Power systems are vulnerable not only to physical faults but also to cyberattacks, which can prevent the communication network from receiving measurement information from bus sensors during such incidents. Therefore, the rapid detection of power line outages (LOs) is crucial for maintaining the stability of the power system in the presence of denial-of-service (DoS) attacks. This paper introduces a real-time multiple LOs (Multi-LOs) detection algorithm based on a novel distributed finite-time observer. Unlike existing observers designed for Multi-LOs detection that depend on secure communication services, the proposed observer can detect Multi-LOs within a finite time, even in the presence of DoS attacks and voltage uncertainties. A comprehensive mathematical analysis establishes the upper bound for the settling time of estimation errors and demonstrates that the proposed distributed observer converges within a finite time during DoS attacks, thereby enabling timely detection of Multi-LOs. Simulation results further validate the effectiveness and robustness of the proposed Multi-LOs detection algorithm.

Due to the various constraints in the communication process, such as bandwidth, quantization, time delay, etc., it will inevitably have some impacts on the performance of communication networked systems. More seriously, it will cause the system to lose its stability. This study investigates the output tracking performance (OTP) of communication networked systems under the combined effects of uncertain time delays, packet losses, and channel noise. By using the methods of first order Pade approximation, all-pass decomposition and Youla parameterization, the explicit expressions of OTP are derived by the design of the optimal controller, which mainly contain single-degree-of-freedom (SDOF) and two-degree-of-freedom(TDOF), respectively. The results show that the OTP of NCSs has strong connections with the inherent properties of the plant and the communication parameters of channel. Finally, the correctness of the theoretical results is verified by the simulations for the inverted pendulum system.

This article investigates the event-triggered composite control problem for switched two-time-scale systems (TTSSs). First, the stabilization problem of switched TTSSs under switching composite control is addressed. Unlike existing results that require the singular perturbation parameter (SPP) to be sufficiently small or to satisfy specific linear matrix inequality conditions, an explicit upper bound of the SPP is derived. Then, both SPP-dependent and SPP-independent dynamic event-triggered mechanisms are developed to reduce the computational burden associated with control signal updates in switched TTSSs. These mechanisms are novel in that they incorporate the boundary layer system and explicitly balance the fast and slow time-scale dynamics. Furthermore, two sufficient stability conditions are established: one concerning the upper bound of the SPP and the other concerning the mode-dependent average dwell time, ensuring the stability of switched TTSSs under the respective mechanisms. In addition, Zeno's behavior is excluded in both cases. Finally, three numerical examples, including a comparative study, are provided to demonstrate the effectiveness and advantages of the proposed results.

This article investigates the deception-eliminating design (DED) against false information attacks by deceptive agents in asynchronous optimal consensus control. We model the asynchronous interactions among agents as multistage games and establish a Tit-for-Tat rule to compel deceptive agents to turn to transmitting true state information. Furthermore, we design a false information counterattack rule under asynchronous updates by leveraging the invariance of the rank of equivalent matrices and the convexity of positive semidefinite quadratic forms. This design effectively intimidates deceptive agents that have transitioned to cooperation, ensuring they do not revert to transmitting false information. Subsequently, by utilizing the properties of Riccati differential equations, the integrating factor methods, the Minkowski inequality, and proof by contradiction, we theoretically analyze the impact of false information on consensus and provide an explicit upper bound for the strategy update periods of agents with different performance matrices. Theoretical proof shows that as long as the strategy update periods of all agents remain below this upper bound and the above two DEDs are implemented, the asynchronous consensus is guaranteed.

This article studies an energy transaction management of multienergy prosumers (MEPs) in integrated energy community (IEC). A novel MEPs coalitional game model with transferable utility is proposed to coordinate the electricity–heat transactions and enhance the local energy consumption among MEPs in IEC. The superadditivity of the proposed coalitional game is rigorously proven to ensure coalition incentives. A superadditivity-directed coalition formation algorithm is developed to achieve a stable and efficient coalition partition with significantly reduced computational burden. Furthermore, the nonemptiness of the core for the proposed coalitional game is rigorously proven, and a Shapley value-based payoff allocation mechanism is designed and proven to align with the core, ensuring both fairness and stability in the proposed coalitional game. Simulation results show that the proposed model and method achieve the highest payoff across all cases, ensure the fair and stable payoff allocation, achieve reductions of 99.2% in iterations and 98.98% in solving time, and confirm their feasibility for large-scale applications.

Time-series forecasting with missing values remains a critical challenge across diverse domains. While Transformer-based models excel with complete data, they suffer from “attention sink” where attention mechanisms disproportionately focus on missing data points, creating a self-reinforcing feedback loop that degrades performance. To address this, we propose meta-enhanced attention (MeaTS), a novel end-to-end Transformer-based framework specifically designed for robust multivariate time-series forecasting with missing values. Our approach introduces two key innovations, first, an MeaTS mechanism that dynamically adjusts attention weights by suppressing focus on missing values while amplifying attention to observed data, effectively mitigating the attention sink problem; second, an extracting latent value module that transforms data with missing values into informative features through frequency-domain representations, enhancing data representation without explicit imputation. Extensive experiments demonstrate that MeaTS achieves a 19.86% improvement in mean absolute error compared to state-of-the-art models, such as SDformer, GinAR+, and BiTGraph.

Accurate electricity forecasting is crucial for ensuring grid stability, optimizing resource allocation, and enabling efficient energy markets. To enhance prediction accuracy, we challenge the paradigms of extracting temporal dependence patterns in traditional time series forecasting approaches and introduce a novel concept: time-element entanglement (TimeEE). This concept asserts that complex nonlinear coupling interactions between multiple past time steps significantly influence the current time step. Building on this idea, we present TimeEE, an innovative model for electricity forecasting through time-element entanglement. Its key component, the element entangled block, explicitly models the entangled information between time points, capturing essential time-element interactions for highly accurate predictions. Our model demonstrates state-of-the-art performance in both prediction accuracy and efficiency across multiple datasets, providing a new and efficient solution for electricity forecasting.

Mobile energy storage systems (MESSs) represent a proactive approach to load restoration of faulted microgrids. Existing studies mainly focus on minimizing the overall cost of the MESS fleet but fail to guarantee that self-interested MESSs are willing to follow the optimal social cost solution. Hence, this article allows MESSs to make independent decisions and formulates a nonconvex game. In particular, we incorporate the maximum tolerable service waiting time and construct a potential function to prove that the selfish actions of MESSs converge to a Nash equilibrium. We derive a small upper bound on the price of anarchy (PoA), demonstrating that granting MESSs the autonomy to make self-interested Nash decisions does not significantly increase the overall social cost. Moreover, we model subjective behaviors under uncertain grid power availability, accounting for both loss-sensitive and gain-seeking tendencies. Simulation results show that for different MESS fleet sizes and battery anxiety extents, Nash equilibria are always achieved, with all PoA values below the theoretical bound. Higher MESS numbers or battery anxiety extents improve load restoration performance. Under uncertain surplus energy, gain-seeking MESSs behave more aggressively and earn higher average profits.

In this paper, the p-th moment synchronization problem for a class of stochastic multi-layer neural networks with intra-layer and inter-layer connections is investigated. Due to the multiple connections with delays and stochastic noise, the typical methodologies that build a canonical linear or expanded matrix model to analyze its stability by constraining eigenvalues in the left-half plane, such as the Kronecker product method, linear matrix inequality and M -matrix approach are tough to tackle the problem. Consequently, a graph-theory-based Lyapunov functional is constructed by combining multiplicative principles and a graph-theoretic approach to help examine the effect of inter- and intra-layer connectivity on a unified framework. With the proposed adaptive fixed-time controller, sufficient conditions for the p-th moment synchronization in a fixed time are derived in terms of algebraic inequality. A corollary, together with a constant-gain fixed-time controller, is presented in case there is no delay. Finally, a confirmatory and two comparative simulations show the effectiveness and convenient implementation of the proposed control strategy.

This paper presents a robust load frequency control (LFC) scheme for an islanded microgrid (IMG) based on sampled-data control. First, a novel LFC system model for an IMG is proposed by incorporating a virtual inertia control (VIC) mechanism with a sampled-data-based auxiliary controller. Then, through applying Lyapunov theory and introducing an exponential decay rate (EDR) as a performance indicator, a novel stability criterion is established for the LFC of the IMG. Based on the stability criteria, the controller design is presented by utilizingH∞performance index μ and EDR as conditions, resulting in a fast and robust LFC scheme. Finally, simulation results and comparison analysis show that the proposed method is superior, and the introduced VIC mechanism with a sampled-data-based auxiliary controller can effectively improve the dynamic performance of the system.

Since the teleoperation process of networked mobile robotic systems (NMRSs) is generally affected by the coupling of the human force, complex dynamical model, external environment and nonholonomic constraints, designing an effectiveness control to guarantee the desired performance for the teleoperation task is a challenging work. Besides, due to the limitation of the communication and control technology, the controller for the teleoperation system is usually required to have low computation load and low monitoring sampling frequency. To address these challenges, a novel self-triggered model predictive control (STMPC) framework, being consisted of the local STMPC, high-order estimation and M/R fixed-time controller, is constructed. Compared to traditional MPC methods, our proposed local STMPC offers lower computational load and requires fewer monitoring resources while retaining the ability to optimize control performance and handle multiple constraints. Using the presented STMPC framework in a hierarchical manner and newly designed high-order estimation, we successfully and simultaneously solve the teleoperation task while accounting for the self-triggering mechanism, decentralized control implementation, disturbance rejection, and complex nonholonomic model. Additionally, we derive sufficient conditions for ensuring the stability of the closed-loop system. Finally, the simulation and experiment results are provided to demonstrate the effectiveness of the proposed STMPC framework. Note to Practitioners—Our research introduces a groundbreaking framework, self-triggered model predictive control (STMPC), specifically designed to elevate control efficiency, quality, and reliability in the context of remote operation of networked mobile robotic systems (NMRSs). This innovation holds immense promise, particularly in domains requiring the utilization of autonomous vehicle fleets, such as large-scale exploration, search and rescue missions, and escort operations in hazardous or hard-to-reach areas (e.g., forest firefighting, warzone patrolling, saturation attacks, and space escort). These environments are often characterized by extreme external conditions and unpredictable external influences, such as meteorite impacts, flames, obstacles, and explosions, posing significant challenges for the control and task execution of unmanned vehicle fleets. Moreover, these missions demand high precision and control responsiveness from the robotic clusters to swiftly accomplish urgent tasks. Consequently, the need arises for autonomous vehicle fleets that not only possess the capacity to adapt to various constraints and external disturbances but also execute mission tasks swiftly and precisely. STMPC, as presented in this paper, demonstrates its capability to address these issues and requirements effectively, making it a versatile and powerful tool for enhancing control and system performance in various scenarios.

The data communication among sensors, actuators, and controllers in network systems poses various challenges, including packet loss, network-induced delay, and channel noise interference, all of which adversely affect performance of control systems. Based on single-degree-of-freedom (SDOF) controller and two-degree-of-freedom (TDOF) controller, respectively, this article discuss networked control systems (NCSs) with packet loss, network-induced delay, and logarithmic quantization constraints in the communication channel, and proposes a discrete-time modified performance index. By the frequency domain analysis methods and Youla parameterization techniques of controllers, explicit expressions of the modified performance limitations are derived, which quantitatively reveal the impact of the fundamental characteristics of the plant and communication constraints on systems' performance. In addition, a linear dual-freedom vehicle system is modeled and the obtained theorems are applied to verify the correctness of the results. The results show that communication constraints have an adverse impacts on systems' performance, and modified performance index can better measure systems' performance.

This article delves into the semi-global robust output regulation of uncertain singular linear systems subject to input saturation and denial-of-services (DoSs) attacks. In the event of a DoS attack, the communication channel between the plant and the controller is blocked. To address the input saturation nonlinearity and to enhance robustness against uncertainty and Dos attacks, a post-processing internal model-based robust output feedback regulator is proposed. It employs the small gain techniques, incorporating two small positive parameters. The overall system is modeled as a hybrid system using hybrid formalism, for which a jump corresponds to the DoS attacks. It is shown that the proposed regulator exhibits robustness against certain DoS attacks and structure uncertainties. Consequently, the output error can asymptotically converge to the origin when the structured uncertainty is small enough, and the proposed constraint on the duration of DoS attacks is met. Finally, two numerical example are given to illustrate the effectiveness of the result.

This article investigates the leader-following consensus of nonlinear time-delay multiagent system under impulsive control with simultaneous consideration of packet loss and parameter mismatch. Specifically, the inherent parameter mismatch between the leader's dynamics and followers' dynamics is explicitly addressed. To mitigate communication frequency, two novel impulsive control protocols are developed: 1) a pure impulsive scheme for theoretical analysis and 2) a limited impulsive strategy for practical implementation. Furthermore, an auxiliary function is introduced to characterize packet loss phenomena during information transmission, ensuring alignment with real-world communication constraints. By integrating impulsive control theory with reverse average dwell-time analysis, sufficient consensus criteria are rigorously derived for multiagent system with time delays and heterogeneous parameters. Finally some numerical simulations validate the effectiveness of the proposed control framework, demonstrating its capability to achieve consensus under practical communication imperfections.

Privacy preservation is a challenging problem in decentralized nonconvex optimization containing sensitive data. Prior approaches to decentralized nonconvex optimization are either not strong enough to protect privacy or exhibit low utility under a high privacy guarantee. To address these issues, we propose a differentially private linearized alternating direction method of multipliers (DP-LADMM), which achieves fast convergence property for nonconvex objective functions while achieving saddle/maximum avoidance under differential privacy guarantee. We also apply the Analytic Gaussian Mechanism to track the cumulative privacy loss and provide a tight global differential privacy guarantee for DP-LADMM. The theoretical analysis offers an explicit convergence rate for our algorithm. To the best of our knowledge, this is the first paper to provide explicit convergence for decentralized nonconvex optimization with differential privacy and saddle/maximum avoidance. Numerical simulations and comparison studies on decentralized estimation confirm the superiority of the algorithm and the effectiveness of global privacy preservation.

Voltage regulation in distribution grids often suffers uncertainties such as model mismatches and parameter changes due to the installation of renewable energy sources. This article proposes a model-free robust online feedback optimization (OFO) framework that addresses these challenges. By reformulating the classical optimization problem into an incremental input form under box constraints, an OFO method that solely relies on measurement data is developed. To handle unknown input–output constraint relationships, an adaptive forgetting factor least-squares algorithm for gradient estimation is adopted, which can swiftly track the gradient information. Sound theoretical proof is laid out to show the convergence property of the proposed method. Simulations using a modified IEEE 22-bus system and real distribution grid data validate the proposed method’s effectiveness and robustness.

The imbalance between faulty and healthy samples of electric vehicle (EV) charging piles makes it difficult for both algorithm-level and data-level diagnostic methods to identify fault categories with limited data. In this article, a fault diagnosis scheme for EV DC charging piles based on a nonparametric balanced diffusion (BalDiff) model under imbalanced samples is proposed by the data-level method. First, the nonparametric BalDiff model is proposed to augment the imbalanced samples. Unlike existing generative data augmentation methods, it does not require the assumption of a specific data distribution. In addition, BalDiff uses attenuated noise instead of the constant noise intensity during the reverse process of the diffusion model to enhance the ability to capture the sample distribution. Second, an online fault diagnosis strategy applicable to different data sources is designed, which guarantees the diagnosis time while ensuring the diagnosis accuracy. Case studies on different data sources show that the proposed scheme achieves at least 4.2% and 6.7% improvement in F1-score and accuracy compared to the comparison methods, and the single diagnosis time is only 0.021 s.

This article focuses on the dynamic quantized control for Takagi–Sugeno fuzzy semi-Markov jump systems (T–S FSMJSs) under fading channels and deception attacks, employing an improved event-triggered mechanism (ETM) strategy. Specifically, a more generalized semi-Markov process (SMP) that is governed by a higher-level deterministic switching signal (HLDSS) is addressed. A novel dynamic ETM is skillfully developed based on the information of quantization and two internal dynamic adjusting variables to further enhance the network bandwidth utilization. The dual asynchronous phenomenon between the plant and controller (asynchronous modes and mismatched premise variables) is addressed. This implies that the designed controller is not required to share the same membership functions and modes as the plant, establishing a more rational structure. The hidden semi-Markov model (HSMM) is attained to characterize the stochastic varying channel fading amplitudes. The Lyapunov function incorporating mode and fuzzy information is constructed to establish sufficient criteria ensuring the strictly dissipative performance and mean-square exponential stability (MSES) of the resulting closed-loop systems, and a new security fuzzy dual asynchronous controller is then developed. Finally, the efficacy and applicability of the results are demonstrated through numerical and practical examples.

The stability problem of generalized neural networks (GNNs) with time-varying delay is investigated in this article. Novel parameter-dependent negative-determination conditions (NDCs) for cubic matrix-valued polynomials are derived by using convex approaches. In comparison with the existing methods, the positive-definite constraint on the constructed Lyapunov–Krasovskii functional (LKF) is relaxed by using the sum of several matrices to maintain positive definiteness instead of this requirement on every single matrix involved in the LKF. Less conservative stability conditions are established by using the constructed LKF and the proposed parameter-dependent NDCs for cubic matrix-valued polynomials. Two examples, including comparative results to existing methods are presented to show the feasibility and superiority of the proposed method.

Microgrid is a typical cyber-physical system which integrates the power flow and the communication flow. Privacy preservation of sensitive information delivering over the communication network has received significant attention. In this paper, a privacy-preserving distributed secondary controller is proposed for DC microgrids. The privacy information is subjected to an output mask function to ensure its anonymity, thereby providing real-time security for private power information. Furthermore, by employing event-triggered mechanism to determine the updating of control signals, current sharing and voltage regulation are achieved simultaneously. In addition, the decreasing event-triggered threshold improves the systems response speed and accuracy. Finally, the simulation and experimental cases demonstrate the effectiveness of the proposed controller.

This article investigates the dynamic-memory event-triggered (DMET)-based double asynchronous dissipative control issue for Takagi–Sugeno fuzzy semi-Markov jump systems (T-S FSMJSs) under redundant channel strategy. To mitigate the network burden and improve the system control performance, a new DMET mechanism is skillfully developed by adopting weighted past released packets of measured output and dynamically adjusted thresholds. Due to the impact of network environment, a novel framework of DMET-based controller scheme is constructed by considering the premise variable asynchronous and mode asynchronous phenomena between the plant and controller simultaneously. The imperfect premise matching and hidden semi-Markov model approaches are employed to resolve two types of mismatches. The redundant channel communication strategy with quantization measurement is used to improve the nonfragility of signal transmission. In terms of the stochastic theory and fuzzy model technique, some criteria are forwarded to ensure the stochastic stability and strict dissipative performance for closed-loop T-S FSMJSs. By the matrix decoupling method, the control scheme is established. Lastly, a single-link robot arm system example is presented to certify the effectiveness of the developed result.

This paper investigates the stability problem of Takagi-Sugeno (T-S) fuzzy systems with time-varying delay. Different from some existing methods, a new delay-productdependent Lyapunov-Krasovkii functional (LKF) is proposed, whose derivative is estimated by using the proposed cubic function negative-determination lemma (NDL). Moreover, a parameter-dependent reciprocally convex inequality (PDRCI) is proposed to improve the estimation accuracy of reciprocally convex terms. Besides, an improved cubic function NDL is derived to solve the negative-definiteness determination of cubic functions. Based on the proposed delay-product-dependent LKF, the developed PDRCI and the improved cubic function NDL, a less conservative stability criterion is obtained. Three examples are presented to demonstrate the merits of the proposed approaches by comparing with several existing results.

This article adopts a novel homogeneous polynomial parameter-dependent (HPPD) codesign method to deal with the security control problem of the Takagi–Sugeno fuzzy system of cyber-physical dc microgrid under hybrid attacks. A new fuzzy control scheme and state estimation scheme, i.e., a function whose homogeneous polynomial parameters depend on the fuzzy membership function normalized at the current time, is proposed to achieve the combined goal of relaxed control and accurate estimation. Relaxed exponential stability conditions are then derived with less conservatism than the existing conditions. In addition, an HPPD codesign method is further proposed. The effectiveness and superiority of the results in this article are illustrated through simulation examples.

This article focuses on the${H_\infty }$control for nonlinear two-time-scale systems with event-triggered mechanisms. Utilizing the Takagi–Sugeno fuzzy model, it is feasible to represent nonlinear two-time-scale systems as fuzzy two-time-scale systems. Event-triggered state feedback control strategy is designed for achieving the${H_\infty }$performance, which inevitably leads to the asynchronous phenomenon of the premise variables between continuous time and triggering instants. Under the consideration of the asynchronous phenomenon, based on a$\varepsilon$-dependent Lyapunov function, the fuzzy composite state feedback controller gains and the event-triggered parameters are codesigned in the form of linear matrix inequalities, and the upper bound of$\varepsilon$is provided as well. Furthermore, the proposed event-triggered mechanism ensures the exclusion of Zeno behavior. Finally, simulation results including comparison studies are shown to demonstrate the effectiveness of the proposed control strategy.

Fast detection of power line outages is critical for maintaining the stable operation of the power system. The aim of this article is to address the real time detection problem of multiple line outages in power systems. To effectively tackle the high-computational complexity issues associated with traditional approaches, we propose a multiple line outages detection algorithm that utilizes a distributed finite-time observer. The proposed method utilizes only local measurements and information from neighboring buses to update local observer for each bus. The proposed observer is mathematically proven to converge in a finite time, ensuring rapid detection of multiple line outages. Finally, simulation results demonstrate the effectiveness and rapidity of the proposed detection algorithm.

This article investigates the robust output regulation of linear uncertain singularly perturbed systems by output-feedback control. Due to the presence of extremely small perturbation parameters, numerical issues will arise when applying conventional robust regulator design techniques to the regular system. To handle the problem, a continuous-time output-feedback controller adopting the forwarding design technique is proposed by combining the internal model approach with singularly perturbed theory. In this manner, the circumvention of numerical issues and the resolution of robustness problems can be achieved. Additionally, the sampling of the transmissions between the plant and the controller is taken into account, leading to the proposed implementation of a dynamic output-based event-triggered strategy. As a result, the output regulation property becomes practical. To avoid Zeno behavior, the implementation ensures the existence of a strictly positive minimum intertime for the triggering instant sequence. The validity of the results is demonstrated through the presentation of a numerical example.

This article investigates the control problem for networked switched systems under denial-of-service (DoS) attacks. The switching signal and the quantized system state are transmitted to the remote controller via a network, which suffers from bandwidth constraints and DoS attacks. To save communication resources in both time and space, a Zeno-free dynamic event-triggered mechanism (DETM) and a dynamic quantization mechanism are co-constructed. However, the triggering error and the quantization error are induced simultaneously. Meanwhile, the quantization saturation and the system instability issues are raised due to the asynchronous mode and the bandwidth-constrained network under DoS attacks. To tackle these problems, a quantization-dependent DETM is presented in terms of a quantization parameter. A dynamic quantizer with a mode-dependent variable quantization level (MDVQL) is proposed. The constraint conditions on the quantization level and the event-triggered parameters are obtained to ensure the unsaturation of the quantizer, and sufficient conditions are provided for the exponential stability of the switched systems. Lastly, simulation examples are carried out to verify the effectiveness of the theoretical results and show the merits of the proposed methods for saving communication bandwidth.

When realizing multiagent optimal consensus control, it may encounter the situation that malicious agents transmit false information. Besides, due to the unreliability of information interaction and the uncertainty of the system itself, the agent may not fully know its own cost function. In this article, an improved Bayesian graphical game method is proposed to solve the optimal consensus problem of linear dynamical networks in the presence of false information. The agent’s probabilistic estimate of the uncertainty is named belief, and the update of probability estimate is named belief update. An information tradeoff principle is designed to solve the belief update problem in the presence of malicious neighbors. The principle can not only reduce the loss caused by false information but also force malicious agents to switch from deception to cooperation. On this basis, a new belief update method with information tradeoff principle is established. It is proved that this new belief update method has a faster convergence rate than the Bayesian belief update method when malicious neighbors exist. As an illustrative example, the graphical game solution is applied to the formation tracking control problem of a quad-rotor unmanned aerial vehicle swarm. Theoretical proof and simulation comparisons can illustrate the proposed method’s advantages.

Standard output regulation design techniques cannot be applied for linear two-time-scale systems subject to saturated inputs. In this work, a state feedback output regulation is first proposed based on a classical stabilizing composite state feedback controller. Nevertheless, the corresponding design is difficult to implement due to numerical issues. Thus, the method of asymptotic power series expansion is applied to provide an approximate solution to the regulator equation. Then, a time-continuous state feedback controller is designed by combining the Chang transformation approach and the low-gain feedback technique, which results in a semi-global bounded output regulation of the closed-loop system. Furthermore, to reduce control updates, a dynamic event-triggered control scheme is proposed which ensures the exclusion of Zeno behavior by maintaining a strictly positive time between any two triggering moments, regardless of the initial state of the system. Additionally, an observer-based event-triggered control scheme is proposed to cater to the practical scenario in which system state information is unavailable. Finally, to demonstrate the effectiveness of our proposed technique, two examples are presented.

This article studies the lag-bipartite formation tracking (LBFT) problem of the networked robotic systems (NRSs) with directed matrix-weighted signed graphs. Unlike the traditional formation tracking problems with only cooperative interactions, solving the LBFT problem implies that: 1) the robots of the NRS are divided into two complementary subgroups according to the signed graph, describing the coexistence of cooperative and antagonistic interactions; 2) the states of each subgroup form a desired geometric pattern asymptotically in the local coordinate; and 3) the geometric center of each subgroup is forced to track the same leader trajectory with different plus-minus signs and a time lag. A new hierarchical control algorithm is designed to address this challenging problem. Based on the Lyapunov stability argument and the property of the matrix-weighted Laplacian, some sufficient criteria are derived for solving the LBFT problem. Finally, simulation examples are proposed to validate the effectiveness of the main results.

In this study, the Takagi-Sugeno (T-S) fuzzy impulsive control problem is investigated for a class of nonlinear positive systems with time delay. The time delay under consideration is both in the continuous-time dynamics and at the impulsive instants, which can model practical systems more accurately. An impulse-time-dependent copositive Lyapunov function (IDCLF) is constructed, and the Razumikhin technique is adopted to develop conditions that ensure the globally exponential stability of T-S fuzzy positive systems with delayed impulses. The size constraint between the impulse delay and the bound of impulsive intervals is removed. A T-S fuzzy impulsive controller is designed in terms of the solutions to certain vector inequalities that are readily solvable. Numerical examples and a practical example of lipoprotein metabolism and potassium ion transfer model are given to demonstrate the effectiveness, advantages, and practicality of the proposed results.

This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This article proposes the design of an event-triggered control strategy for consensus of interconnected two-time scales systems with structured uncertainty. The control design under consideration ensures also that consensus is achieved with an overall guaranteed cost. Since each system involves processes evolving on both fast and slow time scales, two Zeno-free event-triggered mechanisms are designed to independently decide the sampling and transmission instants for the slow and fast states, respectively. As the first step, we design an event-triggering consensus protocol in the ideal/nominal case when the interconnected systems are not affected by uncertainties and the interactions happen over a fixed interaction network. Next, the results are extended in order to take into account structured uncertainties affecting the systems' dynamics. At this step, we go further and we provide sufficient conditions for event-triggering consensus with a guaranteed overall cost. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This article proposes a new cooperation framework of energy storage sharing that comprises prosumers, energy storage providers (ESPs), and a middle agent to achieve social energy optimality. In this framework, the prosumers share multiple energy storages of the ESPs via the agent. An energy sharing optimization problem minimizing the total energy cost is formulated involving the energy storage operation, the shiftable load schedule, and the energy trading with the utility. To solve the problem, a fast alternating direction multiplier method (ADMM) with restart which converges faster than traditional ADMM is adopted. Furthermore, a clearing scheme, named as an ideal profit realization ratio distribution model is introduced to distribute the participants’ net profits fairly. Simulation results show that the energy costs can be substantially reduced, and the net profit distribution is relatively fairer compared with the widely used Nash bargaining scheme, which ensures the feasibility of our framework in real applications.

In this brief, we investigate the fixed-time synchronization of competitive neural networks with multiple time scales. These neural networks play an important role in visual processing, pattern recognition, neural computing, and so on. Our main contribution is the design of a novel synchronizing controller, which does not depend on the ratio between the fast and slow time scales. This feature makes the controller easy to implement since it is designed through well-posed algebraic conditions (i.e., even when the ratio between the time scales goes to 0, the controller gain is well defined and does not go to infinity). Last but not least, the closed-loop dynamics is characterized by a high convergence speed with a settling time which is upper bounded, and the bound is independent of the initial conditions. A numerical simulation illustrates our results and emphasizes their effectiveness.

This article investigates the multitarget tracking problem for multiple Lagrangian plants (MLPs) in the presence of sampled-data interactions, uncertain dynamic terms, and input-to-output redundancy. Two classes of impulsive estimator-based control (IEC) algorithms, including the first- and higher-order IEC algorithms, are newly designed to observe the dynamic uncertain terms, estimate the states of the multiple targets, and finally solve the above-mentioned problem. Based on the properties of the small-value norms, Lyapunov stability theory, Schur stability theory, and Hurwitz criterion, some sufficient conditions and the convergence radius are derived for guaranteeing the convergence of these IEC algorithms. Finally, numerical simulations are performed on networked heterogeneous manipulators to verify the effectiveness of the proposed algorithms.

This article investigates the finite-time output multiformation tracking (OMFT) problem of networked heterogeneous robotic systems (NHRSs), where each robot model involves external disturbances, parametric uncertainties, and possible kinematic redundancy. Besides, the interactions among robotic systems are described as a directed graph with an acyclic partition. Then, several novel practical finite-time hierarchical control (FTHC) algorithms are designed. The convergence analysis of the closed-loop dynamics is extremely difficult due to the lack of effective analysis methods. Based on the mathematics induction and reductio ad absurdum, a new nonsmooth Lyapunov function is proposed to derive the sufficient conditions and settling time functions. Finally, numerical simulations are performed on the NHRS to verify the main results.

In this article, we propose an economic storage sharing framework for prosumers and energy storage providers (ESPs) to promote renewable energy utilization cooperatively. The optimal shared capacities of ESPs and the energy sharing profiles of prosumers are first derived via minimizing social energy costs. Then the storage sharing profits of ESPs and the energy sharing payments of prosumers are successively determined by the asymmetric bargaining-based benefits sharing model. Specifically, the prosumer group bargains with the ESPs with the nominal required capacity and the shared capacities as their bargaining power to share the storage sharing benefits. Then prosumers bargain with each other to share the energy sharing benefits with their bargaining power quantified by a nonlinear energy sharing mapping method. Therefore, the benefits sharing model based on the contributions of prosumers and ESPs is fair enough for the participants. Numerical simulation tests verify the efficiency of the proposed framework.

This paper introduces a distributed compensation approach for the global optimization with separable objective functions and coupled constraints. By employing compensation variables, the global optimization problem can be solved without the information exchange of coupled constraints. The convergence analysis of the proposed algorithm is presented with the convergence condition through which a diminishing step-size with an upper bound can be determined. The convergence rate can be achieved at O(lnT/√T). Moreover, the equilibrium of this algorithm is proved to converge at the optimal solution of the global optimization problem. The effectiveness and the practicability of the proposed algorithm is demonstrated by the parameter optimization problem in smart building.

In this paper, the leader-following output consensus problem for a class of uncertain nonlinear multiagent systems with unknown control directions is investigated. Each agent system has nonidentical dynamics and is subject to external disturbances and uncertain parameters. The agents are connected through a directed and jointly connected switching network. A novel two-layer distributed hierarchical control scheme is proposed. In the upper layer, to save the communication resources and to handle the switching networks, an event-triggered communication scheme is proposed, and a Zeno-free event-triggered mechanism is designed for each agent to generate the asynchronous triggering time instants. Furthermore, to avoid the continuous monitoring of the system states, a Zeno-free self-triggering algorithm is proposed. In the lower layer, to handle the unknown control directions problem and to achieve the output tracking of the local references generated in the upper layer, the Nussbaum-type function-based technique is combined with internal model principle. With the proposed two-layer distributed hierarchical controller, the leader-following output consensus is achieved. The obtained result is further extended to the formation control problem. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

In this paper, the global robust output regulation problem of a class of uncertain nonlinear systems is investigated by the event-triggered adaptive control law for the case of unknown control direction. The Nussbaum-type function-based technique is proposed to tackle the presence of unknown control direction. Then, by applying the adaptive control technique and the internal model principle, a new event-triggered adaptive control method is proposed. With the proposed corresponding event-triggered mechanism, the output regulation of the nonlinear systems can be realized regardless of the unknown control direction, meanwhile the Zeno behavior can be excluded. Finally, a numerical example is presented to verify the effectiveness of the proposed control law.

This paper investigates the sampled-delayed-databased consensus problem for upper-triangular multiagent nonlinear systems via output feedback. The sampled-delayed outputs are directly utilized to design observer-based piecewise continuous output feedback controllers. By properly constructing a Lyapunov-Krasovskii function, we obtain a parametric sequence and prove that it is convergent. Then, sufficient conditions are presented to guarantee the considered multiagent systems achieve global and exponential consensus. We also give the maximum admissible sampling period and transmission delay. Finally, simulation results show the validity and effectiveness of the newly proposed methods.

The paper proposes a two-stage energy sharing framework for a new prosumer microgrid with renewable energy generation, multiple storage units, and load shifting. In the first stage, a robust bilevel energy sharing model is formulated to provide a robust energy sharing schedule for prosumers and retailer overcoming the impact of the uncertainties of market prices and renewable energy. Through proper linearization techniques, the bilevel optimization problem is transformed into a single-level mixed integer linear programming problem that is practically solvable. In the second stage, an online optimization model is formulated for each prosumer to continually optimize its energy schedule at each hour according to the latest system state, and the proposed punishment mechanism is embedded for prosumers adjusting their previous energy sharing schedules. The simulation cases show the benefits of the energy sharing management framework.

This study addresses the stability and L1-gain analysis for impulsive positive systems (IPSs). By constructing a discretized copositive Lyapunov function, a sufficient condition ensuring the asymptotic stability is established for IPSs under the range dwell time constraint. Based on the stability result, the L1-gain characterization is further analyzed. The obtained criteria can be checked by the linear programming method. The theoretical results obtained are demonstrated by a numerical example.

This paper presents a fuzzy-model-based method for solving the consensus problem of a class of nonlinear multi-agent systems (MASs) with input saturation. Since each agent has nonlinear dynamics, the system is not asymptotically null controllable with bounded controls (ANCBC). Therefore, the widely-used low-gain feedback method for designing consensus protocols of MASs with input saturation can no longer work. To this end, the Takagi-Sugeno (T-S) fuzzy model is adopted to formulate the error dynamics of those nonlinear follower agents with input saturation as well as a leader with time-varying states. Accordingly, by using the properties of convex hull, a set of invariance condition in the format of linear matrix inequality (LMI) is designed. Furthermore, by enlarging the shape reference set, the estimation of the attraction domain can be obtained. Simultaneously, by viewing the control gain as an extra free parameter in the LMI optimization procedure, the leader-follower consensus algorithm is proposed, which guarantees that all followers with input saturation can track the leader, and they can asymptotically reach consensus. Finally, numerical experiments validate the effectiveness of the proposed anti-saturation consensus algorithm.

This paper studies the synchronization problem of coupled delayed multistable neural networks (NNs) with directed topology. To begin with, several sufficient conditions are developed in terms of algebraic inequalities such that every subnetwork has multiple locally exponentially stable periodic orbits or equilibrium points. Then two new concepts named dynamical multisynchronization (DMS) and static multisynchronization (SMS) are introduced to describe the two novel kinds of synchronization manifolds. Using the impulsive control strategy and the Razumikhin-type technique, some sufficient conditions for both the DMS and the SMS of the controlled coupled delayed multistable NNs with fixed and switching topologies are derived, respectively. Simulation examples are presented to illustrate the effectiveness of the proposed results.

The bipartite consensus problem of multiple two-time scales agents over the signed digraph, where both cooperative and competitive interactions exist among the agents, is considered with a new distributed protocol. Sufficient conditions for bipartite consensus is presented in terms of easily checkable algebraic Riccati equation (ARE). Compared with the existing result on consensus of multiple two-time scales agents, the communication topology here is more generic. Moreover, the upper bound of the singular perturbation parameter is also presented. Simulation examples are given to illustrate the effectiveness of the proposed results.

This paper studies the global synchronization of complex dynamical network (CDN) under digital communication with limited bandwidth. To realize the digital communication, the so-called uniform-quantizer-sets are introduced to quantize the states of nodes, which are then encoded and decoded by newly designed encoders and decoders. To meet the requirement of the bandwidth constraint, a scaling function is utilized to guarantee the quantizers having bounded inputs and thus achieving bounded real-time quantization levels. Moreover, a new type of vector norm is introduced to simplify the expression of the bandwidth limit. Through mathematical induction, a sufficient condition is derived to ensure global synchronization of the CDNs. The lower bound on the sum of the real-time quantization levels is analyzed for different cases. Optimization method is employed to relax the requirements on the network topology and to determine the minimum of such lower bound for each case, respectively. Simulation examples are also presented to illustrate the established results.

In this paper, the average consensus problem of multi-agent systems under logarithm quantized communication is investigated. The logarithm quantizer with finite quantization levels is considered. A sufficient condition is derived to ensure the convergence of the multi-agent system. Finally, a numerical example is given to show the effectiveness of the result obtained in this paper.

This paper is concerned with the consensus problem of discrete-time multi-agent systems (MASs) over lossy communication channels. The communication connection between agents at initial time is assumed to be accessible, which ensures that the system can reach consensus over ideal communication channels. However, the communication channel can be fading in practice, packet loss occurs inevitably. In this paper by modeling the packet losses as a Markov process with defective transition probabilities, a new description of the scenario of packet losses is addressed. Since the transition probabilities considered here comprise three types: known, uncertain within given intervals, and unknown, our model is more general and practical than previous results. A distributed protocol is introduced to achieve dynamic consensus. Sufficient condition for the mean-square consensus of discrete-time MASs is derived in the form of linear matrix inequalities. Numerical example is also given to illustrate the effectiveness of the theoretical results.

In this paper, synchronization of continuous dynamical networks with discrete-time communications is studied. Though the dynamical behavior of each node is continuous-time, the communications between every two different nodes are discrete-time, i.e., they are active only at some discrete time instants. Moreover, the communication intervals between every two communication instants can be uncertain and variable. By choosing a piecewise Lyapunov-Krasovskii functional to govern the characteristics of the discrete communication instants and by utilizing a convex combination technique, a synchronization criterion is derived in terms of linear matrix inequalities with an upper bound for the communication intervals obtained. The results extend and improve upon earlier work. Simulation results show the effectiveness of the proposed communication scheme. Some relationships between the allowable upper bound of communication intervals and the coupling strength of the network are illustrated through simulations on a fully connected network, a star-like network, and a nearest neighbor network.

In this paper, an overview on pinning control of complex dynamical networks is reported. The research works on pinning control of complex dynamical networks are investigated thoroughly from seven aspects, including the dynamic behavior of the nodes, the structures of the networks, the control purpose, the control method, the methods of theoretical analysis, the implementation strategies and some other aspects of the research results. The unsolved problems existing in pinning control are also discussed, which will give some suggestions on future research works.