This article is concerned with the output feedback security tracking control of a class of disturbed second-order nonlinear systems against denial-of-service (DoS) attacks. Novel radial basis function neural network (RBFNN)-based finite-time state observers are developed to estimate the system’s unavailable states. Adaptive filters are proposed to suppress the influences of disturbances and RBFNN approximation errors. Then, an RBFNN-based security controller is designed to alleviate the effects of nonlinear dynamics and DoS attacks based on the signals of observers and filters. It is established that the uniformly ultimately bounded output tracking results of the system can be obtained by utilizing an RBFNN-based finite-time observation and filtering compensation control designs through Lyapunov stability analysis. Comparative simulations are employed to display the feasibility and superiority of the designed RBFNN-based observation and filtering compensation control schemes of a nonlinear autonomous marine system (AMS).

Solder paste position offset is a critical stencil printing quality indicator, the prediction of which using available data is important for printing quality improvement. However, existing data-driven works on solder paste position offset prediction often suffer from their poor adaptation to changing printing stages and small training samples. To address the above problems, we propose a novel multi-scale convolutional attention network based on meta-transfer strategy for solder paste position offset prediction in surface mount technology assembly lines. First, to better capture the fluctuating trends of printing sequence in different cleaning cycles, we propose a multi-scale convolutional attention network, in which a multi-head attention with position encoding is designed at each level to adaptively capture the changing trend of solder paste position offset at different stages. Then, to improve the precision of prediction model under insufficient samples, we introduce a meta-transfer strategy. Specifically, the parameters of the model are updated in the meta-training process through the bi-level optimization and the parameter fine-tuning method is used in the meta-testing process to improve the prediction performance of proposed model under complex working conditions. The proposed method is verified over practical dataset and compared with other advanced methods. The results show that the proposed method can accurately and robustly achieve solder paste position offset prediction, especially in small sample dataset.

The coordination of electric vehicle battery charging stations (BCSs), battery swapping stations (BSSs), and residential buildings (RBs) within a community microgrid (CM) presents a significant opportunity to enhance system flexibility and reduce operational costs. However, the randomness of user behaviors and the intermittency of renewable energy pose threats to the stability of the CMs. This article investigates the robust cooperative operation of CMs with BCSs and BSSs, allowing for energy sharing. First, a robust energy management framework utilizing cooperative game theory is developed among a BCS, a BSS, and a set of RBs within a CM, achieving proactive energy sharing and fair distribution of benefits. Then, an alternating direction method of multipliers algorithm combined with robust optimization is designed to address the energy management problem in a distributed manner, ensuring the protection of private data and enhancing system robustness. Finally, numerical examples verify the effectiveness and superiority of the proposed method. In comparison with the noncooperative benchmark, the proposed method achieves a reduction in operational costs of 5.69%, 9.1%, and 8.48% for the BCS, BSS, and RBs, respectively.

The battery swapping mode, due to its high energy replenishment efficiency for electric vehicles (EVs), has progressively seen widespread application. However, uncertainties inherent in battery swapping stations (BSSs) pose challenges to ensuring profitability and service quality in actual operations. To overcome these challenges, the chance constraint is firstly introduced in this paper to guarantee quality of service (QoS). A BSS multi-stage stochastic optimization model with risk consideration is then proposed by adopting the conditional value at risk (CVaR) model, aiming to reduce costs under acceptable QoS levels. The stochastic dual dynamic integer programming (SDDiP) algorithm is utilized for developing a real-time multi-stage BSS stochastic planning algorithm. Simulation results demonstrate that the proposed optimization method can effectively increase revenue while ensuring service quality, providing feasible decision-making strategies for real-time BSS operations.

In this article, two novel distributed variational Bayesian (VB) algorithms for a general class of conjugate-exponential models are proposed over synchronous and asynchronous sensor networks. First, we design a penalty-based distributed VB (PB-DVB) algorithm for synchronous networks, where a penalty function based on the Kullback-Leibler (KL) divergence is introduced to penalize the difference of posterior distributions between nodes. Then, a token-passing-based distributed VB (TPB-DVB) algorithm is developed for asynchronous networks by borrowing the token-passing approach and the stochastic variational inference. Finally, applications of the proposed algorithm on the Gaussian mixture model (GMM) are exhibited. Simulation results show that the PB-DVB algorithm has good performance in the aspects of estimation/inference ability, robustness against initialization, and convergence speed, and the TPB-DVB algorithm is superior to existing token-passing-based distributed clustering algorithms.

It is of great significance to develop a coordinated battery swapping station (BSS) recommendation method to reduce the cost of electric vehicles (EVs) and optimize the operation of BSS system. In this paper, the BSS recommendation problem is studied by comprehensively considering the battery swapping cost, diversity of BSS capacities, and differentiated demands of EVs, so as to be as close to the actual situation as possible. To describe the interactions among EVs, we propose a game theory-based approach to recommend appropriate BSSs for EVs to minimize the total cost (namely the sum of travel cost and battery swapping cost) of each EV. Under the game framework, a price function is designed to regulate the swapping price of each BSS, which acts as a coordination signal to induce EVs to join the game and also to alleviate congestion of BSSs. Then, an iterative algorithm is devised to seek the Nash equilibrium, through which a suitable BSS is determined for each EV. Compared to the shortest distance approach, the case studies indicate that the proposed approach can effectively reduce the average cost of EVs, improve the success rate of battery swapping, and balance the utilization ratio of BSSs.

Optimal energy management of smart grids requires the information exchange between devices, which may disclose private information to the adversaries and further lead to great losses. To this end, this article considers the privacy-preserving optimal energy management problem for smart grids, which integrates both the power allocation of distributed energy resources on the supply side and the demand response of distributed load demands on the demand side. We first propose a cloud-edge computing structure of the smart grid and model the optimal energy management problem as the maximization problem of social welfare including the supply-side net benefit and the demand-side net utility, while maintaining the supply–demand balance and satisfying the operating constraints. A privacy-preserving average consensus algorithm is then developed, where each node sends the projected states to their neighbors to protect the privacy of the initial state. By applying the privacy-preserving average consensus algorithm, we propose a distributed privacy-preserving optimal energy management algorithm based on the generalized alternating direction method of multipliers. Finally, simulation examples are provided to validate the effectiveness of the proposed algorithms.

The development of electric vehicle (EV) industry is facing a series of issues, among which the efficient charging of multiple EVs needs solving desperately. This paper investigates the coordinated charging of multiple EVs with the aim of reducing the charging cost, ensuring a high battery state of charge (SoC), and avoiding the transformer overload. To this end, we first formulate the EV coordinated charging problem with the above multiple objectives as a Markov Decision Process (MDP) and then propose a multi-agent deep reinforcement learning (DRL)-based algorithm. In the proposed algorithm, a novel interaction model, i.e., communication neural network (CommNet) model, is adopted to realize the distributed computation of global information (namely the electricity price, the transformer load, and the total charging cost of multiple EVs). Moreover, different from the most existing works which make specific constraints on the size, the location, or the topology of the distribution network, what we need in the proposed method is only the transformer load. Besides, due to the use of long and short-term memory (LSTM) for price prediction, the proposed algorithm can flexibly deal with various uncertain price mechanisms. Finally, simulations are presented to verify the effectiveness and practicability of the proposed algorithm in a residential charging area.

This article investigates a bounded cluster synchronization problem of dynamical systems, which can be of the generic linear type or Lipschitz nonlinear type, over directed switching network. Each cluster is equipped with a virtual leader which produces the desired trajectory for the agents to track. It is required only a fraction of systems is influenced by the leader. The interaction topology, which describes the information exchange among the dynamical systems as well as the virtual leaders, is allowed to be time varying with a well-defined average over an infinite horizon. That is, each augmented cluster, consisting of the agents as well as the corresponding virtual leader, in the time-average network topology is required to have a directed spanning tree. We then transform the cluster synchronization problem into a stability problem via the averaging method. It is proved that the convergence property for both types of dynamical systems is exclusively determined by the averaging system if the network topology switches sufficiently fast compared to original systems. Finally, it is concluded that if the intracluster coupling strength of the time-average topology is stronger than a threshold, then bounded cluster synchronization can be realized for a fast switching linear or nonlinear systems. Two examples are provided to verify our results.

This article considers the resilient consensus problems of discrete-time complex cyber-physical networks under F-local deception attacks. A resilient consensus algorithm, where extreme values received are removed by each node, is first introduced. By utilizing the presented algorithm, a necessary and sufficient condition to ensure resilient consensus in the absence of trusted edges is then provided by means of network robustness. We further generalize the notion of network robustness and present the necessary and sufficient condition for the achievement of resilient consensus in the presence of trusted edges. In addition, we show that through appropriately assigning the trusted edges, the resilient consensus can be reached under arbitrary communication network. Finally, the validity of the theoretical findings is demonstrated by simulation examples.

In network systems, a group of nodes may evolve into several subgroups and coordinate with each other in the same subgroup, i.e., reach cluster synchronization, to cope with the unanticipated situations. To this end, the leader-following practical cluster synchronization problem of networks of generic linear systems is studied in this paper. An event-based control algorithm that can largely reduce the amount of communication is first proposed over directed communication topologies. In the proposed algorithm, each node decides itself when to transmit its current state to its neighbors and how to update its controller according to the estimations of the states of it and its neighbors. Then, the Lyapunov method is utilized to perform the convergence analysis. It shows that the practical cluster synchronization can be ensured by choosing appropriate parameters no matter what kind of estimation for the state is applied. Furthermore, the Zeno behavior is also excluded for each node under some mild assumptions. Besides, three kinds of common estimations for the states including zero-order hold model, first-order approximate model, and high-order model-based estimations are, respectively, analyzed from the perspective of the exclusion of Zeno behavior. Finally, the validity of the proposed algorithm is demonstrated, the effects of the concerned parameters are simply presented, and the effects of the three estimations are also compared through several simulations.

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

The bipartite consensus problem for a group of homogeneous generic linear agents with input saturation under directed interaction topology is examined. It is established that if each agent is asymptotically null controllable with bounded controls and the interaction topology described by a signed digraph is structurally balanced and contains a spanning tree, then the semi-global bipartite consensus can be achieved for the linear multiagent system by a linear feedback controller with the control gain being designed via the low gain feedback technique. The convergence analysis of the proposed control strategy is performed by means of the Lyapunov method which can also specify the convergence rate. At last, the validity of the theoretical findings is demonstrated by two simulation examples.

The containment control is studied for the second-order multiagent systems over a heterogeneous network where the position and velocity interactions are different. We consider three cases that multiple leaders are stationary, moving at the same constant speed, and moving at the same time-varying speed, and develop different containment control algorithms for each case. In particular, for the former two cases, we first propose the containment algorithms based on the well-established ones for the homogeneous network, for which the position interaction topology is required to be undirected. Then, we extend the results to the general setting with the directed position and velocity interaction topologies by developing a novel algorithm. For the last case with time-varying velocities, we introduce two algorithms to address the containment control problem under, respectively, the directed and undirected interaction topologies. For most cases, sufficient conditions with regard to the interaction topologies are derived for guaranteeing the containment behavior and, thus, are easy to verify. Finally, six simulation examples are presented to illustrate the validity of the theoretical findings.

Web Services standard-based system resource management is a new direction. In this paper, in order to verify the WS-Management standard whether to satisfy the realistic system resource management requirements, we use IBM MAPE categories to find more WS-Management related use cases. We design three typical use cases based on MAPE principles. Finally, we make a conclusion and propose our next-step work.

Cloud computing environment requires a more open and loosely-coupled service and resource management model. Web Services for Management specification (WS-Management), as an initiative of DMTF organization, can help to manage IT resources cross multiple domains in cloud environment. In this paper, we propose a novel WS-Management-based Cloud-oriented resource management model. We describe the main components of this model. And then, we discuss our management model verification experimental scheme focusing on DASH resource. Finally, we present conclusion and future work.