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This paper addresses the problem of leader-follower consensus for a class of nonlinear multi-agent systems with Lipschitz-type dynamics and a directed communication topology. First, in order to reduce communication frequency among followers, an output-based event-triggered control strategy is proposed for leader-following consensus. Second, neural-network-based observers are designed to reconstruct those immeasurable information of follower agents. Third, instead of continuously monitoring follower agents’ measurements, a distributed self-triggered control strategy is put forward, which is based only on each agent’s local estimation and its previous triggered instants. Furthermore, it is proved theoretically that the consensus tracking errors are ultimately bounded and the Zeno behavior is definitely excluded. Finally, simulation results are given to show the effectiveness and advantage of the proposed results. [ABSTRACT FROM AUTHOR]

The problem of event-triggered real-time scheduling stabilization of Takagi–Sugeno fuzzy system is investigated in this paper. Firstly, a weighted matrix approach is introduced in order to exploit implicit information of a class of nonlinear plants more freely. Secondly, by taking account of the joint-distribution of both past and current normalized fuzzy weighting functions, an event-triggered real-time scheduler is proposed to decide which control mode is to be activated at each sampling instant. Specifically, all the activated control modes can be duly updated with their respective gained matrices for accommodating different time-variant situations provided that the current joint-distribution varies. As a result, the existing stabilization implementation quality can be significantly improved while the computational burden does not increase. Finally, the effectiveness and merits of the proposed method are verified via two simulation examples. [ABSTRACT FROM AUTHOR]

The maximum min-sum dispersion problem (Max-Minsum DP) is an important representative of a large class of dispersion problems. Having numerous applications in practice, the NP-hard Max-Minsum DP is however computationally challenging. This paper introduces an effective solution-based tabu search (SBTS) algorithm for solving the Max-Minsum DP approximately. SBTS is characterized by the joint use of hash functions to determine the tabu status of candidate solutions and a parametric constrained swap neighborhood to enhance computational efficiency. Experimental results on 140 benchmark instances commonly used in the literature demonstrate that the proposed algorithm competes favorably with the state-of-the-art algorithms both in terms of solution quality and computational efficiency. In particular, SBTS improves the best-known results for 80 out of the 140 instances, while matching 51 other best-known solutions. We conduct a computational analysis to identify the respective roles of the hash functions and the parametric constrained swap neighborhood. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of adaptive event-triggered scheme for networked nonlinear interconnected systems via T-S fuzzy models. The transmission of control signals is based on a novel adaptive event-triggered communication scheme, where the adaptive threshold is dependent on a dynamic error of the system rather than a predetermined constant as the one in the existing results. The amount of the releasing data is regulated with the adaptive threshold that plays a crucial role in decision of whether releasing the sampled data or not. This technique reflects an inherent dynamic balance between the control performance and the utilization of the network resource. A corresponding Lyapunov function is constructed to achieve sufficient conditions of stability and stabilization. Finally, a simulation example is given to show the effectiveness of the proposed theoretic results. [ABSTRACT FROM AUTHOR]

In this study, we propose a systematic homogenous polynomial approach for fuzzy control design of discrete-time Takagi-Sugeno fuzzy systems under unreliable communication links. A novel delayed fuzzy control scheme, which is homogenous polynomially parameter-dependent on both the current normalized fuzzy weighting function and the one-step-past normalized fuzzy weighting function, is designed to accomplish the assignment of relaxed control synthesis. In contrast to the existing methods, the closed-loop controlled system is stochastically stable in the mean square sense with less conservative stabilization conditions. Finally, simulation results are provided to verify the advantage and the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this note, the distributed event-triggered cooperative attitude control of multiple groups of rigid bodies evolved on nonlinear manifold SO (3) is investigated. The distributed event-triggered mechanism is introduced into the controller design to reduce the communication pressure. It is verified that, the orientations of rigid bodies in each group converge to a common value with zero angular velocities, and these common values in all groups achieve an anti-consensus configuration under the proposed intra- and inter-group cooperative control law. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we consider a leader-follower output consensus problem for a class of uncertain heterogeneous non-affine pure-feedback multi-agent systems (MASs) in the presence of time-delay items and input saturation restrictions under switching directed topologies. A distributed adaptive control scheme is constructed by combining Lyapunov–Krasovskii functionals, backstepping methods, neural networks (NNs), auxiliary systems, graph theory, the mean value theorem and the implicit function theory along with the dynamic surface control (DSC) technique. The key advantages of the designed control approach are that there is no requirement of precise knowledge about uncertain dynamics and time-delay items of individual agents and the computational burden can drastically be reduced by employing the DSC technique. Also, norms of unknown weight of neural networks are estimated online instead of weight vectors themselves. In theory, it can be proven that the output signals of follower agents can synchronize with the leader’s and all signals in the closed-loop system are cooperatively semiglobally uniformly ultimately bounded (CSUUB) by suitable choice of design parameters. Two simulation examples are presented to demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, we study the finite-time distributed event-triggered consensus control for multi-agent systems. The controllers and the events are designed in a distributed manner, based only on local information. Compared with conventional asymptotic consensus, finite time consensus can reach consensus faster and have better disturbance rejection properties. Thus, we propose a new nonlinear distributed control protocol to achieve finite-time consensus. Moreover, in order to realize the consensus control for multi-agent systems, only the communication between the agent and its local neighbors is needed, therefore, the designed control is essentially distributed. Two sufficient conditions are proposed to reach finite-time consensus for multi-agent systems with fixed and switching network topologies, respectively. Finally, two simulation examples are presented to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with event-triggered H ∞ stabilization (in the mean square sense) for networked stochastic system with multiplicative noise and network-induced delays. Firstly, a new discrete event-triggered transmission protocol (DETTP) is proposed to reduce the utilization rate of communication bandwidth. The main features of the proposed protocol involve three aspects: (1) it allows the presence of external disturbances and multiplicative noise; (2) it is more general than some existing event-triggered transmission schemes in the deterministic setting; and (3) the minimum inter-event time is implicity defined and is naturally given by the sampling period chosen for the plant. Secondly, under the DETTP, a new closed-loop stochastic networked control system model is established. Remarkably, to facilitate theoretical analysis, the resultant event-based stochastic networked control system is further transformed into a system with two successive delay components in the state, considering the effects of network-induced delays, multiplicative noise and DETTP in a unified framework. By using stochastic system theory, sufficient conditions on the existence of the desired event-based H ∞ state feedback controller are derived such that the resultant event-based stochastic networked control system is exponentially stable in the mean square sense with a prescribed H ∞ performance while reducing the number of sampled-state transmission along the feedback link as much as possible. Thirdly, a co-design algorithm for determining the parameters of the DETTP and the state feedback controller gain matrix are also proposed. Finally, two practical examples are given to illustrate the effectiveness and advantage of the proposed method over some existing results in the literature. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new approach is proposed and investigated for the stability analysis and stabilizing controller design of randomly switched linear discrete systems. The approach is based on sojourn probabilities and it is assumed that these probabilities are known a prior. A new Lyapunov functional is constructed and two main theorems are proved in this paper. Theorem 1 gives a sufficient condition for a switched system with known sojourn probabilities to be mean square stable. Theorem 2 gives a sufficient condition for the design of a stabilizing controller. The applications of these theorems and the corresponding corollary and lemma are demonstrated by three numerical examples. Finally, some future research is proposed. [Copyright &y& Elsevier]

Abstract: This paper investigates the reliable control design for networked control system under event-triggered scheme. The key idea is that only the newly sampled sensor measurements that violate specified triggering condition will be transmitted to the controller, and the main attribute of our approach is that the proposed event-triggered scheme only needs a supervision of the system state in discrete instants and there is no need to retrofit the existing system. Considering the effect of the network transmission delay, event-triggered scheme and probabilistic sensor or actuator fault with different failure rates, a new fault model is proposed. Based on the newly built model, criteria for the exponential stability and criteria for co-designing both the feedback and the trigger parameters are derived by using Lyapunov functional. These criteria are obtained in the form of linear matrix inequalities. A simulation example is employed to show the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper studies the H ∞ filtering problem for a class of discrete-time networked systems with partly known distribution transmission delays. It is assumed that the random sensor-filter delay satisfies Markovian characteristics, but the transition probabilities of the delay transferring from one value to others are partly known. A novel measurement model is proposed to reflect the random delay of the transmission modes and the resulting filtering error system is modeled as a Markovian jump system (MJS) with multiple modes and state delay. Sufficient conditions are derived for the filtering error system to be stochastically stable and ensure a prescribed H ∞ disturbance attenuation level bound by using mode-dependent Lyapunov functional approach. Based on the derived conditions, a design procedure for the H ∞ filter is also presented. A numerical example is provided to show the design process of the filter and the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of output feedback control for networked control systems (NCSs) with limited communication capacity. Firstly, we propose a new model to describe the non-ideal network conditions and the input/output state quantization of the NCSs in a unified framework. Secondly, based on our newly proposed model and an improved separation lemma, the observer-based controller is developed for the asymptotical stabilization of the NCSs, which are shown in terms of nonlinear matrices inequalities. The nonlinear problems can be computed through solving a convex optimization problems, and the observed and controller gains could be derived by solving a set of linear matrix inequalities. Thirdly, two simulation examples are given to demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

• A dynamic trade-off between conservatism and complexity is established via a online evaluator. • More efficient fuzzy stabilization conditions are developed in this paper. • The recent multiple-sums-based method is improved evidently in the study. This paper is concerned with the problem of developing more efficient stabilization conditions for discrete-time Takagi-Sugeno fuzzy systems, i.e., not only reducing the conservatism but also (at the same time) alleviating the complexity of fuzzy control synthesis. Firstly, under the framework of traditional fuzzy stabilization, the recent multiple-sums-based method reported in the literature is improved by removing all the redundant variables, and thus the same feasible stabilization range can be obtained at the cost of more economical computational burden. Secondly, in order to further enhance the control efficiency, a dynamic trade-off between conservatism and complexity is established by proposing a new online evaluator of the updated system variation information across two adjacent sampling instants, and thus the so-called resilient stabilization is developed for reducing the conservatism without increasing or even alleviating the complexity. Moreover, all the determined matrices at the farthest sampling instant can be entirely removed without introducing any conservatism due to the proposed substitution technique. Finally, the superiority and effectiveness of our proposed methods are illustrated by a set of simulation comparisons with relevant research results reported in recent literature. [ABSTRACT FROM AUTHOR]

A deep integration of cyber and physical systems will lead to severe risk and security challenges in the new power system. To address this problem, an optimal defense resource allocation method based on game theory is proposed to against potential cyber-attacks in smart grid proactively. Based on this method, a two-layer game model is designed to optimal resource allocation. One layer is consisted by an evolutionary game between defense nodes and attackers, and the anther layer is consisted by a noncooperative game between multiple defense nodes. Further up, the offensive and defensive evolution results for all scenarios have been discussed, then the solution to resource allocation problem among multiple nodes is formulated. Different from previous work, characteristics of the bounded rationality of the attacker are considered according to the indexes of integrity, usability and confidentiality. Meanwhile, quantum response equalization is introduced to quantify player gains. Finally, specific algorithms are employed to demonstrate the feasibility and effectiveness of the method proposed in this paper. • In this paper, an optimal defense resource allocation method is proposed to proactively defend against potential cyber-attacks in smart grid. • An evolutionary game model is formulated to describe the game behavior of both attackers and defenders, which is a model that has rarely been studied in the field of grid information security, especially for the consideration of the bounded rationality of the players. • Quantitative analyses on cyber-attacks and game gains are studied as well. Cyber-attacks have been subdivided into three categories, which makes the model more accurate. We obtain the impact of cyber-attacks specifically by designing a system loss function and introduce a quantum response equilibrium formula to calculate the probability of an attack. • Based on the current research work on offensive and defensive games, a two-layer game model is proposed to obtain the optimal defense resource allocation scheme instead of a simple Nash equilibrium strategy. The network layer gains and physical layer properties of the grid nodes are considered in model, which is necessary for cyber-physical systems such as smart grids. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose an output-based tracking control scheme for a class of continuous-time nonlinear systems via the adaptive dynamic programming (ADP) technique. A neural networks (NNs) observer is constructed to reconstruct immeasurable information of the nonlinear systems, and, by introducing a new state vector and appropriate coordinate transformation, tracking control issues are converted into optimal regulation problems where critic-actor neural networks structures are developed for the solution of Hamilton–Jacobi–Bellman (HJB) equation corresponding to tracking errors. In addition, a robust term is introduced to eliminate effects from approximation errors. It is proven that all signals in the closed-loop system are uniformly ultimately bounded (UUB) by the Lyapunov approach. Finally, simulation examples are provided for illustration of the theoretical claims. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of dynamic output feedback control for networked nonlinear systems. A novel event-triggered mechanism (ETM) is proposed, in which a new event-triggering condition is introduced. Compared with some existing ETMs, the proposed ETM has at least three merits: i) The data-releasing rate can be further decreased, leading to a reduction of network loads; ii) During the time when the system is disturbed by external signals, this ETM can release more sampled data to the controller so that some better closed-loop performance can be achieved; and iii) Wrong decision-making on data-releasing can be avoided due to a new definition of the related error. By employing the Lyapunov–Krasovskii functional method, sufficient conditions are derived to design both controller gains and ETM parameters. An example with four cases is given to show the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a sojourn-probability-dependent method is proposed to investigate the robust H ∞ control for a class of switched systems with input delays. The considered system has the following characteristics: (1) it is a switched system consisting of a set of subsystems; (2) sojourn probabilities (i.e. the probability of switched systems staying in each subsystem) are assumed to be known (or partly known) a prior; and (3) there are input delays and parameter uncertainties in each subsystem. By using the sojourn probability information, a new type of switched system model is built. By using the Lyapunov functional method, the robust mean square stability criteria are obtained for switched systems under two conditions: (A) all sojourn probabilities of the subsystems are known; (B) only partly sojourn probabilities are known. Then the robust H ∞ controller feedback gains are derived by using the cone complement linearization method. An inverted pendulum system and a numerical example are given to demonstrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses a secure bipartite tracking control problem for a class of nonlinear multi-agents (MASs) with nonsymmetric input constraints. In the presence of adversarial sensor attacks, a secure measurement preselector, along with an explicit sufficient condition, and a neural network (NN) secure state observer are introduced for achieving secure state estimation. Then, a secure bipartite tracking control strategy is proposed, where observation predictors are designed to reconstruct prediction errors in such a way as to improve control performance. Furthermore, an auxiliary system is presented to eliminate influence from nonsymmetric input saturations. It is theoretically proved that the proposed control strategy not only guarantees bipartite tracking of the MAS but also preserves the stability of the resulting closed-loop system in spite of senor attacks. Finally, two illustrative examples are presented to verify the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

In real video surveillance scenes, the extracted face regions generally have low-resolution (LR) and are sensitive to pose and illumination variations; these flaws undoubtedly degrade the subsequent recognition task. To overcome these challenges, we propose an approach named multilayer locality-constrained structural orthogonal Procrustes regression (MLCSOPR). The proposed MLCSOPR not only learns the pose-robust discriminative representation features to reduce the resolution gap between the LR image space and the high-resolution (HR) one but also strengthens the consistency between the LR and HR image space. In particular, several contributions are made in this paper: (i) Inspired by the orthogonal Procrustes problem (OPP), a matrix approximation is exploited to find an optimal correction between two data matrices. (ii) The nuclear norm constraint is applied to the reconstruction error to maintain the structural property. (iii) Based on the abovementioned learned resolution-robust representation features, a linear regression-based classification strategy is adopted to recognize the LR input face images. Experiments on commonly used face databases have shown the effectiveness of the proposed method on cross-resolution face matching with pose variations. [ABSTRACT FROM AUTHOR]