Abstract Designing controllers directly from measurement data has attracted growing attention in recent years, as it avoids the need for accurate system modeling or explicit system identification. This paper focuses on recent advances in data-driven control for linear discrete-time systems with unknown system matrices. For noisy input-state data, an in-depth analysis is provided on several representative approaches, including data-driven control based on Willems et al.’s fundamental lemma, quadratic matrix inequalities, linear fractional transformations for combining prior knowledge with data, and integral quadratic constraints. For noisy input-output data, a concise review is presented on control methods based on quadratic matrix inequalities, along with key insights into their structure and implications. The paper concludes by outlining several challenging problems that merit further investigation in future research.

Abstract This paper is concerned with event-triggered stabilization of a class of nonlinear networked systems. First, a novel event-triggered sampling scheme is proposed based on a monotonically increasing function in the form of the integral of the error norm between the current state and the latest triggered state. It is proven that this scheme ensures the minimum inter-event time to be strictly greater than zero in an explicit expression. Second, a novel Lyapunov functional tailored for the proposed event-triggered scheme is constructed. By applying the looped functional method, a sampling-interval-dependent stabilization criterion is derived. This criterion provides an algorithm to co-design both control gains and event-triggered parameters for a given threshold. Finally, a practical system comprising two identical pendulums is given to demonstrate the effectiveness of the proposed method.

This article investigates the security challenges associated with integrated attack-defense strategies for multiarea power systems (MAPSs). Traditional attack models often employ indiscriminate approaches that overlook the heterogeneous significance of different area subsystems. In practice, however, these areas contribute unequally to the overall stability and security of MAPSs. To address this gap, an area-significance-driven attack model is introduced, in which the importance of each area is quantitatively evaluated based on its contribution to global system stability and operational security. This targeted approach enables adversaries to maximize system disruption by selectively exploiting high-impact components. In parallel, an adaptive defensive control strategy is proposed to dynamically respond to the characteristics of detected attacks, thereby mitigating damage and enhancing system resilience. By coupling targeted attack modeling with adaptive defensive control, a unified framework is established that characterizes the interactive dynamics between adversarial threats and system responses in MAPSs. Finally, simulations on a three-area power system confirm the disruptive potential of area-significance-driven attacks and demonstrate the effectiveness of the proposed defense strategy in maintaining system-wide stability under adversarial conditions.

In this article, we investigate aggregative games with coupling constraints and local feasibility constraints over digraphs. It is noted that the imbalance introduced by the digraph leads to an inaccurate estimation of the global aggregation function and increases the difficulty of handling coupling constraints. To overcome this issue, a consensus dynamics is designed to estimate the right eigenvector associated with the zero eigenvalue of the Laplacian matrix constructed using the nodes’ out-degree information. By normalizing with the estimated right eigenvector, the imbalance is eliminated, enabling accurate estimation of the global aggregation function. Based on this, a distributed projection-based algorithm is developed, and through the aid of the proposed consensus dynamics, the asymptotic convergence to the Nash equilibrium (NE) is rigorously proven via singular perturbation theory. In particular, when either local feasibility constraints or coupling constraints are absent, the proposed algorithm achieves exponential convergence to the NE. Finally, the effectiveness of the proposed algorithms is validated through simulations on the location problem and Nash–Cournot games.

Abstract Event-triggered control (ETC) offers an efficient strategy for significantly reducing communication and computation resources in networked systems by triggering control updates only when necessary. This study provides an overview of recent advances in ETC. First, data-driven (or model-free) ETC, which has gained significant attention in recent years, is reviewed for linear systems with and without unknown disturbances. Second, co-design issues are deeply analyzed for both state feedback and dynamic output feedback control. Third, the separation principle is thoroughly examined in the context of event-triggered observer-based output feedback control. Fourth, some insightful discussions are made on the ideal execution property of event-triggered schemes, as well as the modeling of ETC under packet dropouts. Finally, several challenging issues for future research are outlined.

This paper addresses the passivity issue of neural networks with a time-varying delay. We introduce an allowable delay set flexible fragmentation approach for constructing a novel Lyapunov–Krasovskii functional (LKF). Unlike some existing methods, this LKF is more flexible as it allows for different Lyapunov matrices in different allowable delay subsets. Based on the proposed LKF, and utilizing integral inequality approaches and zero equation techniques, several passivity criteria are derived for neural networks with a time-varying delay. Two numerical examples are finally provided to demonstrate the advantages of the proposed method.

This study addresses the problem of event-triggered and privacy-preserved platooning control of connected automated vehicles with finite communication resources and data privacy constraints. To efficiently use the communication resources, an asynchronous edge-based dynamic event-triggered mechanism that features adaptive edge-related triggering parameters is designed. Such a design allows for dynamic scheduling of the inter-vehicle communication on a per-edge basis while avoiding the Zeno behavior. Privacy of transmitted vehicular data is then protected through a novel hybrid privacy-preserving strategy that combines output masking with matrix transformation. Subsequently, a set of event-triggered adaptive distributed estimators with guaranteed privacy is developed to facilitate each follower vehicle’s accurate estimation of the full leader motion state. The state estimates are then employed in the design of neural adaptive platoon controllers such that each follower vehicle in the platoon follows the leader with synchronized speed and acceleration under a refined constant time headway spacing policy. Tractable design criteria for admissible estimator and controller gains as well as triggering and learning parameters, are further derived. Finally, co-simulations using CarSim and MATLAB/Simulink are performed to validate the effectiveness of the derived results.

This paper addresses the problem of stabilizing aperiodic sampled-data systems using output feedback control. First, a novel two-sided looped functional is constructed, providing a sufficient condition to ensure the asymptotic stability of the resulting closed-loop system. This condition is then adapted for control design. Using a cone complementary linearization algorithm with a stringent iteration criterion, a practical approach is developed to compute the control gain. Finally, three examples, including an inverted pendulum system and a one-area load-frequency control system, demonstrate the effectiveness and superiority of the proposed method over others in the literature.

This paper presents an adaptive dynamic surface control (DSC) method for nonlinear systems with multiple disturbances, parameter uncertainties, and unknown nonlinear dynamics, using a reduced-order extended state observer (ROESO). Compared to related methods, this proposed approach offers several distinct advantages: (i) The method does not require knowledge of the upper bound function for unknown nonlinear dynamics, allowing the controlled plant to not be bounded-input bounded-state, thus expanding the applicability of the DSC method; (ii) By utilizing known model information, an ROESO is designed to handle mismatched uncertainties and disturbances, reducing the load on the observer and enhancing its capability to suppress unknown nonlinear dynamics; (iii) This method employs an adaptive output-feedback DSC approach, which is more practical and easier to implement than state-feedback methods; and (iv) The observer gain, adaptive gain, and DSC gain are simultaneously optimized using a particle swarm optimization algorithm. Additionally, detailed stability analysis is provided, and simulations and comparative experiments are conducted on a rotational system to demonstrate the efficacy and superiority of the proposed method. Note to Practitioners—Most real systems are nonlinear and subject to a variety of matched and unmatched uncertainties and disturbances. Adaptive control is an effective method for dealing with parametric uncertainty, but requires that the uncertainty can be represented linearly in terms of unknown parameters and cannot handle exogenous disturbances. As an alternative active disturbance attenuation method, disturbance/uncertainty estimation and attenuation techniques have a two-degree-of-freedom control structure. However, for nonlinear systems, they simply estimate and compensate for the nonlinear characteristics of the system as disturbances, which, although effective, can lead to problems such as excessive inputs in the actual control, thus affecting the performance of the system. Backstepping control is one of the most powerful tools for handling nonlinear systems to deal with mismatched disturbances and has been well used in various fields, but it usually suffers from the problem of “explosion of complexity”. Thus, how to deal with various matched and unmatched uncertainties and disturbances in a nonlinear system is still a challenge. Motivated by these considerations, this paper presents an adaptive output-feedback dynamic surface control method for a class of uncertain nonlinear systems with multiple mismatched uncertainties and disturbances.

This article investigates the problems of exponential stability and dissipativity for neural networks with time-varying delays. To capture more information on the delay and its derivative in constructing Lyapunov-Krasovskii functionals (LKFs), the original delayed neural network (DNN) is modeled as a switching system with two modes, corresponding to cases where the delay derivative is positive or negative. This model provides extra freedom in constructing a proper LKF, allowing for the selection of different Lyapunov matrices in each mode. By applying the average dwell time (ADT) technique, several criteria for exponential stability and exponential dissipativity are obtained for DNNs. Two extensively studied benchmark examples and a quadruple-tank process control system are provided to demonstrate the superiority of the proposed criteria over some existing methods and to verify the practical applicability of the approach.

Spectrum sharing technology is used to alleviate the tension and scarcity of spectrum resources, and federated learning can significantly enhance the performance of tasks such as incumbent detection and improve the quality of spectrum sharing. However, spectrum sharing methods based on federated learning still face challenges such as large-scale data transmission and the lack of privacy protection for sensing nodes. To tackle these issues, in this paper, we propose a compressed sensing (CS) based transmission framework that integrates efficient aggregation and privacy protection. In particular, a multiple measurement vector (MMV)-CS model is used for efficient aggregation between the central server and sensing nodes. By designing different measurement vectors, local environment sensing nodes can be divided into different clusters, forming multiple superimposed transmission signals at the central server. Thus, the central server will obtain the aggregated information of local models from different clusters, completing the optimization of the global model in federated learning. In this process, the efficiency of data aggregation has been greatly improved, and the data privacy of individual environment sensing nodes is protected. The security analysis and simulation results are provided to validate the effectiveness of the proposed schemes. The detection performance of the proposed method is as good as that of the approach under the raw training samples, while the privacy-preserving and communication efficiency are significantly improved.

This paper concerns the tracking control problem of nonlinear networked control systems (NCSs) considering external disturbance and random package dropouts. The sensor samples system states periodically. The sampled data packages may have dropouts while being transmitted through communication networks. Notice that the number of consecutive lost packages is usually lower and upper bound. Then, an aperiodically sampled data model is employed to describe such a networked control system. By utilizing an augmented state approach, the tracking control problem is transformed into the stabilization for the augmented system. A new two-sided-looped functional (TSLF) is introduced, and the system state and the sampling pattern are fully encountered. Sufficient conditions are derived to design controllers, presented as a series of linear-matrix inequalities (LMIs). A surface-mounted permanent magnet synchronous motor (SMPMSM) model and Rossler's chaotic system are provided to demonstrate the effectiveness of the proposed method.

The rapid development of artificial intelligence (AI) has brought about revolutionary changes to industrial wireless networks. Meanwhile, these AI models have also incurred a more complex security environment. This article will investigate the new situations that may arise in the industrial radio security under the support of AI technologies. First, typical radio threats and the uniqueness of industrial wireless networks are introduced. We then review existing industrial wireless physical-layer security schemes based on various AI models from the perspective of countering these radio threats. From the attackers' perspective, three typical case studies are introduced, in which AI technologies will aid in jamming, spoofing, and eavesdropping attacks. Finally, we discussed the openness issues and potential solutions in industrial radio security. This article is of significant in understanding the current status of AI-based industrial radio security, as well as the main problems and challenges. This investigation can promote the healthy development of smart factories and future industries.

This article deals with the problem of stability and stabilization of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay. First, a novel Lyapunov–Krasovskii functional is constructed, which is dependent on membership functions and takes more information on the time-varying delay into account. Next, based on an$N$-order free-matrix-based integral inequality and a switching method, a cluster of criteria on the stability and stabilization are obtained for the closed-loop system connected with switching state-feedback controllers. Then, a parameter tuning method and an iterative algorithm are devised to calculate control gains. Finally, three numerical examples including the truck-trailer system are given to show that the proposed criteria can offer less conservative results than some existing ones.

This article deals with the stability of neural networks (NNs) with time-varying delay. First, a generalized reciprocally convex inequality (RCI) is presented, providing a tight bound for reciprocally convex combinations. This inequality includes some existing ones as special case. Second, in order to cater for the use of the generalized RCI, a novel Lyapunov-Krasovskii functional (LKF) is constructed, which includes a generalized delay-product term. Third, based on the generalized RCI and the novel LKF, several stability criteria for the delayed NNs under study are put forward. Finally, two numerical examples are given to illustrate the effectiveness and advantages of the proposed stability criteria.

This article is concerned with the extended dissipativity of discrete-time neural networks (NNs) with time-varying delay. First, the necessary and sufficient condition on matrix-valued polynomial inequalities reported recently is extended to a general case, where the variable of the polynomial does not need to start from zero. Second, a novel Lyapunov functional with a delay-dependent Lyapunov matrix is constructed by taking into consideration more information on nonlinear activation functions. By employing the Lyapunov functional method, a novel delay and its variation-dependent criterion are obtained to investigate the effects of the time-varying delay and its variation rate on several performances, such as$H_\infty $performance, passivity, and$l_{2}-l_\infty $performance, of a delayed discrete-time NN in a unified framework. Finally, a numerical example is given to show that the proposed criterion outperforms some existing ones.

Metaverse allows the physical reality to tightly integrate with the digital universe. As one typical metaverse application, platooning control of vehicular cyber–physical systems has attracted extensive attention as it is beneficial to improve traffic efficiency, driving safety, and emission reduction. However, due to the open nature of wireless communication networks, the transmitted vehicle-to-vehicle (V2V) data packets become exposed to the public and concomitant data leakage can lead to unintended consequences to vehicular platoons. This article is concerned with the privacy-preserving platooning control issue of vehicular cyber–physical systems with input saturations. First, a novel distributed proportional-integral observer is proposed to estimate the full state of each vehicle, where the integral terms with a forgetting factor facilitate to realize the tradeoff between transient performance and steady-state performance for the platoon. Second, sampled-data-based dynamic encryption and decryption schemes, featuring a dynamic private key, are developed such that the encrypted and decrypted V2V data can be kept private to each platoon vehicle. It is then shown that the platooning control problem over a generic communication topology can be cast into the stability issue of an auxiliary dynamic system. Furthermore, sufficient conditions on the existence of the desired observer and controller gains as well as the private key parameter selection are derived to guarantee the desired platoon stability and privacy preservation requirements. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed control method.

This article investigates the problem of delay-dependent stability for the one-area load frequency control (LFC) system with electric vehicles (EVs). Two closed-loop models of the LFC system with EVs are proposed, including the model based on the model reconstructed technique and the model with uncertain parameters that considers state of charge. By employing the Lyapunov-Krasovskii functional method, two delay-dependent stability criteria are presented for the systems under study such that a more accurate admissible delay upper bound (ADUB) can be obtained. Case studies are finally carried out to disclose the interrelationship between the ADUB, PI controller gains, and other parameters of the EVs.

This brief is concerned with the stability of a neural network with a time-varying delay using the quadratic function negative-definiteness approach reported recently. A more general reciprocally convex combination inequality is taken to introduce some quadratic terms into the time derivative of a Lyapunov-Krasovskii (L-K) functional. As a result, the time derivative of the L-K functional is estimated by a novel quadratic function on the time-varying delay. Moreover, a simple way is introduced to calculate the coefficients of a quadratic function, which avoids tedious works by hand as done in some studies. The L-K functional approach is applied to derive a hierarchical type stability criterion for the delayed neural networks, which is of less conservatism in comparison with some existing results through two well-studied numerical examples.

The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic deception attacks modeled by two independent Markov processes. Two discrete event-triggered mechanisms are introduced for both channels to reduce the number of data transmission through the communication channels. To comply with practical point of view, static output feedback is utilized. By employing the Lyapunov-Krasovskii functional method, some sufficient conditions on the synchronization of master-slave neural networks are derived in terms of linear matrix inequalities, which make it easy to design suitable output feedback controllers. Finally, a numerical example is presented to show the effectiveness of the proposed method.

This paper is concerned with position-based synchronization of networked harmonic oscillators. Note that synchronization cannot be achieved via current-position-based protocols. The objective of this paper is to investigate the positive effects of network-induced delays on the synchronization of networked harmonic oscillators. That is, if taking network-induced delays into account, the motion of harmonic oscillators can be really synchronized via a proper position-based control protocol. In doing so, the harmonic oscillators are connected via a shared digital communication network. Different from some existing results, system measurements from oscillator nodes are sampled in an asynchronous way; and network-induced delays are assumed to be time varying and bounded, and they do not need to be synchronous with those from the other communication channels. At each oscillator node, a buffer is embedded into the controller to store the newest sampled-data packets transmitted from its neighboring nodes through communication channels. Then, based on the store of the buffer, the controller computes its control signal with its own period. As a result, the overall synchronization error system is modeled as a linear system with multiple interval time-varying delays. By employing the discretized Lyapunov-Krasovskii functional method, a sufficient condition on synchronization of networked harmonic oscillators is derived, which can ensure that the synchronization error system is asymptotically stable for network-induced delays falling into a certain closed interval whose lower bound is a positive real number. This condition is thus used to design suitable control protocols in terms of linear matrix inequalities with several tuning parameters. Finally, a multirobot platform is given to demonstrate the effectiveness of the proposed method.

This article addresses the problem of cluster consensus for multiagent systems (MASs) associated with weighted antagonistic interactions. Compared with some existing results, a general communication topology among agents is introduced in this article, where there is no need to confine directed cycles to the root of a spanning tree. By taking into consideration the structures of directed cycles and multiple paths, the judgment of structural balance is simplified significantly. A novel cluster consensus protocol is also proposed for structurally unbalanced digraphs. Moreover, two necessary and sufficient conditions are derived, by which cluster consensus can be achieved in an MAS if and only if its communication topology contains a directed spanning tree. Then, by employing an algebra theorem, a sufficient criterion for the unstable system under a directed cycle is obtained, whether the number of agents on this cycle is odd or even. Some illustrative examples are given to demonstrate the effectiveness of theoretical results.

This article investigates H∞synchronization of delayed neural networks under a receding horizon scheme, where two types of interval time-varying delays are considered according to whether the lower bound of the delay derivative is known or not. Note that a receding horizon synchronization law can be regarded as an optimization solution at each timeslot to a minimaxization problem related closely with a certain cost functional. In this article, two cost functionals with some delay-dependent matrices are introduced, respectively, for the two types of time delays. In order to obtain less conservative conditions, a novel inequality on quadratic polynomial functions is established, which includes some existing ones as its special cases. Based on the novel inequality, two sufficient conditions are derived to design the terminal weighting matrices of the cost functionals such that the resulting synchronization error system can be stabilized with a prescribed infinite horizon H∞performance level. Finally, three numerical examples are used to demonstrate the validity of the proposed results.

An event-triggered mechanism is of great efficiency in reducing unnecessary sensor samplings/transmissions and, thus, resource consumption such as sensor power and network bandwidth, which makes distributed event-triggered estimation a promising resource-aware solution for sensor network-based monitoring systems. This paper provides a survey of recent advances in distributed event-triggered estimation for dynamical systems operating over resource-constrained sensor networks. Local estimates of an unavailable state signal are calculated in a distributed and collaborative fashion based on only invoked sensor data. First, several fundamental issues associated with the design of distributed estimators are discussed in detail, such as estimator structures, communication constraints, and design methods. Second, an emphasis is laid on recent developments of distributed event-triggered estimation that has received considerable attention in the past few years. Then, the principle of an event-triggered mechanism is outlined and recent results in this subject are sorted out in accordance with different event-triggering conditions. Third, applications of distributed event-triggered estimation in practical sensor network-based monitoring systems including distributed grid-connected generation systems and target tracking systems are provided. Finally, several challenging issues worthy of further research are envisioned.

This article is concerned with designing resilient state feedback controllers for a class of networked control systems under denial-of-service (DoS) attacks. The sensor samples system states periodically. The DoS attacks usually prevent those sampled signals from being transmitted through a communication network. A logic processor embedded in the controller is introduced to not only receive sampled signals but also capture information on the duration time of each DoS attack. Note that the duration time of DoS attacks is usually both lower and upper bounded. Then the closed-loop system is modeled as an aperiodic sampled-data system closely related to both lower and upper bounds of duration time of DoS attacks. By introducing a novel looped functional, which caters for the N -order canonical Bessel-Legendre inequalities, some N -dependent stability criteria are presented for the resultant closed-loop system. It is worth pointing out that a number of identity formulas are uncovered, which enable us to apply the notable free-weighting matrix approach to derive less conservative stability criteria. A linear-matrix-inequality-based criterion is provided to design stabilizing state-feedback controllers against DoS attacks. A satellite control system is given to demonstrate the effectiveness of the proposed method.

This paper is concerned with passivity of a class of delayed neural networks. In order to derive less conservative passivity criteria, two Lyapunov-Krasovskii functionals (LKFs) with delay-dependent matrices are introduced by taking into consideration a second-order Bessel-Legendre inequality. In one LKF, the system state vector is coupled with those vectors inherited from the second-order Bessel-Legendre inequality through delay-dependent matrices, while no such coupling of them exists in the other LKF. These two LKFs are referred to as the coupled LKF and the noncoupled LKF, respectively. A number of delay-dependent passivity criteria are derived by employing a convex approach and a nonconvex approach to deal with the square of the time-varying delay appearing in the derivative of the LKF. Through numerical simulation, it is found that: 1) the coupled LKF is more beneficial than the noncoupled LKF for reducing the conservatism of the obtained passivity criteria and 2) the passivity criteria using the convex approach can deliver larger delay upper bounds than those using the nonconvex approach.

This paper focuses on the network-based modeling and proportional-integral (PI) control for a continuous-time direct-drive-wheel system in a wireless network environment. The developed system can simplify configuration, reduce bus cables, and realize vehicle height adjustment. A novel network-based model is first established by constructing a PI control system and taking network-induced delays and stochastic packet dropouts into account. By using two different artificial delays to characterize the update of proportional and integral control signals, the network-based PI control system is modeled as a stochastic impulsive system with two input delays and reset equations at updating instants. Then, through involving the reset states and the relationship among two delayed states and the current state in the discontinuous Lyapunov-Krasovskii functional and actively introducing the upper bounds of nonzero network-induced delays, some exponential mean-square stability and H∞performance conditions with less conservatism are derived in terms of tractable linear matrix inequalities. An algorithm is presented to determine the minimum H∞performance and the corresponding PI control parameters by combining a particle swarm optimization technique with the performance condition. These results can be extended to a network-based PI control of general continuous-time linear systems. A ZigBee-based network simulation platform is finally built and some simulation results are provided to validate the proposed methods.

Distributed coordination control is the current trend in networked systems and finds prosperous applications across a variety of fields, such as smart grids and intelligent transportation systems. One fundamental issue in coordinating and controlling a large group of distributed and networked agents is the influence of intermittent interagent interactions caused by constrained communication resources. Event-triggered communication scheduling stands out as a promising enabler to strike a balance between the desired control performance and the satisfactory resource efficiency. What distinguishes dynamic event-triggered scheduling from traditional static event-triggered scheduling is that the triggering mechanism can be dynamically adjusted over time in accordance with both available system information and additional dynamic variables. This article provides an up-to-date overview of dynamic event-triggered distributed coordination control. The motivation of dynamic event-triggered scheduling is first introduced in the context of distributed coordination control. Then some techniques of dynamic event-triggered distributed coordination control are discussed in detail. Implementation and design issues are well addressed. Furthermore, this article exemplifies two applications of dynamic event-triggered distributed coordination control in the fields of microgrids and automated vehicles. Several challenges are suggested to direct the future research.

This paper is concerned with active power sharing and frequency regulation in an islanded microgrid under event-triggered communication. A distributed secondary control scheme with a sampled-data-based event-triggered communication mechanism is proposed to achieve active power sharing and frequency regulation in a unified framework, where neighborhood sampled-data exchange occurs only when the predefined triggering condition is violated. Compared with traditional periodic communication mechanisms, the proposed event-triggered communication mechanism shows some prominent ability in reducing the number of communication among neighbors while guaranteeing the desired performance level of microgirds. By employing the Lyapunov-Kravovskii functional method, some sufficient conditions are derived to characterize the effects of control gains, system parameters, and sampling period on stability of microgrids. Finally, case studies on a modified IEEE 34-bus test system are conducted to evaluate the performance of the proposed distributed control scheme, showcasing its effectiveness, robustness against load changes, and plug-and-play ability.

Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in event-triggered consensus of MASs. First, a basic framework of multiagent event-triggered operational mechanisms is established. Second, representative results and methodologies reported in the literature are reviewed and some in-depth analysis is made on several event-triggered schemes, including event-based sampling schemes, model-based event-triggered schemes, sampled-data-based event-triggered schemes, and self-triggered sampling schemes. Third, two examples are outlined to show applicability of event-triggered consensus in power sharing of microgrids and formation control of multirobot systems, respectively. Finally, some challenging issues on event-triggered consensus are proposed for future research.

This paper is concerned with energy-to-peak state estimation on static neural networks (SNNs) with interval time-varying delays. The objective is to design suitable delay-dependent state estimators such that the peak value of the estimation error state can be minimized for all disturbances with bounded energy. Note that the Lyapunov-Krasovskii functional (LKF) method plus proper integral inequalities provides a powerful tool in stability analysis and state estimation of delayed NNs. The main contribution of this paper lies in three points: 1) the relationship between two integral inequalities based on orthogonal and nonorthogonal polynomial sequences is disclosed. It is proven that the second-order Bessel-Legendre inequality (BLI), which is based on an orthogonal polynomial sequence, outperforms the second-order integral inequality recently established based on a nonorthogonal polynomial sequence; 2) the LKF method together with the second-order BLI is employed to derive some novel sufficient conditions such that the resulting estimation error system is globally asymptotically stable with desirable energy-to-peak performance, in which two types of time-varying delays are considered, allowing its derivative information is partly known or totally unknown; and 3) a linear-matrix-inequality-based approach is presented to design energy-to-peak state estimators for SNNs with two types of time-varying delays, whose efficiency is demonstrated via two widely studied numerical examples.

This paper is concerned with the problem of robust ${H}_{\infty}$ control of an uncertain discrete-time Takagi-Sugeno fuzzy system with an interval-like time-varying delay. A novel finite-sum inequality-based method is proposed to provide a tighter estimation on the forward difference of certain Lyapunov functional, leading to a less conservative result. First, an auxiliary vector function is used to establish two finite-sum inequalities, which can produce tighter bounds for the finite-sum terms appearing in the forward difference of the Lyapunov functional. Second, a matrix-based quadratic convex approach is employed to equivalently convert the original matrix inequality including a quadratic polynomial on the time-varying delay into two boundary matrix inequalities, which delivers a less conservative bounded real lemma (BRL) for the resultant closed-loop system. Third, based on the BRL, a novel sufficient condition on the existence of suitable robust ${H}_{\infty}$ fuzzy controllers is derived. Finally, two numerical examples and a computer-simulated truck-trailer system are provided to show the effectiveness of the obtained results.

This paper is concerned with global asymptotic stability of delayed neural networks. Notice that a Bessel-Legendre inequality plays a key role in deriving less conservative stability criteria for delayed neural networks. However, this inequality is in the form of Legendre polynomials and the integral interval is fixed on . As a result, the application scope of the Bessel-Legendre inequality is limited. This paper aims to develop the Bessel-Legendre inequality method so that less conservative stability criteria are expected. First, by introducing a canonical orthogonal polynomial sequel, a canonical Bessel-Legendre inequality and its affine version are established, which are not explicitly in the form of Legendre polynomials. Moreover, the integral interval is shifted to a general one . Second, by introducing a proper augmented Lyapunov-Krasovskii functional, which is tailored for the canonical Bessel-Legendre inequality, some sufficient conditions on global asymptotic stability are formulated for neural networks with constant delays and neural networks with time-varying delays, respectively. These conditions are proven to have a hierarchical feature: the higher level of hierarchy, the less conservatism of the stability criterion. Finally, three numerical examples are given to illustrate the efficiency of the proposed stability criteria.

Fixed-time cooperative control is currently a hot research topic in multiagent systems since it can provide a guaranteed settling time, which does not depend on initial conditions. Compared with asymptotic cooperative control algorithms, fixed-time cooperative control algorithms can achieve better closed-loop performance and disturbance rejection properties. Different from finite-time control, fixed-time cooperative control produces the faster rate of convergence and provides an explicit estimation of the settling time independent of initial conditions, which is desirable for multiagent systems. This paper aims at presenting an overview of recent advances in fixed-time cooperative control of multiagent systems. Some fundamental concepts about finite- and fixed-time stability and stabilization are first recalled with insight understanding. Then, recent results in finite- and fixed-time cooperative control are reviewed in detail and categorized according to different agent dynamics. Finally, this paper raises several challenging issues that need to be addressed in the near future.

This brief is concerned with the problem of neural state estimation for static neural networks with time-varying delays. Notice that a Luenberger estimator can produce an estimation error irrespective of the neuron state trajectory. This brief provides a method for designing such an estimator for static neural networks with time-varying delays. First, in-depth analysis on a well-used reciprocally convex approach is made, leading to an improved reciprocally convex inequality. Second, the improved reciprocally convex inequality and some integral inequalities are employed to provide a tight upper bound on the time-derivative of some Lyapunov-Krasovskii functional. As a result, a novel bounded real lemma (BRL) for the resultant error system is derived. Third, the BRL is applied to present a method for designing suitable Luenberger estimators in terms of solutions of linear matrix inequalities with two tuning parameters. Finally, it is shown through a numerical example that the proposed method can derive less conservative results than some existing ones.

This paper is concerned with global asymptotic stability of a neural network with a time-varying delay, where the delay function is differentiable uniformly bounded with delay-derivative bounded from above. First, a general reciprocally convex inequality is presented by introducing some slack vectors with flexible dimensions. This inequality provides a tighter bound in the form of a convex combination than some existing ones. Second, by constructing proper Lyapunov-Krasovskii functional, global asymptotic stability of the neural network is analyzed for two types of the time-varying delays depending on whether or not the lower bound of the delay derivative is known. Third, noticing that sufficient conditions on stability from estimation on the derivative of some Lyapunov-Krasovskii functional are affine both on the delay function and its derivative, allowable delay sets can be refined to produce less conservative stability criteria for the neural network under study. Finally, two numerical examples are given to substantiate the effectiveness of the proposed method.

This brief is concerned with global asymptotic stability of a neural network with a time-varying delay. First, by introducing an auxiliary vector with some nonorthogonal polynomials, a slack-matrix-based integral inequality is established, which includes some existing one as its special case. Second, a novel Lyapunov-Krasovskii functional is constructed to suit for the use of the obtained integral inequality. As a result, a less conservative stability criterion is derived, whose effectiveness is finally demonstrated through two well-used numerical examples.

This paper is concerned with the state estimation for neural networks with two additive time-varying delay components. Three cases of these two time-varying delays are fully considered: 1) both delays are differentiable uniformly bounded with delay-derivative bounded by some constants; 2) one delay is continuous uniformly bounded while the other is differentiable uniformly bounded with delay-derivative bounded by certain constants; and 3) both delays are continuous uniformly bounded. First, an extended reciprocally convex inequality is introduced to bound reciprocally convex combinations appearing in the derivative of some Lyapunov-Krasovskii functional. Second, sufficient conditions are derived based on the extended inequality for three cases of time-varying delays, respectively. Third, a linear-matrix-inequality-based approach with two tuning parameters is proposed to design desired Luenberger estimators such that the error system is globally asymptotically stable. This approach is then applied to state estimation on neural networks with a single interval time-varying delay. Finally, two numerical examples are given to illustrate the effectiveness of the proposed method.

This paper provides an overview and makes a deep investigation on sampled-data-based event-triggered control and filtering for networked systems. Compared with some existing event-triggered and self-triggered schemes, a sampled-data-based event-triggered scheme can ensure a positive minimum inter-event time and make it possible to jointly design suitable feedback controllers and event-triggered threshold parameters. Thus, more attention has been paid to the sampled-data-based event-triggered scheme. A deep investigation is first made on the sampled-data-based event-triggered scheme. Then, recent results on sampled-data-based event-triggered state feedback control, dynamic output feedback control, H∞filtering for networked systems are surveyed and analyzed. An overview on sampled-data-based event-triggered consensus for distributed multiagent systems is given. Finally, some challenging issues are addressed to direct the future research.

This paper is concerned with networked output feedback H∞control of an offshore structure under earthquakes. With a networked model of the offshore structure, a networked output feedback H∞control strategy is proposed to suppress seismic vibration of the structure. Then, some delay-dependent stability criteria for the offshore structure system are derived. Simulation results show that compared with traditional network-free controllers, the networked output feedback H∞controller can mitigate vibration of the offshore structure and control cost to a smaller level.

This paper is concerned with stability of a linear system with a time-varying delay. The direct Lyapunov method is a powerful tool for studying stability of the system. Note that a tighter estimation on the time derivative of some Lyapunov-Krasovskii functional usually leads to a less conservative stability criterion. First, by introducing an auxiliary vector-valued function, this paper proposes a novel integral inequality, which can provide a tighter estimation on the integral term appearing in the time derivative of the Lyapunov-Krasovskii functional. Second, by introducing an augmented Lyapunov-Krasovskii functional, this novel integral inequality is employed to derive a stability criterion for the system with a time-varying delay. Finally, it is shown through a well-studied numerical example that the proposed stability criterion outperforms those using the IQC approach, the quadratic separation approach, the Wirtinger-based integral inequality approach and the free-matrix-based integral inequality approach.

This paper provides a brief overview of delayed feedback control schemes for offshore structures. Two simplified models of offshore steel jacket structures are listed first. Then based on the dynamic models of offshore structures, several active control strategies including delayed H∞control and delayed integral sliding mode control by using pure delayed signals and using current as well as delayed signals of the systems are outlined, respectively. Finally, some challenging problems and potential research directions about delayed feedback control for offshore structures are presented.

This paper is concerned with decentralized event-triggered dissipative control for systems with the entries of the system outputs having different physical properties. Depending on these different physical properties, the entries of the system outputs are grouped into multiple nodes. A number of sensors are used to sample the signals from different nodes. A decentralized event-triggering scheme is introduced to select those necessary sampled-data packets to be transmitted so that communication resources can be saved significantly while preserving the prescribed closed-loop performance. First, in order to organize the decentralized data packets transmitted from the sensor nodes, a data packet processor (DPP) is used to generate a new signal to be held by the zero-order-hold once the signal stored by the DPP is updated at some time instant. Second, under the mechanism of the DPP, the resulting closed-loop system is modeled as a linear system with an interval time-varying delay. A sufficient condition is derived such that the closed-loop system is asymptotically stable and strictly (Q0, S0, R0)-dissipative, where Q0, S0, and R0are real matrices of appropriate dimensions with Q0and R0symmetric. Third, suitable output-based controllers can be designed based on solutions to a set of a linear matrix inequality. Finally, two examples are given to demonstrate the effectiveness of the proposed method.

Networked control systems (NCSs) are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators, and so on). NCSs have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical development of NCSs. In-depth analysis and discussion is made on sampled-data control, networked control, and event-triggered control. More specifically, existing research methods on NCSs are summarized. Furthermore, as an active research topic, network-based filtering is reviewed briefly. Finally, some challenging problems are presented to direct the future research.

This paper is concerned with event-triggered generalized dissipativity filtering for a neural network (NN) with a time-varying delay. The signal transmission from the NN to its filter is completed through a communication channel. It is assumed that the network measurement of the NN is sampled periodically. An event-triggered communication scheme is introduced to design a suitable filter such that precious communication resources can be saved significantly while certain filtering performance can be ensured. On the one hand, the event-triggered communication scheme is devised to select only those sampled signals violating a certain threshold to be transmitted, which directly leads to saving of precious communication resources. On the other hand, the filtering error system is modeled as a time-delay system closely dependent on the parameters of the event-triggered scheme. Based on this model, a suitable filter is designed such that certain filtering performance can be ensured, provided that a set of linear matrix inequalities are satisfied. Furthermore, since a generalized dissipativity performance index is introduced, several kinds of event-triggered filtering issues, such as H∞ filtering, passive filtering, mixed H∞ and passive filtering, (Q,S,R) -dissipative filtering, and L2 - L∞ filtering, are solved in a unified framework. Finally, two examples are given to illustrate the effectiveness of the proposed method.

This paper is concerned with the distributed H∞ filtering problem over sensor networks subject to time varying transmission delays. First, the classical Round-Robin protocol is used for the scheduling of samplers' information towards the corresponding filter. Second, a distributed event-triggering transmission scheme is introduced for information communication among distributed filters. As a result, communication and energy resources can be saved significantly while certain system performance can be preserved. Third, the resulting filtering error system is modeled as a switched system with time-varying delays. By employing Lyapunov-Krasovskii functional approach, a linear matrix inequality (LMI)-based sufficient condition is established which guarantees the filtering error system asymptotically stable with prescribed H∞ performance. An algorithm for both the distributed filters and event-triggering parameters is presented on the basis of solutions of a set of LMIs. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

This paper is concerned with the problem of the robust sampled-data H∞ control for an offshore steel jacket platform subject to the self-excited wave force and the external disturbance. First, by using the input delay method, the corresponding closed-loop system with the sampling measurements is transformed into a continuous-time system. Then, based on a Lyapunov functional, the H∞ performance is established and the stability criteria for the closed-loop system is derived. Finally, the effectiveness of the proposed robust sampled-data H∞ controller is demonstrated by a simulation example. The simulation results show that the designed controller is effective to control the offshore platform, Moreover, to obtain almost the same control performance, the required control force under the robust sampled-data H∞ controller is smaller than the one under the robust H∞ controller.

This paper is concerned with the problem of event-triggered H∞control for a class of networked control systems with nonlinear perturbations. The nonlinear perturbations appear in both the system dynamic equation and the controlled output signals. An event-triggered transmission scheme is introduced to select `necessary' sampled-data packets to be transmitted through a communication network. Under the event-triggered transmission scheme, the closed-loop system is modeled as a system with an interval time-varying delay. Employing the matrix-based quadratic convex approach recently reported in the literature, a novel sufficient condition on the existence of desired event-triggered H∞controllers is derived in terms of solutions to a set of linear matrix inequalities. No parameters need to be tuned when controllers are designed. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

This paper deals with the event-triggered distributed H∞filtering for a class of networked systems with sensor networks. The topology of the sensor network is supposed to be time-varying. Whether or not a sampled data packet of a sensor node should be transmitted to its neighbors is determined by a predefined event-triggering condition, which closely depends on the variation of the sensor network topology. By modelling the filtering error system as a switched linear system with time-varying delays, a bounded real lemma (BRL) is derived using Lyapunov-Krasovskii functional approach. Based on the BRL, a sufficient condition on the existence of desired H∞filters is obtained in terms of linear matrix inequalities. An algorithm to the design of both filter parameters and event-triggering parameters is given. It is shown through a numerical example that the proposed method can not only save significantly precious communication resources, and maintain some certain system performance as well.

This paper is concerned with the problem of event-triggered distributed H∞filtering for a class of networked systems with sensor networks. First, an event-triggered scheme is introduced to select those necessary data packets to be transmitted for filter design. As a result, communication and energy resources can be significantly saved while some certain system performance can be preserved. Second, under the event-triggered scheme, a delay system model is established for distributed filtering error systems. The Lyapunov-Krasovskii method is thus employed to formulate a linear matrix inequality based sufficient condition on the existence of suitable distributed H∞filters. Third, a co-design algorithm for both distributed H∞filters and event-triggered parameters is presented in terms of solutions to a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

This paper is concerned with the problem of designing distributed event-triggered H∞filters over sensor networks subject to heterogeneous coupling intercommunication delays. A new distributed event-triggered scheme is proposed to determine whether or not each sensor's current sampled data should be broadcasted and transmitted to its underlying neighboring nodes through the communication network. In this scheme, each sensor node is able to make its own decisions to broadcast and transmit only when its local measurement output error exceeds a designed threshold. Heterogeneous coupling delays are incorporated in the intercommunication between the specific sensor node and its interacting neighbors. A refined technique is proposed to realize the complicated decoupling among the exchanged measurement outputs in the presence of coupling intercommunication delays. Then the resulting filter error system is modeled by a new delay system subject to finite time-varying “state” delays. Based on the Lyapunov-Krasovskii functional method, a sufficient condition for distributed event-triggered H∞filter design is established, from which the desired filter parameters and the triggering parameter in the event condition can be co-designed. The filter design problem is posed in terms of linear matrix inequalities. A quarter-car suspension model is finally presented to show the effectiveness and feasibility of the developed theoretical results.

This paper is concerned with event-triggered mixed H∞and passive control for linear systems via dynamic output feedback controllers. The system outputs are grouped into multiple nodes, in which different senors sample the corresponding data with a same period. A decentralized event-triggering scheme is introduced to select those necessary data packets to be transmitted so that communication resources can be significantly saved while preserving the prescribed closed-loop performance. First, to organize the decentralized data packets transmitted from the sensor nodes, a data packet packet (DPP) is used to generate new signal once its stores are updated at some time instants. Second, under the mechanism of the DPP, the closed-loop system is modeled as a linear system with an interval time-delay. Employ Lyapunov functional method to formulate a novel sufficient condition such that the closed-loop system is asymptotically stable with the mixed H∞and passive performance. Third, an LMI-based approach is proposed to co-design both event-triggering schemes and desired dynamic output feedback controllers. A satellite control system is given to demonstrate the effectiveness of the method proposed in this paper.

This paper is concerned with the stability of networked control systems in the discrete-time domain. A new bounding technique is proposed to estimate some finite-sum terms appearing in the forward difference of the chosen Lyapunov functional. This new bounding technique can provide tighter upper bounds for some finite-sum terms and avoid overly bounding for the finite-sum terms. Then a novel delay-dependent stability criterion is derived by using this new bounding technique. Compared with some existing stability criteria in the published literature, the novel stability criterion is proven theoretically to be less conservative and is shown to be of smaller numerical complexity.

This paper is concerned with global asymptotic stability for a class of generalized neural networks (NNs) with interval time-varying delays, which include two classes of fundamental NNs, i.e., static neural networks (SNNs) and local field neural networks (LFNNs), as their special cases. Some novel delay-independent and delay-dependent stability criteria are derived. These stability criteria are applicable not only to SNNs but also to LFNNs. It is theoretically proven that these stability criteria are more effective than some existing ones either for SNNs or for LFNNs, which is confirmed by some numerical examples.

This brief deals with the problem of global asymptotic stability for a class of delayed neural networks. Some new Lyapunov-Krasovskii functionals are constructed by nonuniformly dividing the delay interval into multiple segments, and choosing proper functionals with different weighting matrices corresponding to different segments in the Lyapunov-Krasovskii functionals. Then using these new Lyapunov-Krasovskii functionals, some new delay-dependent criteria for global asymptotic stability are derived for delayed neural networks, where both constant time delays and time-varying delays are treated. These criteria are much less conservative than some existing results, which is shown through a numerical example.