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.

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.

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.

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 paper delves into a class of distributed nonconvex optimization control (DNOC) problems for multiagent systems (MASs), aiming to attain consensus among agents while minimizing the collective sum of local cost functions, referred to as the global cost function, through local information exchange. To accomplish this objective, it is introduced a novel distributed predefined-time optimal (DPTO) control algorithm. We prove that the proposed DPTO control algorithm guides the system states towards convergence to the global optimal solution within a user-defined time frame, provided that the global cost function satisfies the Polyak-Lojasiewicz condition, which is less stringent than the standard strong convexity condition. Finally, theoretical findings are substantiated through simulation studies.

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 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.

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.

Due to the widespread adoption of distributed generators and the increasing diversity of loads, AC microgrid has emerged as a prominent research area. However, there is a dearth of research outcomes that address the achievement of precise AC bus voltage/frequency restoration and active/reactive power sharing control objectives within a predefined time. In this paper, a distributed predefined-time secondary controller is presented to fulfill the aforementioned objectives without load power, which is accomplished by defining composite errors and employing a distributed predefined-time observer. Notably, the convergence time of bus voltage and the output power of converters can be tailored by a predefined parameter. Numerical simulation tests have been conducted to verify the efficacy of the predefined-time method in scenarios involving load variations and plug-and-play.

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 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 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.

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 considers the distributed economic dispatch problem in power systems. A novel constrained consensus-based iterative algorithm is proposed to cooperatively search the optimal incremental cost. The algorithm is carried out by alternately executing a continuous-time finite-time consensus algorithm and employing local projection operations only when consensus variables reach agreement. At each iteration, the initial values of consensus variables are restored locally based on the deviation between the consensus value in the last iteration and its projection on local constraints. Compared to existing methods, our method only needs a single consensus algorithm and does not require continuous projection operations. Thus each node in our algorithm only transmits a single variable to neighbours and the search time of the optimal solution is significantly reduced. Convergence analysis is given, and numerical examples are presented to show the effectiveness of our method with comparison to some existing results.