some specific situations. In this paper, general noise disturbance, which may be nonwhite, is introduced to NNs. Since NNs with nonwhite noise cannot be described by Itô integral equation, a novel modeling method of stochastic NNs is utilized. By a framework in light of random field approach and Lyapunov theory, the global asymptotic stability and stabilization in probability or in the mean square of NNs with general noise are analyzed, respectively. Criteria for the concerned systems based on linear matrix inequality are proposed. Some examples are given to illustrate the effectiveness of the obtained results.

Design of nonlinear optimal control for chaotic synchronization of coupled stochastic neural networks via Hamilton-Jacobi-Bellman equation

This paper presents a new theoretical design of nonlinear optimal control on achieving chaotic synchronization for coupled stochastic neural networks. To obtain an optimal control law, the proposed approach is developed rigorously by using Hamilton-Jacobi-Bellman (HJB) equation, Lyapunov technique, and inverse optimality, and hence guarantees that the chaotic drive network synchronizes with the chaotic response network influenced by uncertain noise signals. Furthermore, the paper provides four numerical examples to demonstrate the effectiveness of the proposed approach.

Sampled-Data Synchronization of Markovian Coupled Neural Networks With Mode Delays Based on Mode-Dependent LKF

This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.

Network-Based Practical Consensus of Heterogeneous Nonlinear Multiagent Systems

This paper studies network-based practical leader-following consensus problem of heterogeneous multiagent systems with Lipschitz nonlinear dynamics under both fixed and switching topologies. Considering the effect of network-induced delay, a network-based leader-following consensus protocol with heterogeneous gain matrix is proposed for each follower agent. By employing Lyapunov-Krasovskii method, a sufficient condition for designing the network-based consensus controller gain is derived such that the leader-following consensus error exponentially converges to a bounded region under a fixed topology. Correspondingly, the proposed design approach is then extended to the case of switching topology. Two numerical examples with networked Chua's circuits are given to show the efficiency of the design method proposed in this paper.

Observer-Based Non-PDC Control for Networked T-S Fuzzy Systems With an Event-Triggered Communication

This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.

Adaptive control for a class of nonlinear complex dynamical systems with uncertain complex parameters and perturbations

In this paper, adaptive control is extended from real space to complex space, resulting in a new control scheme for a class of n-dimensional time-dependent strict-feedback complex-variable chaotic (hyperchaotic) systems (CVCSs) in the presence of uncertain complex parameters and perturbations, which has not been previously reported in the literature. In detail, we have developed a unified framework for designing the adaptive complex scalar controller to ensure this type of CVCSs asymptotically stable and for selecting complex update laws to estimate unknown complex parameters. In particular, combining Lyapunov functions dependent on complex-valued vectors and back-stepping technique, sufficient criteria on stabilization of CVCSs are derived in the sense of Wirtinger calculus in complex space. Finally, numerical simulation is presented to validate our theoretical results.

Robust H∞ state-feedback control for linear systems

This paper investigates the problem of robust H∞ control for linear systems. First, the state-feedback closed-loop control algorithm is designed. Second, by employing the geometric progression theory, a modified augmented Lyapunov-Krasovskii functional (LKF) with the geometric integral interval is established. Then, parameter uncertainties and the derivative of the delay are flexibly described by introducing the convex combination skill. This technique can eliminate the unnecessary enlargement of the LKF derivative estimation, which gives less conservatism. In addition, the designed controller can ensure that the linear systems are globally asymptotically stable with a guaranteed H∞ performance in the presence of a disturbance input and parameter uncertainties. A liquid monopropellant rocket motor with a pressure feeding system is evaluated in a simulation example. It shows that this proposed state-feedback control approach achieves the expected results for linear systems in the sense of the prescribed H∞ performance.

Dynamic IQC-Based Control of Uncertain LFT Systems With Time-Varying State Delay

This paper presents a new exact-memory delay control scheme for a class of uncertain systems with time-varying state delay under the integral quadratic constraint (IQC) framework. The uncertain system is described as a linear fractional transformation model including a state-delayed linear time-invariant (LTI) system and time-varying structured uncertainties. The proposed exact-memory delay controller consists of a linear state-feedback control law and an additional term that captures the delay behavior of the plant. We first explore the delay stability and the L₂ -gain performance using dynamic IQCs incorporated with quadratic Lyapunov functions. Then, the design of exact-memory controllers that guarantee desired L₂ -gain performance is examined. The resulting delay control synthesis conditions are formulated in terms of linear matrix inequalities, which are convex on all design variables including the scaling matrices associated with the IQC multipliers. The IQC-based exact-memory control scheme provides a novel approach for delay control designs via convex optimization, and advances existing control methods in two important ways: 1) better controlled performance and 2) simplified design procedure with less computational cost. The effectiveness and advantages of the proposed approach have been demonstrated through numerical studies.

Redesigned Predictive Event-Triggered Controller for Networked Control System With Delays

Event-triggered control (ETC) is a control strategy which can effectively reduce communication traffic in control networks. In the case where communication resources are scarce, ETC plays an important role in updating and communicating data. When network-induced delays are involved, two unsynchronized phenomena will appear if the existing ETC strategy, designed for networked control systems (NCSs) free of delays, is adopted. This paper deals with the ETC problem for NCS with delays existing in both sensor-to-controller and controller-to-actuator channels. A new predictive ETC strategy is proposed to solve both unsynchronized problems. It is shown that the stability of the resulting closed-loop system can be guaranteed under such an ETC strategy. Finally, both simulation studies and experimental tests are carried out to illustrate the proposed technique and verify its effectiveness.

Stability criteria for T-S fuzzy systems with interval time-varying delays and nonlinear perturbations based on geometric progression delay partitioning method

In this paper, a novel delay partitioning method is proposed by introducing the theory of geometric progression for the stability analysis of T-S fuzzy systems with interval time-varying delays and nonlinear perturbations. Based on the common ratio α, the delay interval is unequally separated into multiple subintervals. A newly modified Lyapunov-Krasovskii functional (LKF) is established which includes triple-integral terms and augmented factors with respect to the length of every related proportional subintervals. In addition, a recently developed free-matrix-based integral inequality is employed to avoid the overabundance of the enlargement when dealing with the derivative of the LKF. This innovative development can dramatically enhance the efficiency of obtaining the maximum upper bound of the time delay. Finally, much less conservative stability criteria are presented. Numerical examples are conducted to demonstrate the significant improvements of this proposed approach.

Mode-Dependent Stochastic Synchronization for Markovian Coupled Neural Networks With Time-Varying Mode-Delays

This paper investigates the stochastic synchronization problem for Markovian hybrid coupled neural networks with interval time-varying mode-delays and random coupling strengths. The coupling strengths are mutually independent random variables and the coupling configuration matrices are nonsymmetric. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is proposed, where some terms involving triple or quadruple integrals are considered, which makes the LKF matrices mode-dependent as much as possible. This gives significant improvement in the synchronization criteria, i.e., less conservative results can be obtained. In addition, by applying an extended Jensen's integral inequality and the properties of random variables, new delay-dependent synchronization criteria are derived. The obtained criteria depend not only on upper and lower bounds of mode-delays but also on mathematical expectations and variances of the random coupling strengths. Finally, two numerical examples are provided to demonstrate the feasibility of the proposed results.

State Estimation of Discrete-Time Takagi-Sugeno Fuzzy Systems in a Network Environment

In this paper, we investigate the H∞ filtering problem of discrete-time Takagi'Sugeno (T-S) fuzzy systems in a network environment. Different from the well used assumption that the normalized fuzzy weighting function for each subsystem is available at the filter node, we consider a practical case in which not only the measurement but also the premise variables are transmitted via the network medium to the filter node. For the network characteristics, we consider the multiple packet dropouts which are described by using a Markov chain. It is assumed that the filter uses the most recent packet. If there are packet dropouts occurring, the filter adopts the information for the last received packet. Suppose that the mode of the Markov chain is ordered according to the number of consecutive packet dropouts from zero to a preknown maximal value. For each mode of the Markov chain, it only has at most two jumping actions: 1) jump to the first mode and the current packet is transmitted successfully and 2) jump to the next mode and the number of consecutive packet dropouts increases by one. We aim to design mode-dependent and fuzzy-basis-dependent T-S fuzzy filter by using the transmitted packet subject to the described network issue. With the augmentation technique, we obtain a stochastic filtering error system in which the filter parameters and the Markovian jumping variable are all involved. A sufficient condition which guarantees the stochastic stability and the H∞ performance is derived with the Lyapunov method. Based on the sufficient condition, we propose the filter design method and the filter parameters can be determined by solving a set of linear matrix inequalities (LMIs). A tunnel-diode circuit in a network environment is presented to show the effectiveness and the advantage of the proposed design approach.

Network-Based Robust H₂/H∞ Control for Linear Systems With Two-Channel Random Packet Dropouts and Time Delays

This paper focuses on the robust output feedback H₂/H∞ control issue for a class of discrete-time networked control systems with uncertain parameters and external disturbance. Sensor-to-controller and controller-to-actuator packet dropouts and time delays are considered simultaneously. According to the stochastic characteristic of the packet dropouts and time delays, a model based on a Markov jump system framework is proposed to randomly compensate for the adverse effect of the two-channel packet dropouts and time delays. To analyze the robust stability of the resulting closed-loop system, a Lyapunov function is proposed, based on which sufficient conditions for the existence of the H₂/H∞ controller are derived in terms of linear matrix inequalities, ensuring robust stochastic stability as well as the prescribed H₂ and H∞ performance. Finally, an angular positioning system is exploited to demonstrate the effectiveness and applicability of the proposed design strategy.

H ∞ cluster synchronization for a class of neutral complex dynamical networks with Markovian switching

H_∞ cluster synchronization problem for a class of neutral complex dynamical networks (NCDNs) with Markovian switching is investigated in this paper. Both the retarded and neutral delays are considered to be interval mode dependent and time varying. The concept of H_∞ cluster synchronization is proposed to quantify the attenuation level of synchronization error dynamics against the exogenous disturbance of the NCDNs. Based on a novel Lyapunov functional, by employing some integral inequalities and the nature of convex combination, mode delay-range-dependent H_∞ cluster synchronization criteria are derived in the form of linear matrix inequalities which depend not only on the disturbance attenuation but also on the initial values of the NCDNs. Finally, numerical examples are given to demonstrate the feasibility and effectiveness of the proposed theoretical results.

Global adaptive output feedback control for a class of nonlinear time-delay systems

This paper addresses the problem of global output feedback control for a class of nonlinear time-delay systems. The nonlinearities are dominated by a triangular form satisfying linear growth condition in the unmeasurable states with an unknown growth rate. With a change of coordinates, a linear-like controller is constructed, which avoids the repeated derivatives of the nonlinearities depending on the observer states and the dynamic gain in backstepping approach and therefore, simplifies the design procedure. Using the idea of universal control, we explicitly construct a universal-type adaptive output feedback controller which globally regulates all the states of the nonlinear time-delay systems.

Sampled-data exponential synchronization of complex dynamical networks with time-varying coupling delay

This paper studies the problem of sampled-data exponential synchronization of complex dynamical networks (CDNs) with time-varying coupling delay and uncertain sampling. By combining the time-dependent Lyapunov functional approach and convex combination technique, a criterion is derived to ensure the exponential stability of the error dynamics, which fully utilizes the available information about the actual sampling pattern. Based on the derived condition, the design method of the desired sampled-data controllers is proposed to make the CDNs exponentially synchronized and obtain a lower-bound estimation of the largest sampling interval. Simulation examples demonstrate that the presented method can significantly reduce the conservatism of the existing results, and lead to wider applications.

Deviations in influenza seasonality: odd coincidence or obscure consequence?

In temperate regions, influenza typically arrives with the onset of colder weather. Seasonal waves travel over large spaces covering many climatic zones in a relatively short period of time. The precise mechanism for this striking seasonal pattern is still not well understood, and the interplay of factors that influence the spread of infection and the emergence of new strains is largely unknown. The study of influenza seasonality has been fraught with problems. One of these is the ever-shifting description of illness resulting from influenza and the use of both the historical definitions and new definitions based on actual isolation of the virus. The compilation of records describing influenza oscillations on a local and global scale is massive, but the value of these data is a function of the definitions used. In this review, we argue that observations of both seasonality and deviation from the expected pattern stem from the nature of this disease. Heterogeneity in seasonal patterns may arise from differences in the behaviour of specific strains, the emergence of a novel strain, or cross-protection from previously observed strains. Most likely, the seasonal patterns emerge from interactions of individual factors behaving as coupled resonators. We emphasize that both seasonality and deviations from it may merely be reflections of our inability to disentangle signal from noise, because of ambiguity in measurement and/or terminology. We conclude the review with suggestions for new promising and realistic directions with tangible consequences for the modelling of complex influenza dynamics in order to effectively control infection.