Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method.

Abstract The rapid growth of the low-altitude economy, including unmanned aerial vehicles (UAVs) and urban air mobility (UAM), is reshaping industries from transportation to emergency response. Powered by advances in fifth-generation (5G) and 5G-advanced (5.5G) connectivity, artificial intelligence (AI), and new energy systems, these platforms are becoming increasingly autonomous and capable. However, their growing software complexity introduces critical cybersecurity risks. Vulnerabilities in communication protocols, onboard firmware, and AI systems can be exploited to hijack UAVs, disrupt operations, or leak sensitive data. While research has addressed isolated aspects, a unified security perspective is still lacking. This work presents a systematic review of software-level security challenges and defenses in low-altitude UAV/UAM systems. We first categorize major attack surfaces across communication, firmware, and AI layers. Furthermore, we survey defense mechanisms suited to real-time, resource-constrained aerial platforms. Finally, we propose future directions, including quantum-resistant communication protocols, hardware-software cosecurity, and edge-AI-driven architectures. Our work aims to inform researchers, practitioners, and regulators in developing integrated, resilient security strategies for the evolving low-altitude ecosystem.

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

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

Online Multi-Object Tracking (MOT) plays a pivotal role in autonomous systems. The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role. This paper proposes a learning and graph-optimized (LEGO) modular tracker to improve data association performance in the existing literature. The proposed LEGO tracker integrates graph optimization, which efficiently formulates the association score map, facilitating the accurate and efficient matching of objects across time frames. To further enhance the state update process, the Kalman filter is added to ensure consistent tracking by incorporating temporal coherence in the object states to further enhance the state update process. Our proposed method, utilising LiDAR alone, has shown exceptional performance compared to other online tracking approaches, including LiDAR-based and LiDAR-camera fusion-based methods. LEGO ranked 3rdamong all trackers (both online and offline) and 2ndamong all online trackers in the KITTI MOT benchmark for cars1, at the time of submitting results to KITTI object tracking evaluation ranking board. Moreover, our method also achieves competitive performance on the Waymo open dataset benchmark.

Vision–language models are emerging as a unified paradigm for perception, prediction, and planning in autonomous driving. However, most existing approaches still rely on shallow fusion between visual and textual features, leading to weak cross-modal interaction, feature drift, and poor adaptability in complex scenes. This work present ScopeDrive, an end-to-end VLM framework that enables deep semantic alignment and adaptive reasoning through two novel components. The text-anchored calibrator transforms textual semantics into multiscale anchors that progressively calibrate visual features across layers, strengthening cross-modal correspondence. The density-aware agent modulator estimates scene complexity and dynamically adjusts attention distribution, allowing the model to focus on dense or dynamic regions when needed. Evaluated on the DriveLM, DriveBench, and NuScenes-QA benchmarks, ScopeDrive surpasses both lightweight and large-scale baselines, achieving a BLEU-4 of 53.27 and METEOR of 38.75 on DriveLM while maintaining only 328 M parameters. It also delivers state-of-the-art performance on perception and planning tasks under both clean and corrupted conditions. These results demonstrate that ScopeDrive effectively breaks the shallow-fusion barrier, offering a lightweight yet semantically aligned foundation for interpretable autonomous-driving intelligence.

Distributed optimization, as a key technology for collaborative intelligence in multiagent systems, has been widely applied in sensor networks, deep learning, and smart grids. Although numerous effective algorithms have been proposed, classical methods typically rely on idealized assumptions, such as accurate objective information, perfect communication channels, and trustworthy system environments. However, these assumptions are frequently violated in real-world applications. To bridge the gap between theory and practice, distributed optimization under information constraints has emerged as a research focus. This survey provides a systematic overview of recent advances in this field. We categorize information constraints based on their origin into three primary types: i) observational constraints, including stochastic objectives, online optimization, and zeroth-order methods; ii) communication constraints, such as random network topologies, delays, asynchronous updates, and communication-efficient strategies; and iii) system-level constraints, encompassing privacy preservation and Byzantine-resilient optimization. This survey reviews the research progress and challenges associated with each constraint category. Furthermore, we use two representative case studies to analyze the practical application of these algorithms and the origins of information constraints in real-world problems. Finally, we explore promising future research directions.

This article presents a novel model-free distributionally robust framework for a challenging equilibrium-seeking problem (ESP) under fully unknown coupled dynamics. We consider a scenario in the ESP where the state transitions of players are governed by an unknown coupled dynamic system, and each player aims to minimize its own cost function. By predicting the stochastic distribution of player states through Gaussian process regression, we propose a novel distributionally robust approximation (DRA) that transforms the complex ESP with unknown coupled dynamic system into a solvable distributionally robust optimization problem. The gradient of the DRA's objective function is quantified, ensuring solvability. The effectiveness of the proposed DRA framework is evaluated through a nonlinear system, demonstrating comparable performance to model-based methods without requiring any dynamic model.

Game theory has emerged as a fundamental framework for modeling and analyzing strategic interactions and decision-making among multiple agents, and has witnessed rapidly growing impact in cyber–physical systems over the past decade. Its integration with dynamic systems has driven major theoretical and technological advances in a wide range of applications, including smart grids, autonomous driving, robotic swarms, and networked control systems. In particular, distributed games in dynamic systems and their equilibrium learning mechanisms have attracted increasing attention due to their scalability, lightweight information exchange, and real-time implementability. This article provides a comprehensive survey of distributed games in dynamic systems, where agents interact only with local neighbors while collectively achieving global equilibrium and stability. First, the foundational theories of distributed dynamic games under three representative classes of systems: linear dynamic systems, nonlinear dynamic systems, and uncertain dynamic systems, are presented. Then, state-of-the-art distributed equilibrium learning and control methods are reviewed, including gradient-based dynamics, payoff-based learning, best-response dynamics, and learning-based approaches. To demonstrate the practical relevance and impact of distributed games in dynamic systems, representative application domains are discussed in detail. Finally, several promising future research directions are outlined, highlighting open challenges at the intersection of distributed games, learning, and dynamic systems.

Large-scale interconnected systems have become effective solutions to coordinate multiple subsystems and efficiently allocate resources. These systems support flexible scalability while ensuring strong reliability, ease of operation, and performance. The core issue resides in developing scalable controllers that coordinate dynamic subsystem couplings to achieve global stability through localized adjustments during plug-and-play operations. This article systematically reviews recent advances in scalable control technologies for large-scale interconnected systems. First, control problems are categorized based on system modeling approaches. Second, the corresponding control strategy design methods are thoroughly analyzed for each modeling framework. Typical scalable control techniques developed for such systems are also summarized. Finally, key challenges and future research directions in dynamic topology adaptation, heterogeneous system integration, and improved computational efficiency are discussed.

Reliable protocol knowledge is often difficult to obtain in industrial networks, as industrial communications come with limited documentation, vendor-specific encodings, and opaque payloads. This lack of transparency hinders message interpretation and protocol analysis. To recover this missing protocol knowledge, network-trace-based protocol reverse engineering (PRE) infers message structure, field roles, and interaction logic directly from recorded traces. This enables protocol-aware intrusion detection, process monitoring, and protocol testing and fuzzing without access to device internals. Although PRE has advanced rapidly, existing techniques are developed under diverse objectives and assumptions. As a result, it is often unclear how isolated results relate to an end-to-end reverse-engineering workflow, and how evaluation outcomes should be compared across tasks and protocols. In this article, we cast reverse engineering of industrial protocols from network traces as a task-driven pipeline and articulate a unified task decomposition spanning message type identification, protocol syntax and semantic inference, payload pattern recognition and semantic inference, and protocol state machine reconstruction. For each task, we describe key methodological themes, common evaluation practices, and practical limitations that affect robustness and deployability in industrial settings. We further discuss security, privacy, and ethical risks that accompany increasingly capable PRE, and identify promising research directions toward more systematic, dependable, and deployment-oriented PRE methodologies.

Fixed-time consensus control offers an explicit upper bound on the settling time that is independent of initial conditions, making it particularly valuable for time-critical applications. This survey reviews recent advances in this field, with emphasis on two primary directions: extending fixed-time consensus to broader classes of dynamical multiagent systems, and designing engineered protocols that enhance practical applicability. First, we examine fixed-time consensus results for general dynamical systems, including well-established methods for general linear multiagent systems and emerging approaches for specific classes of nonlinear systems, where a unified theoretical framework remains elusive. Notably, conventional fixed-time consensus protocols often induce excessively large initial control inputs and lack fully distributed settling-time estimation, motivating the development of protocols with engineered features. Second, we review recent advances in specialized consensus protocols that address these practical challenges, focusing primarily on finite-time consensus protocols with bounded control inputs and fully distributed fixed-time consensus protocols, while also covering recent efforts on event-triggered implementations and secure strategies under cyberthreats. The practical utility of these protocols is demonstrated through two case studies: position synchronization of brushless dc motor systems and frequency regulation in islanded microgrids. Finally, key challenges and promising directions for future research are discussed.

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

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

Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything (V2X) cooperative perception (CP), which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP plays a crucial role in extending the perception range, increasing detection accuracy, and supporting more robust decision-making and control in complex environments. This article provides a comprehensive survey of recent developments in V2X CP, introducing mathematical models that characterize the perception process under different collaboration strategies. Key techniques for enabling reliable perception sharing, such as agent selection, data alignment, and feature fusion, are examined in detail. In addition, major challenges are discussed, including differences in agents and models, uncertainty in perception outputs, and the impact of communication constraints such as transmission delay and data loss. This article concludes by outlining promising research directions, including privacy-preserving artificial intelligence methods, collaborative intelligence, and integrated sensing frameworks to support future advancements in V2X CP.

This paper is concerned with cluster consensus of multi-agent systems (MASs) with homogeneous and heterogeneous dynamics. Firstly, a resilient dynamic event-triggered (RDET) mechanism is proposed by introducing two parameters in the dynamic triggering threshold to describe busy and free networks of each cluster. An auxiliary function is configurated to describe the network status of agents in different clusters. Agents allocated in multiple clusters are classified into two categories: some agents are in the busy network environment and the rest are in the free network environment. The resulting consensus error system is modeled as an augmented system involving two time-varying delays with different bounds. Then, some sufficient criteria are derived for the stability analysis of homogeneous and heterogeneous MASs, respectively. Based on the obtained stability criteria, an algorithm consisting of particle swarm optimization (PSO) and a genetic algorithm (GA) is designed for a co-design of control gains and event-triggered parameters. Finally, a numerical example of satellite formation flying is simulated to illustrate the merits of the developed theoretical results.Note to Practitioners—The objective of this article is to design addresses the challenge of achieving consensus in MASs with diverse dynamics, such as those found in autonomous vehicle fleets or sensor networks. We’ve introduced a RDET mechanism that can handle both busy and free network environments by adjusting its triggering threshold based on network status. This allows agents to communicate efficiently, reducing the need for constant communication and conserving resources. Agents in multiple clusters are categorized into those in high-traffic (busy) and low-traffic (free) networks. We’ve developed a model for the consensus error system that accounts for time-varying delays, which is crucial for stability analysis in both homogeneous and heterogeneous MASs. To optimize performance, we propose an algorithm combining PSO and GA. This powerful tool co-designs control gains and event-triggered parameters, ensuring efficient and stable communication strategies for all agents regardless of their specific dynamics. In practical terms, this work provides a robust framework to improve inter-agent cooperation in complex industrial scenarios, such as large-scale robot swarms, intelligent transportation systems, or even satellite constellation management. By implementing the RDET mechanism with our optimization algorithms, practitioners can expect to achieve tighter synchronization, reduced communication costs, and better overall system resilience, as demonstrated through a simulation of satellite formation flying.

In autonomous driving, LiDAR and radar are crucial for environmental perception. LiDAR offers precise 3D spatial sensing information but struggles in adverse weather like fog. Conversely, radar signals can penetrate rain or mist due to their specific wavelength but are prone to noise disturbances. Recent state-of-the-art works reveal that the fusion of radar and LiDAR can lead to robust detection in adverse weather. Current approaches typically fuse features from various data sources using basic convolutional/transformer network architectures and employ straightforward label assignment strategies for object detection. However, these methods have two main limitations: they fail to adequately capture feature interactions and lack consistent regression constraints. In this paper, we propose a bird’s-eye view fusion learning-based anchor box-free object detection system. Our approach introduces a novel interactive transformer module for enhanced feature fusion and an advanced label assignment strategy for more consistent regression, addressing key limitations in existing methods. Specifically, experiments show that, our approach’s average precision ranks$1^{st}$and significantly outperforms the state-of-the-art method by 13.1% and 19.0% at Intersection of Union (IoU) of 0.8 under “Clear+Foggy” training conditions for “Clear” and “Foggy” testing, respectively. Our code repository is available at:https://github.com/yyxr75/RaLiBEV.

This article is concerned with distributed adaptive resource allocation over general digraphs with resource-demand constraints. The central aim is to tackle two essential challenges in distributed resource allocation, namely, scalable implementation and anytime feasibility, ensuring continuous satisfaction of constraints. For this purpose, two novel fully distributed optimization algorithms, featuring sum-based and product-based schemes for adaptive gains, are first developed. It is shown that these algorithms offer several advantageous features in terms of fully distributed implementation without global knowledge and algorithm simplicity as well as anytime feasibility guarantees over existing methods. Notably, the incorporation of a double-layer adaptive control law with a damping term into each algorithm prevents the continuous growth of adaptive gains, thus avoiding excessively large system gain values and enhancing practical applicability in real-world scenarios. Furthermore, by constructing appropriate Lyapunov functions, rigorous convergence analysis confirms that both algorithms achieve the optimal resource allocation and global asymptotic convergence. Finally, several simulation case studies are conducted to validate the efficiency of the proposed algorithms.

The distributed energy management (DEM) is of significance for smart grids due to the growing concern over potential cyber threats. This study delves into a multiobjective DEM problem that encompasses economic and environmental costs, as well as transmission losses, while also considering the impact of persistent Denial-of-Service (DoS) attacks. To tackle this challenge, a novel distributed optimization algorithm over a digraph is proposed, leveraging a zeroth-order scheme to handle unavailable gradients and incorporating momentum terms to provide accurate descent directions. The theoretical analysis demonstrates the algorithm's ability to achieve a linear convergence rate while ensuring real-time maintenance of decision variables within the feasible domain. Building on this, a novel reputation-based resilient DEM framework is introduced to address scenarios involving persistent DoS attacks. This framework calculates a reputation index for each communication link to monitor its reliability. Considering the impact of attacked links on network connectivity and their reputation indexes, corresponding strategies are devised. Specifically, if an attacked link disrupts network connectivity and its reputation index falls below the threshold, a connectivity restoration optimization algorithm is activated to reconstruct links, minimizing communication costs and alleviating information congestion. Finally, the effectiveness of the proposed algorithm and framework is validated through numerical simulations.

In the evolving landscape of modern power systems, islanded microgrids have contributed a significant role, offering superior resilience, power quality, and the efficient harnessing of distributed generators (DGs). The dynamic nature of such systems necessitates advanced control techniques to ensure stability and optimal operation. This article provides a brief review of recent advances in distributed accelerated (including finite-time, fixed-time, and prescribed-time) control strategies, with a particular focus on their use in the distributed secondary control of islanded ac microgrids. The spotlight is on the application of these distributed accelerated secondary control techniques in managing DGs. We investigate the fundamental principles of these techniques, their evolution over time, and pivotal technological advancements. A comparative analysis underscores the strengths and potential limitations of these control methodologies in context to DGs. Finally, we list some opportunities that these advanced control techniques offer in addressing challenges associated with the control of DGs in islanded microgrids, thereby paving the path for future research.

This article addresses the cooperative tracking control problem of linear discrete-time heterogeneous networked multiagent systems suffering from random communication constraints. The communication constraints, including random network delays and packet losses, exist in the forward and feedback channels of each agent as well as the channels between agents. A dual-prediction-based cooperative control scheme with correction is proposed. Specifically, a dual-prediction generator is developed to compensate actively for the adverse effects of random communication constraints in the three communication channels. Besides, a correction item is introduced to ensure that the predicted value tends to the actual value. A novel cooperative tracking control law with proportional and integral action is designed for each agent. A sufficient condition is obtained for the derived closed-loop system to maintain stability. Comparative experimental results on a networked three-motor system demonstrate the effectiveness of the proposed method.

As the Industrial Internet of Things (IIoT) continues to expand, the need for effective device identification becomes critical for securing industrial environments. Network traffic fingerprinting has emerged as an important technique for IIoT device identification, leveraging the unique communication patterns embedded in network traffic. Despite significant efforts in this area, a comprehensive overview of the relevant research is still missing. To address the lack of comprehensive research, this paper, for the first time, identifies critical knowledge gaps constraining IIoT device identification through network traffic analysis: obscure fingerprint feature space, limited generalizability to unknowns, and scarce data sources. Focusing on these gaps, existing methods are analyzed and summarized in detail across network traffic fingerprinting, IIoT device identification, and public IIoT datasets. Specifically, network traffic fingerprinting methods are categorized into three levels: Packet-level, flow-level, and business-level, and relevant methods are examined in terms of data formats, segmentation units, and extraction or generation techniques. In the context of IIoT device identification, tasks such as device type, model, and instance recognition, as well as abnormal device detection, are extensively investigated using rule-based, traditional machine learning- based, and deep learning-based approaches, with a focus on device fingerprints and application scenarios. Furthermore, main public datasets from the IoT, ICS, and IIoT scenarios are highlighted to support the development of fingerprinting and identification methods. Finally, several future research directions are proposed to guide new advancements in this area.

The Internet of Robotic Things (IoRT) has experienced rapid growth and garnered increased attention in recent years. Applications (Apps) play a crucial role in IoRT, as they provide users with an intuitive interface to access and operate services. However, as user demands increase, Apps become more complex, leading to longer operation sequences and more vulnerabilities. The existing testing methods for Apps can be categorized into random, reinforcement learning, and AI-based approaches. AI-based methods offer a solution to the low coverage efficiency of random-based methods and the weak guidance of reinforcement learning-based methods. However, current AI-based methods have difficulty in capturing long-term dependencies, resulting in low coverage and less detected crashes when testing Apps with long operation sequences. To address the limitation, we propose InforTest, a novel AI-based method based on the Informer prediction model and the component tree structure. InforTest leverages Informer, which excels at extracting long-term dependencies from operation sequences, to generate human-like moves for testing Apps. To improve the efficiency of training and prediction, InforTest uses the component tree, a concise structure to represent primary data sources, i.e., screenshots. After training InforTest on the Rico dataset, our experiments with Apps in the IoRT scenario demonstrated its superiority over existing methodologies such as Monkey, Humanoid, MUBot, and Ape. Notably, InforTest achieved significant enhancements in coverage rates (increases of 67%, 34%, 19%, and 27%, respectively) and in crash detection capabilities (improvements of 175%, 81%, 71%, and 139%, respectively).

The complex and precise mechanical mechanism of quadruped robots is prone to faults, which brings challenges to the reliability and stability of the system. Therefore, it is of significant to develop the fault diagnosis method for quadruped robots, which can provide effective fault information for active fault-tolerant control. In this article, we propose a novel fault detection and isolation method for quadruped robots based on convolution neural networks, gated recurrent units, and attention networks, which can detect and isolate joint faults in real time. The proposed method can automatically learn meaningful high-level spatial and temporal features from sensors data. The effectiveness of the method is verified by the Laikago robot compound fault data.

With the intelligence and automation of industrial Internet of Things, a new collaborative Cloud-Fog Automation paradigm has emerged. The emergence of federated learning (FL) has further enhanced the capabilities of Cloud-Fog Automation, making it possible to develop more secure and versatile collaborative industrial models. However, FL faces various security risks. More importantly, the security risks faced by FL when applied in Cloud-Fog Automation, along with corresponding security measures, have not yet been explored. To address this issue, we make an initial attempt to analyze the security of FL within the context of Cloud-Fog Automation, with the aim of facilitating the design of a more secure FL framework for this paradigm. Specifically, we first analyze the security risks that may be encountered at different phases, then analyze the challenges that need to be faced to resolve these risks. Subsequently, we conduct a systematic review of the state-of-the-art security solutions, and finally summarize the future research directions.

Close-quarters encounter scenarios, characterized by their potentially severe consequences, represent one of the most formidable challenges in ship collision avoidance decision-making and trajectory planning. An efficient collision avoidance framework must simultaneously satisfy critical requirements for real-time decision-making, accurate trajectory planning, and effective vessel maneuvering, all while maintaining rigorous compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). This framework serves as the critical safeguard for the navigation of maritime autonomous surface ships. To design an efficient framework, this study proposes an innovative method for real-time partitioning of collision-free navigable space, termed “adaptive spatio-temporal voxels”, which accounts for the ship’s maneuverability by incorporating the velocity obstacle concept. Furthermore, a spatio-temporal graph, incorporating COLREGs compliance, yields an optimal collision avoidance decision represented as a sequence of voxels. These voxel sequences subsequently serve as constraints within a model predictive control (MPC) framework to optimize and plan a safe, navigable trajectory. This framework has undergone rigorous testing in a variety of close-quarters encounter scenarios. Its computational performance, with both decision-making and trajectory optimization completed within 1s, effectively meets the demands of real-time maritime collision avoidance. The results demonstrate its ability to generate efficient collision avoidance trajectories in complex environments, significantly enhancing overall collision avoidance performance.

This article is concerned with fully distributed Nash equilibrium seeking in networked games under both undirected and directed communication graphs. New fully Nash equilibrium seeking strategies incorporating gradient-based optimization algorithms, consensus algorithms, and double-layer adaptive control laws are presented. In particular, the double-layer adaptive control laws are introduced to ensure that the control gains are not overlarge and free of dependence on any global information. This is achieved by adding a damping term to the adaptive parameter design such that the continuous increase in control gains is avoided. Theoretical analyses are conducted to prove that players' actions can be convergent to the Nash equilibrium under the proposed strategies. Moreover, it is shown that the developed strategies can be extended to accommodate the players with heterogeneous linear dynamics. Finally, numerical examples are provided to illustrate the effectiveness of the proposed methods.

In this article, we address the design issue of the proportional-integral observer (PIO) for partially state-saturated systems, which are affected by probabilistic quantizations and an accumulation-based event-triggered mechanism (ABETM). A comprehensive model is established to characterize partial state saturations, wherein only a part of the state variables are saturated while the remaining variables maintain normal conditions. To conserve communication resources, an ABETM is employed to determine the release of system measurements to the PIO. Before transmission over the communication network, the triggered signal is quantized through a probabilistic quantization mechanism. The goal of this article is to devise a PIO that guarantees ultimate boundedness of the estimation error dynamics in the mean square. Initially, a sufficient condition is derived to ensure that the estimation error dynamics are exponentially ultimately bounded in the mean square. Following this, necessary PIO gains are identified by solving specific matrix inequalities, and the efficacy of this proposed PIO approach is validated using a three-tank system simulation.

Many industrial parks, which are connected to the main grid, have integrated renewable energy to reduce carbon emission for achieving the goal of Industry 5.0. However, the optimal scheduling is challenging due to fluctuations in renewable energy generation. Hydrogen, which plays an important role in the future development of the power grid in Industry 5.0, offers an attractive option to coordinate with the batteries. This work focuses on the day-ahead scheduling of a hydrogen-based microgrid for an industrial park. A day-ahead scheduling model is established by taking into consideration the detailed nonlinear energy conversion behavior of the electrolyzer and fuel cell, as well as the two-timescale property of a battery energy storage system (BESS) and the hydrogen system, including an electrolyzer, a hydrogen energy storage system (HESS), and a fuel cell. Note that the optimization problem is a mixed integer nonlinear programming, which is challenging to be solved. A novel multilearning rate reinforcement learning algorithm is proposed and its convergence is also proved based on two-timescale stochastic approximation theory. Simulation results, based on real-world traces in Belgium at a 15-min resolution, are presented, which shows that the proposed method has a higher reward, lower-operating costs and less computing time. It is also found that the shorter scheduling period for the BESS can lead to reduced operating costs by decreasing the required purchasing power and the renewable energy curtailment power.

Autonomous marine vehicles, which provide a platform for the successful implementation of special tasks, such as maritime rescue, maritime measurement, and dangerous goods monitoring, have been widely utilized. In the last decade, considerable attention has been paid to the analysis, modeling, and networked control of autonomous marine vehicles. This survey provides recent advances in networked and DRL-based control for autonomous marine vehicles. Typical mathematical models of autonomous marine vehicles are introduced first as the foundation for control of autonomous marine vehicles. Then, networked and DRL-based control for autonomous marine vehicles is reviewed. Finally, some challenges and open issues are presented to motivate the future research.

In this paper, consensus tracking is investigated for multi-agent systems (MASs) with unknown disturbances. A K∞function-based control strategy is proposed to guarantee that the tracking errors between each follower and the leader are converged to a small neighborhood of zero in finite-time. To be more specific, by introducing an error auxiliary considering local interactions, a K∞function-based adaptive controller is designed such that the error auxiliary is constrained to an adjustable region. Then, through a matrix-based rigorous proof, practical consensus tracking is realized in finite-time for MASs. A distinguished feature of the K∞function-based control is its independence on the use of the bound information of unknown disturbances. Another advantage of the proposed controller is its capability of alleviating the input chattering issue. Finally, numerical examples are presented to validate the effectiveness of the K∞function-based controller.

Conventional automotive radar has been extensively utilized in advanced driver assistance systems and autonomous driving, with potential applications in future cooperative perception systems. However, compared to LiDAR-based perception, conventional radar-based perception technologies often encounter limitations such as the absence of elevation information and low resolution. These limitations impede their ability to detect and localize objects in the surrounding environment accurately. In recent years, the development of 4D imaging radar emerged as a promising solution to overcome these limitations. 4D imaging radar can measure the pitch angle, enhancing the understanding of the environment and improving object detection and localization accuracy. Due to the cost-effectiveness and operability in adverse weather conditions of 4D radar, its emergence has attracted attention from both the academic and industrial communities. However, the measurements obtained from 4D radar are subject to noise, primarily stemming from the multi-path propagation of radar signals. Additionally, 4D radar captures less geometry and semantic information than the more dense LiDAR point cloud. As a result, existing 3D object detection algorithms specifically developed for dense LiDAR point cloud may yield suboptimal performance when directly applied to sparse 4D radar point cloud data.

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving [1] . Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, sampling has been applied in a few image-based detectors and shown to outperform the widely applied depth-based splatting proposed in Lift-Splat-Shoot (LSS) [2] , even without image depth prediction [3] . However, the potential of sampling is not fully unleashed. As a result, this paper investigates the sampling strategy on the camera and 4D imaging radar fusion-based 3D object detection. In the proposed LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird’s eye view (BEV) features, respectively. They are sent to the core of LXL, called radar occupancy-assisted depth-based sampling , to aid image view transformation.

元宇宙可被视为一个社会化和虚拟化的网络空间, 与现实世界平行但互动. 得益于云计算和数字孪生的快速发展, 元宇宙正在将带有传统控制和滤波范式的工业自动化系统转变为信息物理社会融合系统. 在此情况下, 未来的工业自动化系统可能是在一定时间和空间范围内具有强大计算能力的现实世界系统与虚拟孪生系统的集成. 在该领域中, 元宇宙重点涉及信息传输管理、 用户的行为识别以及控制、 滤波和决策, 且性能和成本将是系统在网络空间和现实世界中行为的综合反映. 毫无疑问, 虚拟网络空间和现实世界之间的信息交换的内在特征, 唤起了对安全控制和滤波的新需求. 如何保证期望的系统性能和实现理想的参数设计, 已然成为该领域研究的重大挑战. 然而, 目前对元宇宙的研究主要集中在解决其社会关切和寻求其在各个领域的应用, 其在控制领域的发展仍处于初级阶段. 当网络空间与现实世界交换感兴趣的信息时, 恶意攻击不可避免, 这可能导致数据不可靠或隐私泄露. 因此, 定义合适的评估标准来揭示网络攻击的影响, 并提供面向工程的安全控制和滤波方案, 具有重要意义. 本期专题旨在推进工业元宇宙中安全控制与滤波的理论研究和技术开发, 服务于智能制造的发展需要. 感谢所有作者对此专题的贡献, 感谢所有为作者提供宝贵意见的评审人. 衷心感谢《信息与电子工程前沿(英文)》期刊执行副主编王飞跃教授鼓励我们组织本期专题, 以及编辑人员的全程支持.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

This article addresses the hybrid model predictive control of chiller systems via collaborative neurodynamic optimization. A mixed-integer optimization problem is formulated for the model predictive control of chiller systems to minimize power consumption, subject to various constraints including thermodynamic and energy-conservation constraints. It is then decomposed into a global and a binary optimization subproblem. A collaborative neurodynamic optimization approach is proposed to solve the subproblems sequentially. The approach is based on multiple pairs of projection neural networks and discrete Hopfield networks, assisted with a metaheuristic rule. The effectiveness of the approach is demonstrated through experiments based on the parameters and specifications of a chiller system.

The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things environments. Most of the industrial automation systems today are based on the traditional International Society of Automation (ISA)-95 model, with some recently transitioned to Cloud Automation systems. Latest developments in network connectivity technologies, artificial intelligence, and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this article, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative “Cloud-Fog Automation” paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision.

This article is concerned with the leader–follower formation control problem of multiple networked mobile robots (MRs), where the information exchanges over communication networks among the MRs suffer from replay attacks. The central aim is to develop an effective secure control scheme for the multiple-MR system such that the desired leader–follower formation control objectives are accomplished regardless of the simultaneous presence of replay attacks, network-induced delays, system uncertainties, and external disturbances. Toward this aim, an extended state observer is first constructed to estimate the unknown nonlinear terms consisting of system uncertainties and external disturbances. Then, leveraging the time-stamp technique, a dedicated data packet analyzer is developed for each MR to detect and handle replay attacks. Furthermore, a secure leader–follower formation control scheme, consisting of a network-based cooperative kinematic control law and two local kinetic control laws, is designed. Finally, both simulation and experiment results are provided to validate the efficacy of the proposed secure leader–follower formation control scheme.

A revolution in advanced manufacturing has been driven by digital technology in the fourth industrial revolution, also known as Industry 4.0, and has resulted in a substantial increase in profits for the industry. In a new paradigm of Industry 5.0, advanced manufacturing will step further and be capable of offering customized products and a better user experience. A number of key enabling technologies are expected to play crucial roles in assisting Industry 5.0 in meeting higher demands of data acquisition and processing, communications, and collaborative robots in the advanced manufacturing process. The aim of this survey is to provide novel insights into advanced manufacturing in Industry 5.0 by summarizing the latest progress of key enabling technologies, such as artificial intelligence of things (AIoT), beyond 5G communications, and collaborative robotics. Finally, key directions for future research to enable this vision to become a reality, such as the industrial metaverse, are outlined.

In this article, we present a collaborative neurodynamic optimization approach to distributed chiller loading in the presence of nonconvex power consumption functions and binary variables associated with cardinality constraints. We formulate a cardinality-constrained distributed optimization problem with nonconvex objective functions and discrete feasible regions, based on an augmented Lagrangian function. To overcome the difficulty caused by the nonconvexity in the formulated distributed optimization problem, we develop a collaborative neurodynamic optimization method based on multiple coupled recurrent neural networks reinitialized repeatedly using a meta-heuristic rule. We elaborate on experimental results based on two multi-chiller systems with the parameters from the chiller manufacturers to demonstrate the efficacy of the proposed approach in comparison to several baselines.

In this brief, the state estimation problem is investigated for a class of randomly delayed artificial neural networks (ANNs) subject to probabilistic saturation constraints (PSCs) and non-Gaussian noises under the redundant communication channels. A series of mutually independent Bernoulli distributed white sequences are introduced to govern the random occurrence of the time delays, the saturation constraints, and the transmission channel failures. A comprehensive redundant-channel-based communication mechanism is constructed to attenuate the phenomenon of packet dropouts so as to enhance the quality of data transmission. To compensate for the influence of randomly occurring time delays, the corresponding occurrence probability is exploited in the process of particle generation. In addition, an explicit expression of the likelihood function is established based on the statistical information to account for the impact of PSCs and redundant channels. By virtue of the modified operations of particle propagation and weight update, a particle-filter-based state estimation algorithm is proposed with mild restriction on the system type. Finally, an illustrative example with Monte Carlo simulations is provided to demonstrate the effectiveness of the developed state estimation scheme.

This article is concerned with the maximum correntropy filtering (MCF) problem for a class of nonlinear complex networks subject to non-Gaussian noises and uncertain dynamical bias. With aim to utilize the constrained network bandwidth and energy resources in an efficient way, a componentwise dynamic event-triggered transmission (DETT) protocol is adopted to ensure that each sensor component independently determines the time instant for transmitting data according to the individual triggering condition. The principal purpose of the addressed problem is to put forward a dynamic event-triggered recursive filtering scheme under the maximum correntropy criterion, such that the effects of the non-Gaussian noises can be attenuated. In doing so, a novel correntropy-based performance index (CBPI) is first proposed to reflect the impacts from the componentwise DETT mechanism, the system nonlinearity, and the uncertain dynamical bias. The CBPI is parameterized by deriving upper bounds on the one-step prediction error covariance and the equivalent noise covariance. Subsequently, the filter gain matrix is designed by means of maximizing the proposed CBPI. Finally, an illustrative example is provided to substantiate the feasibility and effectiveness of the developed MCF scheme.

This article is concerned with supplementary control of discrete-time nonlinear systems with multiple controllers in the framework of goal representation heuristic dynamic programming (GrHDP), where a logarithmic quantizer is used to govern the network communication. For the addressed problem, a neural network (NN)-based observer is first proposed to estimate the unknown system state in the simultaneous presence of quantized influence. In light of the estimated states and the ideal control inputs via a zero-sum game, a GrHDP algorithm with a reinforced term is developed to implement the supplementary control task, where some novel weight updating rules are constructed by virtue of an additional tunable parameter to improve the system performance. Furthermore, a set of conditions about the stability of estimated error dynamics of both observer states and updated NNs' weights are derived by resorting to the Lyapunov stability theory. Finally, the effectiveness of the developed method is verified by a power system and a numerical experiment.

This article is concerned with the event-triggered output feedback cluster consensus of leader-following multi-agent systems (MASs) under limited communication resources. Specifically, the distributed agents are divided into several clusters to accomplish different collective tasks under diverse intracluster and intercluster communications. First, to alleviate excessive communication resource consumption, two sampled-data-based event-triggered schemes are developed to distinguish agent-to-agent communications within clusters and between clusters. Based on these schemes, an event-based cluster consensus control protocol is proposed to solve the problem. Then, sufficient criteria on asymptotic stability of the resulting closed-loop system are derived and expressed in terms of matrix inequalities. It is noteworthy that the derived criteria for controller design are nonlinear and nonconvex with respect to the output feedback control gains and triggering parameters. To handle this issue, a modified genetic algorithm (MGA) with multiple subpopulations is proposed, where the subpopulations are independent of each other. The key feature of the designed MGA lies in that the fitness value is described as an accumulation of initial value and weighing value of each matrix inequality. Finally, an application of satellite formation flying is exemplified to demonstrate the effectiveness of the derived theoretical results.

In the operation planning of heating, ventilation, and air conditioning systems, optimal chiller loading assigns cooling loads to chillers with minimized power consumption. In this article, a mixed-integer optimization problem is formulated for distributed chiller loading and is then decomposed into two optimization subproblems with binary and continuous variables. A collaborative neurodynamic optimization approach is proposed for distributed chiller loading by solving the formulated subproblems. In the collaborative neurodynamic optimization framework, multiple projection neural networks and discrete Hopfield networks are used for scattered searches and a metaheuristic rule is adopted for reinitializing neuronal states upon their local convergence. Experimental results based on the specifications and parameters of three actual chiller systems are elaborated to substantiate the high performance of the approach.

This article investigates the privacy-preserving distributed economic dispatch (DED) problem of islanded microgrids. To improve the convergence rate of the DED algorithm, anacceleratedconsensus scheme is adopted by utilizing a short memory. Then, a privacy-preserving strategy is introduced to prevent sensitive information leakage by adding well-designed perturbations into the proposed consensus algorithm at the initial time instant. The primary objective of this article is to design a privacy-preserving accelerated consensus scheme to achieve a balance between supply and demand at the globally minimized cost while preserving the initial local demand information. By virtue of rigorous algebra manipulation and mathematical induction, a unified framework is established under which the convergence, the optimal convergence rate, and the optimality of the proposed DED algorithm are simultaneously analyzed, and the main results are extended to satisfy the privacy-preserving needs. Furthermore, the proposed privacy-preserving DED algorithm is shown to be resilient against both internal (honest-but-curious) and external eavesdroppers. Finally, the effectiveness of the developed privacy-preserving accelerated consensus algorithm is validated on the IEEE 39-bus power systems.

A new distributed filtering problem is fully studied in this article for time-varying systems over sensor networks under measurement errors and measurement censoring, where the measurement error is modeled as stochastic sensor bias driven by a dynamical equation and the measurement censoring is described by the Tobit measurement model. To reduce data congestion and transmission burden, a weighted try-once-discard protocol (WTODP) is applied to transmission channels to efficiently orchestrate data communication. The transmission priority is determined in a dynamical way depending on the importance of missions. The aim of this article is to construct an optimal distributed Tobit Kalman filter (TKF) such that filter parameters are rigorously determined in the minimum mean squared error sense under the consideration of the bias, censoring and WTODP effects. Specifically, sparsity of the network topology is comprehensively considered by using the novel matrix simplification technique. Furthermore, the resultant filtering error is ensured to be exponentially bounded in the mean squared sense. Finally, an illustrative example is used to show the applicability of the proposed filter.

This article is concerned with the problem of dynamic event-triggered (DET) scaled consensus control for multi-agent systems (MASs) in reliable and unreliable networks. First, by introducing an auxiliary dynamic variable (ADV) obeying its own dynamics, a novel DET mechanism is designed. A nonzero exponentially decaying term is introduced in the DET scheme to enlarge the interval between two consecutive trigger instants. In reliable networks, the DET mechanism works with a set of fixed parameters for the nonzero exponentially decaying term. Second, by introducing a stochastic variable into the parameters to describe the uncertain denial-of-service (DoS) attacks, a resilient DET mechanism is proposed. In unreliable networks, the resilient DET mechanism adjusts the set of parameters between two sets of values by detecting the launch of DoS attacks. Then, by choosing suitable Lyapunov–Krasovskii functions (LKFs), some stability criteria are derived for scaled consensus of MASs both in reliable and unreliable networks. Based on the stability criteria expressed in matrix inequalities, a control design algorithm based on genetic algorithm (GA) is proposed to calculate the control gains and event trigger parameters jointly. Finally, the effectiveness of the results is verified via a numerical example of multiple two-wheel mobile vehicles.

Distributed optimization achieves a minimized objective function through collaboration among distributed agents. Considering limited communication capabilities and privacy concerns, this article proposes a dynamic event-triggered differentially private gradient-tracking algorithm for distributed optimization. The communication requirement is reduced by event triggering, while the$\epsilon$-differential privacy is guaranteed by perturbations on states and the tracking of the average gradient. The convergence point is uniquely determined by the noise injected to the tracking. Sufficient conditions for stepsizes are established theoretically to guarantee the convergence in mean and almost surely. Moreover, the theoretical privacy level is rigorously obtained and the positive effect of the event-triggered communication on the privacy is also discussed. Simulations are conducted for the classification of the dataset on the stability of a 4-node star power system to verify the theoretical findings.

This article addresses the privacy-preserving control issue for two-dimensional systems with probabilistic constraints. According to the exclusive or logical operation and the dynamic coding–decoding rule, a privacy-preserving mechanism (PPM) is developed, under which the transmitted data is efficiently compressed and encrypted into a ciphertext with finite bits. A PPM-based controller is designed that simultaneously guarantees a prescribed probabilistic constraint, mean-square boundedness, and privacy performance. Mathematical techniques, including mathematical induction, Chebyshev inequality, and matrix analysis, are employed to establish sufficient conditions for the presence of the desired controller gains. Additionally, the privacy and secrecy performance of the PPM is analyzed and simulation examples are presented to showcase the efficacy of the proposed controller design method.

This paper addresses the problem of simultaneous cyber attack estimation and sensor attack detection for connected automated vehicles (CAVs), where the vehicle-to-vehicle communication network suffers from false data injection attacks and vehicular radar sensors experience spoofing attacks. First, a delicate proportional-integral observer (PIO) is developed to estimate the unavailable longitudinal vehicle tracking errors and the injected attack signal in real-time. Second, leveraging a residual signal generated from the PIO, an effective detection mechanism, which involves residual evaluation and thresholding, is designed to identify the radar spoofing attacks. Furthermore, due to the concurrent effects of false data injection attacks and spoofing attacks on the estimation errors, formal stability and performance analysis in terms of robustness against false data injection and sensitivity against spoofing is then performed. Finally, simulation examples are provided to demonstrate the efficacy of the proposed attack estimation and attack detection method.

Nash equilibrium, as an essential strategic profile in game theory, is of both practical relevance and theoretical significance due to its wide penetration into various fields, such as smart grids, wireless communication networks, and networked mobile vehicles. In particular, distributed Nash equilibrium seeking strategies have recently attracted increasing attention because they show remarkable advantages in relaxing the requirement of a central node for information broadcasting or full observation of players’ actions. This article aims to provide a survey of distributed Nash equilibrium seeking in games with partial decision information, in which players can only exchange information with their neighbors and their objective functions may explicitly depend on all players’ actions. First, fundamental problem descriptions on distributed Nash equilibrium seeking are presented. Second, related results on distributed Nash equilibrium seeking in general multiplayer games, aggregative games, and multicluster games are reviewed, respectively, where representative continuous- and discrete-time methods are explained in detail. Third, two practical applications, including collaborative control for a network of mobile sensors and energy consumption control in smart grids, are provided to demonstrate the applicability of distributed Nash equilibrium seeking strategies. Finally, some promising directions are suggested for future research.

This article is concerned with the distributed state estimation problem over wireless sensor networks (WSNs), where each smart sensor is capable of harvesting energy from the external environment with a certain probability. The data transmission between neighboring nodes is dependent on the energy level of each sensor, and the internode communication is deemed as a failure when the current energy level is inadequate to guarantee the normal data transmission. Considering the intermittent information exchange over WSNs, a novel distributed state estimator is first constructed via introducing a set of indicator functions, and then the evolution of the probability distribution of energy level and its steady-state distribution is systematically discussed by resorting to the eigenvalue analysis approach and the mathematical induction. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance under known communication sequences. In addition, the convergence of the minimized upper bound of the expected estimation error covariance is analyzed under any initial condition. Finally, an illustrative example regarding the target tracking problem is provided to verify the validity of the obtained theoretical results.

This article is concerned with the influence maximization (IM) problem under a network with probabilistically unstable links (PULs) via graph embedding for multiagent systems (MASs). First, two diffusion models, the unstable-link independent cascade (UIC) model and the unstable-link linear threshold (ULT) model, are designed for the IM problem under the network with PULs. Second, the MAS model for the IM problem with PULs is established and a series of interaction rules among agents are built for the MAS model. Third, the similarity of the unstable structure of the nodes is defined and a novel graph embedding method, termed the unstable-similarity2vec (US2vec) approach, is proposed to tackle the IM problem under the network with PULs. According to the embedding results of the US2vec approach, the seed set is figured out by the developed algorithm. Finally, extensive experiments are conducted to: 1) verify the validity of the proposed model and the developed algorithms and 2) illustrate the optimal solution for IM under different scenarios with PULs.

Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families’ malware may remain undetected until the classifier gains specialized knowledge. The most existing ML-based methods will take a long time to learn new malware families in the latest malware family landscape. Existing ML-based Android malware detection and classification methods struggle with the fast evolution of the malware landscape, particularly in terms of the emergence of zero-day malware families and limited representation of single-view features. In this article, a new multiview feature intelligence (MFI) framework is developed to learn the representation of a targeted capability from known malware families for recognizing unknown and evolving malware with the same capability. The new framework performs reverse engineering to extract multiview heterogeneous features, including semantic string features, API call graph features, and smali opcode sequential features. It can learn the representation of a targeted capability from known malware families through a series of processes of feature analysis, selection, aggregation, and encoding, to detect unknown Android malware with shared target capability. We create a new dataset with ground-truth information regarding capability. Many experiments are conducted on the new dataset to evaluate the performance and effectiveness of the new method. The results demonstrate that the new method outperforms three state-of-the-art methods, including: 1) Drebin; 2) MaMaDroid; and 3)$N$-opcode, when detecting unknown Android malware with targeted capabilities.

In this article, the distributed set-membership fusion filtering problem is investigated for a class of nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. The sensors are communicated with their neighbors according to a given topology through wireless networks of limited bandwidth. With the purpose of relieving the communication burden as well as enhancing the transmission security, a logarithmic-type encoding-decoding mechanism is introduced for each sensor node so as to encode the transmitted data with a finite number of bits. A distributed set-membership filter is designed to determine the local ellipsoidal set that contains the system state by only utilizing the data from the local sensor node and its neighbors, where the proposed filter scheme is truly distributed with desirable scalability. Then, a new ellipsoid-based fusion rule is developed for the designed set-membership filters in order to form the fused ellipsoidal set that has a globally smaller volume than all local ellipsoidal sets. With the aid of the mathematical induction technique, the set theory, and the convex optimization approach, sufficient conditions are derived for the existence of the desired distributed set-membership filters and the fusion weights. Then, the filter parameters and the fusion weights are acquired by solving a set of constrained optimization problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed fusion filtering algorithm.

In this article, the tracking control problem is investigated for a type of linear networked systems subject to the round-Robin (RR) protocol scheduling and impulsive transmission outliers (ITOs). The communication between the controller and sensors is implemented through a shared network, on which the signal transmissions are scheduled by the RR protocol. The considered ITOs are modeled by a sequence of impulsive signals whose amplitudes (i.e., the norms of all impulsive signals) and interval lengths (i.e., the duration between all adjacent impulsive signals) are greater than two known thresholds, respectively. The occurrence moment for each ITO is first examined by using a certain outlier detection approach, and then a novel parameter-dependent tracking controller is proposed to protect the tracking performance from ITOs by removing the "harmful" signals (i.e., the transmitted signals contaminated by ITOs). Sufficient conditions are presented to ensure the exponentially ultimate boundedness of the resulted tracking error, and the controller gain matrices are subsequently designed by solving a constrained optimization problem. Finally, a simulation example is provided to demonstrate the effectiveness of our developed outlier-resistant tracking control scheme.

Chillers are indispensable machines for heat removal and the primary sources of power consumption in heating, ventilation, and air conditioning systems. In this paper, a cardinality-constrained global optimization problem is formulated to minimize power consumption for optimal chiller loading. The formulated problem is solved using a collaborative neurodynamic optimization method based on multiple neurodynamic models. Experimental results based on available actual chiller parameters are elaborated to demonstrate the superiority of the proposed approach to many baseline methods for optimal chiller loading.

This article addresses the problem of cooperative control of double-integrator type multirobot systems. Different from some conventional results with the control gain explicitly known, the control gain in this article is subject to uncertainty. Three different collective behaviors are explored, i.e., leaderless consensus, leader-following consensus, and formation control. For leaderless consensus, a sliding variable is constructed, based on which a novel continuous controller is designed such that the sliding surface is reached in finite-time and thus the state agreement of all agents is realized. For leader-following consensus, two different cases are investigated, i.e., the leader with constant velocity and with time-varying velocity. In both cases, sliding-mode based controllers are developed and corresponding stability conditions are established to ensure that the leader state is tracked by all followers. Finally, the theoretical results are applied to achieve formation control of nonholonomic mobile robots and corresponding experimental studies are conducted to demonstrate the effectiveness of the proposed controllers.

Fixed-time and prescribed-time consensus control can bring an explicit estimate of the settling time without dependence on initial conditions, which is important in providing control engineersa priorisystem information. This article aims at presenting a survey of recent trends and methodologies of fixed-time and prescribed-time consensus control in multiagent systems. First, some typical fixed-time consensus results are reviewed. Despite the advantage in deriving a fixed settling time bound, fixed-time consensus controllers usually result in a conservative estimate of the bound and a large magnitude of initial control input, which in turn show the necessity of designing prescribed-time consensus controllers. Second, characteristics and controller design of (practical, respectively) prescribed-time consensus are provided in detail. Particularly, representative time-varying function-based controllers are presented, by which (practical, respectively) consensus can be achieved in prescribed time. Third, applications of fixed-time and prescribed-time consensus control in mobile robots and smart grids are illustrated in case studies. Finally, several challenging issues in prescribed-time consensus control are discussed for future research.

This article addresses event-triggered optimal load dispatching based on collaborative neurodynamic optimization. Two cardinality-constrained global optimization problems are formulated and two event-triggering functions are defined for event-triggered load dispatching in thermal energy and electric power systems. An event-triggered dispatching method is developed in the collaborative neurodynamic optimization framework with multiple projection neural networks and a meta-heuristic updating rule. Experimental results are elaborated to demonstrate the efficacy and superiority of the approach against many existing methods for optimal load dispatching in air conditioning systems and electric power generation systems.

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

A chiller plant is an essential part of a heating, ventilation, and air conditioning system. Chiller plant operation planning is to determine the throughput of active chillers, pumps, and fans in a chiller plant to meet cooling load demands with minimized power consumption. Existing planning methods are limited to chiller plant operation with homogeneous devices subject to constraints for the conservation of energy or with heterogeneous devices without considering the conservation of energy. In this article, a mixed-integer optimization problem is formulated for chiller plant operation planning with heterogeneous devices to minimize power consumption subject to various constraints, including the constraints for the conservation of energy. The formulated problem is reformulated as a global optimization problem and solved via collaborative neurodynamic optimization with multiple projection neural networks. Experimental results based on equipment manufacturers’ specifications are elaborated to demonstrate the significantly higher performance of the proposed approach than four mainstream methods in terms of power consumption wattage.

In this article, a new outlier-resistant mechanism is proposed to deal with the variance-constrained filtering problem for a class of networked systems subject to sensor resolution under the round-robin protocol (RRP). Sensor resolution, which serves as an important index in determining measurement accuracy, is taken into account in the addressed filtering problem, and the sensor-resolution-induced uncertainty is tackled by using an upper-bounding technique. The RRP is employed to regulate the order of signal transmission in order to relieve communication overhead. In the case of measurement outliers, a tailored saturation function is dedicatedly introduced to the filter structure for the purpose of suppressing the outlier-corrupted innovations, thereby maintaining satisfactory filtering performance. By solving a matrix difference equation, an upper bound is first acquired on the error covariance of the devised filter, and the associated filter parameters are subsequently determined through minimizing the acquired bound. The validity of the developed variance-constrained filter design approach is thoroughly demonstrated via two simulation examples.

Autonomous marine vehicles (AMVs), including autonomous surface and underwater vehicles, are versatile means to explore, exploit, monitor, and protect marine resources and environments. Motion control is a fundamental enabling technique for state-of-the-art AMV development. Especially, guidance is a critical component in AMV motion control. In recent years, line-of-sight (LOS) guidance, as an efficient guidance method, has attracted tremendous interest from both theoretical and practical perspectives. In this paper, an overview of recent advances in LOS guidance for AMV path following is provided. First, a control objective for the path following of an AMV with a kinematic model is specified. Next, major LOS guidance laws for path following are reviewed in detail. Then, LOS guidance laws applicable to coordinated path following of multiple AMVs are elaborated. Finally, six challenging issues for future research are addressed.

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

This article is concerned with the dissipative deconvolution filtering issue for networked two-dimensional (2-D) digital systems. By orchestrating the sensor outputs with a prescribed dynamic transmission order, a new multinode Round-Robin protocol (RRP) under the 2-D setting is developed on the sensor-to-filter channel to ease the communication overheads, and a novel compensation strategy is proposed for enhancing the deconvolution filter performance. A sufficient condition is established for ensuring the dissipativity of the deconvolution filter in terms of coupled matrix inequalities. Furthermore, a particle swarm optimization (PSO) algorithm is formulated to design the filter gain with certain optimized performance. Finally, by transforming the color image into three gray images, the proposed algorithms are applied to the remote color image restoration problem under RRP. It is shown that the use of RRP saves 33.3% communication burden, and the saving of communication resources reduces the signal-to-noise ratio (SNR) by 18.5%. In comparison to the RRP using zero-input compensation strategy in available literature, the introduction of the novel multinode RRP with compensation strategy can indeed improve the reconstruction SNR.

In this article, as inspired by multiagent systems, a novel leader–follower-based particle swarm optimization (LFPSO) algorithm is presented where the particles are classified into leaders and followers according to their respective roles. The leaders are responsible for searching a wide range of the optimal candidate solutions so as to ensure the diversity of the particle population, and the followers are dedicated to seeking the global-best solution in order to guarantee the convergence of particles. A controller parameter is introduced to fine tune the impact of the leaders on the followers. Owing to the leader–follower mechanism, the proposed LFPSO algorithm not only maintains the diversity of the particle population but also improves the possibility of escaping from the locally optimal solution. It is demonstrated via experimental results that the proposed LFPSO algorithm significantly improves the accuracy and convergence rate of conventional particle swarm optimization algorithms. Furthermore, the LFPSO algorithm is successfully applied to denoise real-time signals in oilfield pipeline network and its superiority over existing denoising algorithms is verified as well.

This article is concerned with resilient control design for synchronization of multiple harmonic oscillators coupled via vulnerable network suffering from denial-of-service (DoS) attacks. The synchronization of multiple harmonic oscillators can be achieved under consideration of certain DoS attacks. A distinct resilient distributed control protocol is designed for individual harmonic oscillator by proposing a resilient logical packet processor (RLPP). Some sufficient conditions on the duration and frequency of attacks reflected by the bounds of lumped time-varying delay are derived by employing complete-type Lyapunov–Krasovskii functionals (LKFs). The gain matrix can be designed by solving a set of linear matrix inequalities by combining an optimization algorithm. Finally, the proposed synchronization control scheme is applied to a representative model of the single-phase photovoltaic grid interconnection process. Numerical simulations are provided to show the effectiveness of the developed method.

This article proposes a distributed cooperative manipulation control scheme for multirobot systems to track reference trajectories with unknown payload dynamics, grasp positions, and external disturbances. An online learning module is established to estimate the payload dynamics. Then a wrench-synthetic trajectory tracking control protocol is thereby developed to manipulate an object under unknown external disturbances no matter where the grasping points are. Moreover, sufficient conditions are derived to guarantee the uniform boundedness of the tracking errors of the closed-loop cooperative manipulation system. Finally, numerical simulations are conducted to substantiate the effectiveness of the proposed cooperative manipulation control scheme.

In this article, a proportional-integral-derivative (PID) tracking control problem is studied for a class of linear discrete-time systems under multiple description encoding mechanisms (MDEMs). The data transmissions on the sensor-to-controller channels are subject to packet dropouts whose occurrences are random and governed by two Bernoulli-distributed sequences of certain probability distributions. In order to improve the reliability of data transmission, an MDEM is put forward, with which the data is encoded into two descriptions of identical importance before being transmitted to the decoders through two individual communication channels. The aim of this article is to develop a PID tracking controller for guaranteeing the ultimate boundedness of the resulting tracking error, and the corresponding controller gains are obtained by solving an optimization problem. Moreover, the effect of the packet dropouts on the decoding accuracy is explicated via assessing the boundedness in respect to the decoding error. A simulation example is finally presented to showcase the applicability of the proposed PID tracking control scheme.

This paper is concerned with the secure event-triggered distributed cooperative longitudinal control problem of virtually coupled high-speed trains (VCHSTs) subject to DoS attacks. First, a DoS-aware dynamic event-triggered transmission mechanism (DETM) is proposed to reduce the frequency of train data transmissions over train-to-train (T2T) information flow channels. Via actively prolonging the inter-event times, the resilience of the developed DETM to DoS attacks can be greatly improved. Second, based on the intermittently arrived T2T data, a distributed event-based secure cooperative control protocol is proposed for each train in the convoy. Third, a sufficient condition is derived to guarantee the asymptotic stability of the train tracking error system under the prescribed H∞performance. Furthermore, a co-design method for solving out the desired train controllers and the triggering conditions is developed. Finally, the efficacy of the derived theoretical results is verified through a case study of a realistic railway line.

In this article, the problem of consensus control is investigated for a class of multiagent systems (MASs) with both stochastic noises and nonidentical exogenous disturbances. The signal transmission among agents is implemented through a digital communication network subject to both uniform quantization and round-robin protocol as a reflection of network constraints. The consensus strategy is designed by adopting the estimates of the relative states of the agent to its neighbors, which renders the distributed nature of the controller. A new consensus concept, namely, quasiconsensus in probability, is employed to evaluate the state response of the agents to the stochastic noises, the exogenous disturbances, and the quantization error. An augmented system is first formed that relies on the deviations of the individual state from the average state, the observer error of the relative state, as well as the relative measurement output. Based on the augmented model, an analysis approach on dynamical behaviors is developed to facilitate the consensus analysis of MASs by means of the switching Lyapunov function technique and the stochastic analysis methods. Then, the existence condition and the explicit expression of the time-varying gain matrices are proposed for the expected controller by resorting to the feasibility of several matrix inequalities. Numerical simulation results are presented to demonstrate the applicability of the theoretical results.

In this article, a frequency-domain approach is developed to deal with the global consensus problem for a class of general second-order multiagent systems (MASs) subject to actuator saturations. By employing the describing function and the generalized Nyquist criterion, the global consensus problem is thoroughly investigated for both undirected and directed topologies. First, the describing function is introduced to characterize the actuator saturations in the s -plane, and the inherent representation error is quantitatively analyzed from a frequency-domain perspective. Then, by means of the Kronecker product, the addressed consensus problem of the MAS is transformed into a corresponding stability analysis problem for a certain multi-input-multi-output (MIMO) system and, consequently, the generalized Nyquist criterion for MIMO systems is exploited to derive the condition for the global consensus of the MAS where the impact from the actuator saturation is explicitly reflected. Finally, numerical simulations are provided to illustrate the validity of the proposed theoretical result.

This article investigates the problem of finite-time consensus tracking for incommensurate fractional-order nonlinear multiagent systems (MASs) with general directed switching topology. For the leader with bounded but arbitrary dynamics, a neighborhood-based saturated observer is first designed to guarantee that the observer's state converges to the leader's state in finite time. By utilizing a fuzzy-logic system to approximate the heterogeneous and unmodeled nonlinear dynamics, an observer-based adaptive parameter control protocol is designed to solve the problem of finite-time consensus tracking of incommensurate fractional-order nonlinear MASs on directed switching topology with a restricted dwell time. Then, the derived result is further extended to the case of directed switching topology without a restricted dwell time by designing an observer-based adaptive gain control protocol. By artfully choosing a piecewise Lyapunov function, it is shown that the consensus tracking error converges to a small adjustable residual set in finite time for both the cases with and without a restricted dwell time. It should be noted that the proposed adaptive gain consensus tracking protocol is completely distributed in the sense that there is no need for any global information. The effectiveness of the proposed consensus tracking scheme is illustrated by numerical simulations.

This article deals with the problem of electromagnetic source localization (ESL). An evolutionary particle filter, which is first used to make a decision on the positions of electromagnetic sources, has two characteristics. One characteristic is that the number of particles can be significantly reduced while the other characteristic is that the particle diversity can be well improved. On the basis of the estimated positions of electromagnetic sources, the position and velocity of the virtual leader can be determined. Then, an event-based fixed-time consensus control approach is proposed such that the positions and velocities of robots reach consensus with the virtual leader over a fixed-time interval while saving resource consumption by reducing the communication frequencies and updating times of control inputs. Finally, simulation and experimental results show the effectiveness of the proposed decision and event-based fixed-time consensus control approach for ESL.

This article is concerned with event-triggered dynamic positioning for a mass-switched unmanned marine vehicle (UMV) in network environments. First, a switched dynamic positioning system (DPS) model for a mass-switched marine vehicle is established. The switched DPS model takes into consideration changes in the marine vehicle's mass and the resultant switching of the marine vehicle's parameters. Second, for a mass-switched UMV controlled through a communication network, a novel weighted event-triggering communication scheme considering switching features is proposed. The weighted error data of multiple sampling instants are utilized to avoid a long-time nontriggering phenomenon. The consideration of switching features guarantees the current sampled data to be transmitted if a switch occurs between the last sampling instant and the current sampling instant. Then, under the event-triggering scheme, an asynchronously switched DPS model for the mass-switched UMV is established in network environments. Based on this model, a mode-dependent DPS controller and event generator co-design method are proposed to attenuate the disturbance induced by wind, waves, and ocean currents. The DPS performance analysis demonstrates the effectiveness of the proposed method.

This article is concerned with the problem of recursive state estimation for a class of multirate multisensor systems with distributed time delays under the round-robin (R-R) protocol. The state updating period of the system and the sampling period of the sensors are allowed to be different so as to reflect the engineering practice. An iterative method is presented to transform the multirate system into a single-rate one, thereby facilitating the system analysis. The R-R protocol is introduced to determine the transmission sequence of sensors with the aim to alleviate undesirable data collisions. Under the R-R protocol scheduling, only one sensor can get access to transmit its measurement at each sampling time instant. The main purpose of this article is to develop a recursive state estimation scheme such that an upper bound on the estimation error covariance is guaranteed and then locally minimized through adequately designing the estimator parameter. Finally, simulation examples are provided to show the effectiveness of the proposed estimator design scheme.

This article focuses on the distributed maximum correntropy filtering issue for general stochastic nonlinear systems subject to deception attacks. The considered nonlinear functions consist of a determined one and a stochastic one, and the stochastic signals sent by deception attacks with identified statistic characteristics could be non-Gaussian. The corresponding calculation formulas of both the filter gains and the upper bound of the filter error covariance are proposed by means of the Taylor series expansion and the fixed-point iterative update rule, where the weighted maximum correntropy criterion is utilized to take the place of traditional minimum covariance indexes. Such an upper bound is only dependent on the local information, neighbor information, and the identified statistics of deception attacks and, therefore, the developed filtering scheme realizes the requirement of distributed calculation. Furthermore, a simplified version is obtained by removing weights in the correntropy criterion. Finally, an illustrative example is given to verify the effectiveness of developed distributed maximum correntropy filtering subject to deception attacks.

This article demonstrates that nonmaximum suppression (NMS), which is commonly used in object detection (OD) tasks to filter redundant detection results, is no longer secure. Considering that NMS has been an integral part of OD systems, thwarting the functionality of NMS can result in unexpected or even lethal consequences for such systems. In this article, an adversarial example attack that triggers malfunctioning of NMS in OD models is proposed. The attack, namely, Daedalus, compresses the dimensions of detection boxes to evade NMS. As a result, the final detection output contains extremely dense false positives. This can be fatal for many OD applications, such as autonomous vehicles and surveillance systems. The attack can be generalized to different OD models, such that the attack cripples various OD applications. Furthermore, a way of crafting robust adversarial examples is developed by using an ensemble of popular detection models as the substitutes. Considering the pervasive nature of model reuse in real-world OD scenarios, Daedalus examples crafted based on an ensemble of substitutes can launch attacks without knowing the parameters of the victim models. The experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to 99.9% and reduces the mean average precision scores to 0, while maintaining a low cost of distortion on the original inputs. It also demonstrates that the attack can be practically launched against real-world OD systems via printed posters.

This article deals with the problem of network-based path-tracking control of an underactuated unmanned surface vehicle subject to model uncertainties and unknown disturbances over a wireless network. A two-level network-based control architecture is proposed, including a local inner loop and a remote outer loop. In the remote outer loop, an event-triggered line-of-sight guidance law is designed to achieve path tracking while reducing the network burden for the remote control at the kinematic level. In the local inner loop, an extended state observer is employed to estimate the unknown disturbances due to the model uncertainties and environmental disturbances. Based on the estimated information from the extended state observer, an event-triggered anti-disturbance control law is developed to reduce the execution rate of actuators at the kinetic level. The stability of the closed-loop path-tracking system is proved based on the input-to-state stability and cascade stability theory. The effectiveness of the proposed network-based method for path tracking of the USV is verified via experiments.

This article addresses the problem of dynamic event-triggered platooning control of automated vehicles over a vehicular ad-hoc network (VANET) subject to random vehicle-to-vehicle communication topologies. First, a novel dynamic event-triggered mechanism is developed to determine whether or not the sampled data packets of each vehicle should be released into the VANET for intervehicle cooperation. More specifically, the threshold parameter in the triggering condition is dynamically adjusted over time according to the vehicular data variations, the dynamic threshold updating laws, and the bandwidth occupancy indication. Second, a unified platooning control framework is established to account for various spacing policies, randomly switching communication topologies, unknown leader control input, and external disturbances. Then, a new scheduling and platooning control co-design approach is presented such that the controlled vehicular platoon can successfully track the leader vehicle under random communication topologies and different spacing policies, including constant spacing, constant time headway spacing, and variable time headway spacing, meanwhile maintaining efficient bandwidth-aware resource management. Finally, comparative studies are provided to substantiate the effectiveness and merits of the proposed co-design approach.

This article deals with the problem of secure distributed adaptive platooning control of automated vehicles over vehicular ad-hoc networks (VANETs) in the presence of intermittent denial-of-service (DoS) attacks. The platoon, which is wirelessly connected via directed vehicle-to-vehicle (V2V) communication, is composed of a group of following vehicles subject to unknown heterogeneous nonlinearities and external disturbance inputs, and a leading vehicle subject to unknown nonlinearity and external disturbance as well as an unknown control input. Under such a platoon setting, this article aims to accomplish secure distributed platoon formation tracking with the desired longitudinal spacing and the same velocities and accelerations guided by the leader regardless of the simultaneous presence of nonlinearities, uncertainties, and DoS attacks. First, a new logical data packet processor is developed on each vehicle to identify the intermittent DoS attacks via verifying the time-stamps of the received data packets. Then, a scalable distributed neural-network-based adaptive control design approach is proposed to achieve secure platooning control. It is proved that under the established design procedure, the vehicle state estimation errors and platoon tracking errors can be regulated to reside in small neighborhoods around zero. Finally, comparative simulation studies are provided to substantiate the effectiveness and merits of the proposed control design approach on maintaining the desired platooning performance and attack tolerance.

Most of the existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This article addresses the problem of indoor localization by implementing distributed set-membership filtering based on a received signal strength indicator (RSSI) under unknown-but-bounded process and measurement noises. First, the transmit power and the path-loss exponent are estimated by a novel least-squares curve fitting (LSCF) method in RSSI-based localization. Since the localization process of trilateration is susceptible to inaccuracy caused by the noise-affected distance measurements, a convex optimization method is then developed to obtain the state ellipsoid estimation under the unknown-but-bounded noises. Third, a recursive algorithm is established to compute the global ellipsoid that guarantees to locate the true target at every time step. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed set-membership filtering method for indoor localization.

The main purpose of this article is to investigate the consensus of linear multiagent networks with time-varying characteristics under sampled-data communications, where the time-varying characteristics include both time-varying topologies and the node's linear time-varying dynamics. By using the decoupling method, we prove that the sampled-data consensus problem of multiagent networks is equal to the stability problem of sampled-data systems. Then, the globally asymptotical consensus is investigated for multiagent networks with time-varying characteristics by virtue of the Lyapunov function method. It should be noted that when the Lyapunov function method is utilized to investigate the stability problem of control systems, it is always assumed that the derivative of the constructed Lyapunov function is not more than zero. This assumption is removed here and as a replacement, the average value of the derivative of the Lyapunov function in a period to be negative is needed.

This article is concerned with set-membership global estimation for a networked system under unknown-but-bounded process and measurement noises. First, a group of local set-membership estimators is deployed to obtain the local ellipsoidal estimate of the true system state. Each estimator is capable of communicating with its neighbors within its communication range. Second, a global estimation approach is proposed which generates a trace-maximal ellipsoid within the intersection of all the local estimation sets with an aim to improve the difference of the local estimate at each time instant. Sufficient conditions for providing a global estimate under both complete and incomplete measurement transmissions are derived. Third, as an application, a modified distributed photovoltaic grid-connected generation system is provided to verify the effectiveness of the developed set-membership global estimation approach. Furthermore, an islanding fault detection scheme is derived based on the calculated global ellipsoidal estimate. Finally, simulation verification of the obtained theoretical results on the distributed generation system is presented.

This article is concerned with the energy-to-peak state estimation problem for a class of linear discrete-time systems with energy-bounded noises and intermittent measurement outliers (IMOs). In order to capture the intermittent nature, two sequences of step functions are introduced to model the occurrence of the IMOs. Furthermore, two special indices (i.e., minimum and maximum interval lengths) are adopted to describe the "occurrence frequency" of IMOs. Different from the considered energy-bounded noises, the outliers are assumed to have their magnitudes larger than certain thresholds. In order to achieve a satisfactory performance constraint on the energy-to-peak state estimation under the addressed kind of measurement outliers, a novel parameter-dependent (PD) state estimation strategy is developed to guarantee that the measurements contaminated by outliers would be removed in the estimation process. The proposed PD state estimation method is essentially a two-step process, where the first step is to examine the appearing and disappearing moments for each IMO by using a dedicatedly constructed outlier detection scheme, and the second step is to implement the state estimation task according to the outlier detection results. Sufficient conditions are obtained to ensure the existence of the desired estimator, and the gain matrix of the desired estimator is then derived by solving a constrained optimization problem. Finally, a simulation example is presented to illustrate the effectiveness of our developed PD state estimation strategy.

This article is concerned with both consensus and coordinated path-following control for multiple nonholonomic wheeled mobile robots. In the design, the path-following control is decoupled into the longitudinal control (speed control) and the lateral control (heading control) for the convenience of implementation. Different from coordinated trajectory tracking control schemes, the proposed control scheme removes the temporal constraint, which greatly improves the coordination robustness. In particular, two new coordinated error variables describing a chasing-and-waiting strategy are introduced in the proposed coordinated path-following control for injective paths and circular paths, respectively. All the closed-loop signals have proved to be asymptotically stable in the Lyapunov sense. Finally, simulation results under three typical paths are presented to verify the proposed coordination controllers.

This article addresses the cooperative target enclosing problem of ring-networked underactuated autonomous surface vehicles (ASVs). The target velocity is unavailable, and the ASVs are subject to sideslip effects, unknown control gains, and uncertain kinetics. The control objective is to drive a fleet of ASVs to surround a moving target at a desired range and maintain a spaced formation. An integrated distributed guidance and model-free control method is presented based on extended state observers (ESOs) and a data-driven fuzzy predictor. Specifically, by using two ESOs to estimate the unknown relative kinematics induced by the unknown target velocity and unknown sideslip and a distributed target estimator to recover the target position, intermediate range keeping and phase keeping guidance laws are designed to achieve a circular motion and an evenly spaced formation, respectively. Next, a model-free fuzzy control law is developed based on a data-driven fuzzy predictor, which learns the unknown control gains and uncertain kinetics simultaneously. Finally, the closed-loop control system is proven to be input-to-state stable through Lyapunov analysis. The salient feature of the proposed method is that cooperative circumnavigating a maneuvering target with unknown velocity can be achieved without the global target information and knowledge of vehicle kinetics. Simulation results validate the effectiveness of the proposed distributed guidance and control method for cooperative target enclosing of ASVs.

Multiagent systems (MASs) provide an effective means for coordinating spatially distributed and networked agents (or nodes, subsystems) such that the desired cooperative tasks can be accomplished with promising reliability, manipulability, scalability, and efficiency. One key issue in the study of MASs is the design of distributed cooperative control protocol and algorithm that depend on only local and real-time information exchanges among interacting agents over networks. However, network-enabled information sharing and increasing connectivity in practical MASs present several attack factors for malicious adversaries, thereby rendering secure control of MASs fundamentally significant. This article provides a brief survey of systems and control technologies that have been available for addressing different secure control problems of MASs in the face of various malicious attacks. First, attacks on MASs are classified based on different configuration layers. Then, the existing attack models and strategies on communication layer and agent layer are systematically examined, respectively. Furthermore, some typical secure control techniques for MASs that have been employed to handle these attacks are surveyed. Finally, several challenging issues are envisioned for potential future research.

This article is concerned with bipartite tracking for a class of nonlinear multiagent systems under a signed directed graph, where the followers are with unknown virtual control gains. In the predictor-based neural dynamic surface control (NDSC) framework, a bipartite tracking control strategy is proposed by the introduction of predictors and the minimal number of learning parameters (MNLPs) technology along with the graph theory. Different from the traditional NDSC, the predictor-based NDSC utilizes prediction errors to update the neural network for improving system transient performance. The MNLPs technology is employed to avoid the problem of "explosion of learning parameters". It is proved that all closed-loop signals steered by the proposed control strategy are bounded, and the system achieves bipartite consensus. Simulation results verify the efficiency and effectiveness of the strategy.

This article is concerned with the consensus control problem for a general class of linear multiagent systems (MASs) subject to actuator imperfection consisting of both actuator saturations and actuator faults. A novel two-step saturation-resistant approach is proposed to attenuate the side effects resulting from the actuator imperfection. In the first step of controller design, the state information received from the neighboring agents is used to design a consensus controller capable of tolerating the actuator fault. Then, in the second step of controller optimization, the domain of attraction (DOA) is introduced for MASs to evaluate the performance of the controller and, subsequently, optimize the controller parameters to enlarge the DOA in terms of solutions to a certain set of matrix inequalities. Finally, simulation examples are provided to demonstrate the effectiveness of the developed saturation-resistant approach.

This article addresses the problem of synchronization of impulsive networks with switching topologies. A new synchronization framework is established with an emphasis on settling time estimation. The impulsive networks consist of physical nodes and cyber modules. For physical nodes, states are changed impulsively at discrete time instants due to some switching phenomena or unexpected sudden noises. For cyber modules, two cases of switching scenarios are considered for information exchange patterns during specific time intervals. In the first case, cyber modules lose all the communication links with others, resulting in disconnected topologies. Then, a distributed controller is proposed for nodes without intrinsic nonlinear dynamics. A distinguished feature of this controller is its capability to estimate a bound for settling time, beyond which the synchronization with respect to a virtual target is guaranteed. In the second case, cyber modules lose some communication links but build other new ones with the help of a smart communication center to form connected topologies. A distributed controller is further designed for nodes in the presence of intrinsic nonlinear dynamics. Accordingly, a sufficient condition is derived to achieve synchronization with an estimated settling time bound. For both cases, the estimated bounds are able to reveal the relationship between the impulsive strength and the synchronization performance. Finally, numerical examples including a case study on a modified IEEE 34 bus test feeder are provided to demonstrate the effectiveness of the proposed controllers.

This article deals with the proportional–integral observer (PIO) design problem for a class of linear systems with distributed time delays and randomly occurring parameter uncertainties. The measurement signals, transmitted from the sensors to the observer, might suffer from the randomly occurring deception attacks. The random occurrences of parameter uncertainties and deception attacks are governed by two series of Bernoulli random variables with known probability distributions. An outlier-resistant PIO is developed by introducing an innovation saturation mechanism for the sake of alleviating the adverse effects induced by the deception attacks on the estimation performance. The purpose of the addressed problem is to design a PIO that is capable of guaranteeing the mean-square boundedness of the estimation errors while achieving the desired security level. The desired PIO gain is designed by solving a matrix inequality and the validity of the results obtained is shown by a numerical simulation example.

This article is concerned with quasi-synchronization of grid-connected systems in electrical networks, where heterogeneous Inductance-Capacitance (LC) oscillators are coupled via electrical inductance subject to time-varying delays. Note that complete synchronization fails to be accomplished due to the existence of nonidentical parameters and quasi-synchronization cannot be achieved via non-delayed inductive coupling. A configuration of multiple heterogeneous LC oscillators with inductive coupling subject to time-varying lumped delay is first constructed by introducing an active delay intentionally. Then the complete-type Lyapunov-Krasovskii functionals (LKF) are constructed to investigate the exponential convergence of quasi-synchronization of LC oscillators in the presence of parameter mismatches utilizing the positive effects of interval time-varying delays. Some feasible synchronization criteria are derived. The gain matrix can be designed by solving a set of linear matrix inequalities combining an optimization algorithm. Finally, a numerical example of five LC oscillators in photovoltaic grid-connected system is given to demonstrate the effectiveness of the proposed method.

This article is concerned with the problem of distributed prescribed finite-time observer design for a strictfeedback nonlinear system with external disturbance. The purpose is to reconstruct the unavailable system state based on a group of distributed observers, where each of them can only receive at most 1-D output measurement from the system. First, in the absence of disturbance, a new distributed prescribed finite-time observer featuring time-varying gains is constructed and designed under the assumption of joint observability. It is analytically proved that for any prescribed instant independent of system initial conditions and other design parameters, the obtained distributed observer can guarantee not only the asymptotic convergence to zero of the state error between each observer state and the system state at this prescribed instant but also the definite zero-state error after this prescribed instant. Second, a distributed prescribed finite-time bounded observer is delicately proposed to account for the presence of external disturbance in the system dynamics. It is shown that the state error can be bounded by an arbitrarily positive constant after a prescribed instant. Finally, a numerical example and an electromechanical system are presented to demonstrate the effectiveness of the proposed results.

This article is concerned with the$H_{\infty }$cluster formation control of a multiagent system (MAS) with stochastic sampling in network environments. First, based on a directed communication topology with acyclic partition, agents are separated into several clusters. The agents moving in the same cluster are expected to achieve a desired formation collaboratively, while the agents in different clusters have different formation patterns. Second, the external disturbance for each agent is taken into account. The associated$H_{\infty }$cluster formation control, whose performance bound can be obtained by considering both formation information and cluster characteristics, is introduced to measure the disturbance attenuation ability. Third, by casting a stochastic sampled-data-based$H_{\infty }$cluster formation problem into an$H_{\infty }$control problem of a stochastic system, an$H_{\infty }$cluster formation criterion is derived for the MAS with external disturbance. Finally, a lower-dimensional cluster formation criterion is obtained for the disturbance-free MAS. Cluster formation performance analysis testifies the effectiveness of the proposed design methods.

This article is concerned with the resilient tracking control of a networked control system under cyber attacks. The attacker is an active adversary whose aim is to severely degrade the tracking performance of the system by launching deception attacks on the sensor-to-controller communication channels and denial-of-service attacks on the controller-to-plant channels, respectively. First, a concept of resilient set-membership tracking control is presented, through which the system's true state is guaranteed to reside in a bounding ellipsoidal set of the reference state regardless of the existence of attacks and unknown-but-bounded (UBB) noises. Second, in the case that full information of the system's state is not implicitly trusted in the presence of attacks, a resilient set-membership estimation strategy is provided to secure the state estimates against the deception attacks. Furthermore, based on a recursive computation of a reference state ellipsoid and confidence state estimation ellipsoids, a convex optimization algorithm in terms of recursive linear matrix inequalities is proposed to obtain the gain parameters for both the desired resilient state estimator and the tracking controller. Finally, the effectiveness of the proposed method is illustrated through an Internet-based three-tank system.

This article is concerned with the distributed secondary frequency and voltage control for islanded microgrids. First, the distributed secondary control problem is formulated by taking both communication delays and switching topologies into account. Second, by using an Artstein model reduction method, a novel delay-compensated distributed control scheme is proposed to restore frequencies of each distributed generator (DG) to a reference level in finite time, while achieving active power sharing in prescribed finite-time regardless of initial deviations generated from primary control. Third, a distributed finite-time controller is developed to regulate voltages of all DGs to a reference level. Fourth, the proposed idea is also applied to deal with the finite-time consensus for first-order multiagent systems. Finally, case studies are carried out, demonstrating the effectiveness, the robustness against load changes, and the plug-and-play capability of the proposed controllers.

This paper is concerned with the distributed H∞-consensus filtering problem on attitude tracking over a radar filter network subject to switching topology and random packet dropouts occurring in the data transmission from both the Sun sensor and the filters. Since ground-based radars cannot directly measure the satellite attitude, a Sun sensor is deployed at the satellite side and its measurements are transmitted to radar filters through different network communication channels while suffering from random packet dropouts with different probabilities. In the radar filter network, each radar filter receives data not only from the Sun sensor but also from its local neighboring radar filters in accordance with a switching network topology. A delicate distributed H∞-consensus filtering algorithm, which incorporates the effects of switching network topology and random packet dropouts, is adopted to estimate attitude and attitude-rate. The algorithm guarantees H∞-consensus attenuation performance for the estimation deviations among radar filters, and the robustness against the switching network topology and packet dropouts for the radar filter network. The illustrative examples are given to verify the effectiveness of the proposed distributed H∞-consensus filtering algorithm.

This article is concerned with the distributed resilient estimation of a positive system over a sensor network. First, a heterogeneous sensor interaction framework, where each sensor is capable of sharing its local information of measurement as well as state estimate with its underlying neighbors via distinct interaction topologies, is proposed to account for different sensor communication capacities. During the information exchanges among the sensors, topological attacks are suitably modeled in such a way to incorporate the random and intermittent disruption of the heterogeneous sensor interaction topologies. Second, two sets of distributed resilient estimators are delicately constructed to cope with the resulting random denial of information exchanges within the specific repaired periods and compromised periods caused by the topological attacks. Third, the resilience performance analysis with a prescribedl1-gain attenuation level is carried out, and a linear programming approach is then developed to achieve the design of the desired distributed estimators. Finally, the effectiveness of the proposed design method is verified through a vehicle formation monitoring system.

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

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

The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from antifatigue safe driving to intelligent route planning. However, ADSs are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyberattacks and learning-based adversarial attacks. Inevitably, the safety and security of deep learning-based autonomous driving are severely challenged by these attacks, from which the countermeasures should be analyzed and studied comprehensively to mitigate all potential risks. This survey provides a thorough analysis of different attacks that may jeopardize ADSs, as well as the corresponding state-of-the-art defense mechanisms. The analysis is unrolled by taking an in-depth overview of each step in the ADS workflow, covering adversarial attacks for various deep learning models and attacks in both physical and cyber context. Furthermore, some promising research directions are suggested in order to improve deep learning-based autonomous driving safety, including model robustness training, model testing and verification, and anomaly detection based on cloud/edge servers.

Autonomous surface vehicles (ASVs) are marine vessels capable of performing various marine operations without a crew in a variety of cluttered and hostile water/ocean environments. For complex missions, there are increasing needs for deploying a fleet of ASVs instead of a single one to complete difficult tasks. Cooperative operations with a fleet of ASVs offer great advantages with enhanced capability and efficacy. Despite various application potentials, coordinated motion control of ASVs pose great challenges due to the multiplicity of ASVs, complexity of intravehicle interactions and fleet formation with collision avoidance requirements, and scarcity of communication bandwidths in sea environments. Coordinated control of multiple ASVs has received considerable attention in the last decade. This article provides an overview of recent advances in coordinated control of multiple ASVs. First, some challenging issues and scenarios in motion control of ASVs are presented. Next, coordinated control architecture and methods of multiple ASVs are briefly discussed. Then, recent results on trajectory-guided, path-guided, and target-guided coordinated control of multiple ASVs are reviewed in detail. Finally, several theoretical and technical issues are suggested to direct future investigations including network-based coordination, event-triggered coordination, collision-free coordination, optimization-based coordination, data-driven coordination of ASVs, and task-region-oriented coordination of multiple ASVs and autonomous underwater vehicles.

This article is concerned with the synchronization control problem for a class of discrete-time dynamical networks with mixed delays and switching topology. The saturation phenomenon of physical actuators is specifically considered in designing feedback controllers. By exploring the mixed-delay-dependent sector conditions in combination with the piecewise Lyapunov-like functional and the average-dwell-time switching, a sufficient condition is first established under which all trajectories of the error dynamics are bounded for admissible initial conditions and nonzero external disturbances, while the l2- l∞performance constraint is satisfied. Furthermore, the exponential stability of the error dynamics is ensured for admissible initial conditions in the absence of disturbances. Second, by using some congruence transformations, the explicit condition guaranteeing the existence of desired controller gains is obtained in terms of the feasibility of a set of linear matrix inequalities. Then, three convex optimization problems are formulated regarding the disturbance tolerance, the l2- l∞performance, and the initial condition set, respectively. Finally, two simulation examples are given to show the effectiveness and merits of the proposed results.

This article addresses the problem of distributed path following of multiple under-actuated autonomous surface vehicles (ASVs) with completely unknown kinetic models. An integrated distributed guidance and learning control architecture is proposed for achieving a time-varying formation. Specifically, a robust distributed guidance law at the kinematic level is developed based on a consensus approach, a path-following mechanism, and an extended state observer. At the kinetic level, a model-free kinetic control law based on data-driven neural predictors via integral concurrent learning is designed such that the kinetic model can be learned by using recorded data. The advantage of the proposed method is two-folds. First, the proposed formation controllers are able to achieve various time-varying formations without using the velocities of neighboring vehicles. Second, the proposed control law is model-free without any parameter information on kinetic models. Simulation results substantiate the effectiveness of the proposed robust distributed guidance and model-free control laws for multiple under-actuated ASVs with fully unknown kinetic models.

This article presents a collaborative neurodynamic optimization (CNO) approach to multivehicle task assignments (TAs). The original combinatorial quadratic optimization problem for TA is reformulated as a quadratic unconstrained binary optimization (QUBO) problem with a quadratic utility function and a penalty function for handling load capacity and cooperation constraints. In the framework of CNO with a population of discrete Hopfield networks (DHNs), a TA algorithm is proposed for solving the formulated QUBO problem. Superior experimental results in four typical multivehicle operation scenarios are reported to substantiate the efficacy of the proposed neurodynamics-based TA approach.

Cyber-physical systems (CPSs) empower the integration of physical processes and cyber infrastructure with the aid of ubiquitous computation resources and communication capabilities. CPSs have permeated modern society and found extensive applications in a wide variety of areas, including energy, transportation, advanced manufacturing, and medical health. The security of CPSs against cyberattacks has been regarded as a long-standing concern. However, CPSs suffer from extendable vulnerabilities that are beyond classical networked systems due to the tight integration of cyber and physical components. Sophisticated and malicious cyberattacks continue to emerge to adversely impact CPS operation, resulting in performance degradation, service interruption, and system failure. Secure state estimation and control technologies play a vital role in warranting reliable monitoring and operation of safety-critical CPSs. This article provides a review of the state-of-the-art results for secure state estimation and control of CPSs. Specifically, the latest development of secure state estimation is summarized in light of different performance indicators and defense strategies. Then, the recent results on secure control are discussed and classified into three categories: 1) centralized secure control; 2) distributed secure control; and 3) resource-aware secure control. Furthermore, two specific application examples of water supply distribution systems and wide-area power systems are presented to demonstrate the applicability of secure state estimation and control approaches. Finally, several challenging issues are discussed to direct future research.

This article is concerned with the distributed recursive filtering of cyber-physical systems consisting of a set of spatially distributed subsystems. Due to the vulnerability of communication networks, the transmitted data among subsystems could be subject to deception attacks. In this article, attackers do not have enough knowledge of the full network topology and the system parameters and therefore cannot carry out stealth attacks. For this scenario, a defense strategy dependent on the received innovation is proposed to identify the occurring attacks as far as possible. In light of identified attacks, a novel distributed filter is constructed and its gain is designed via a set of recursive formulas on the upper bound of covariance of filtering errors. The utilization of upper bound is to avoid the calculational challenge of cross-covariance matrices and realize the requirement of distributed implementation, simultaneously. Furthermore, the developed scheme only depends on the neighboring information and the information from the subsystem itself, and thereby satisfying the requirement of the scalability. Finally, a standard IEEE 39-bus power system is utilized to verify the effectiveness of the proposed filtering scheme.

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

The full information estimation (FIE) problem is addressed for discrete time-varying systems (TVSs) subject to the effects of a round-Robin (RR) protocol. A shared communication network is adopted for data transmissions between sensor nodes and the state estimator. In order to avoid data collisions in signal transmission, only one sensor node could have access to the network and communicate with the state estimator per time instant. The so-called RR protocol, which is also known as the token ring protocol, is employed to orchestrate the access sequence of sensor nodes, under which the chosen sensor node communicating with the state estimator could be modeled by a periodic function. A novel recursive FIE scheme is developed by defining a modified cost function and using a so-called “backward-propagation-constraints.” The modified cost function represents a special global estimation performance. The solution of the proposed FIE scheme is achieved by solving a minimization problem. Then, the recursive manner of such a solution is studied for the purpose of online applications. For the purpose of ensuring the estimation performance, sufficient conditions are obtained to derive the upper bound of the norm of the state estimation error (SEE). Finally, two illustrative examples are proposed to demonstrate the effectiveness of the developed estimation algorithm.

This paper is concerned with the moving horizon (MH) estimation issue for a type of networked nonlinear systems (NNSs) with the so-called random access (RA) protocol scheduling effects. To handle the signal transmissions between sensor nodes and the MH estimator, a constrained communication channel is employed whose channel constraints implies that at each time instant, only one sensor node is permitted to access the communication channel and then send its measurement data. The RA protocol, whose scheduling behavior is characterized by a discrete-time Markov chain (DTMC), is utilized to orchestrate the access sequence of sensor nodes. By extending the robust MH estimation method, a novel nonlinear MH estimation scheme and the corresponding approximate MH estimation scheme are developed to cope with the state estimation task. Subsequently, some sufficient conditions are established to guarantee that the estimation error is exponentially ultimately bounded in mean square. Based on that the main results are further specialized to linear systems with the RA protocol scheduling. Finally, two numerical examples and the corresponding figures are provided to verify the effectiveness/correctness of the developed MH estimation scheme and approximate MH estimation scheme.

This paper is concerned with the event-triggered control problem of vehicle active suspension systems subject to uncertain actuator faults. Firstly, networked modeling of the vehicle suspension control system is presented and a dynamic event-triggered communication mechanism is developed to reduce some unnecessary data transmissions between networked sensor and controller, thus enabling efficient usage of the limited communication resources for the in-vehicle network. Different from most existing event-triggered mechanisms, the threshold parameter of the proposed triggering condition is adaptively regulated in accord with a dynamic rule. Secondly, by Lyapunov functional method, a co-design criterion of the desired event-triggering parameter and H∞controller gain is derived so as to guarantee the asymptotic stability of the resulting closed-loop system and preserve several performance requirements of the suspension system, including ride comfort, road holding and suspension deflection/stroke limitation. Finally, simulation results are provided to show the effectiveness of the proposed method.

In this paper, the cooperative dynamic positioning (CDP) of multiple unmanned surface vehicles (multi-USVs) is studied in network environments. Firstly, networked cooperative dynamic positioning system (CDPS) models are constructed for multi-USVs subject to disturbance induced by wind, waves, and current. Secondly, considering the negative influences of network-induced delays, a CDP control protocol is designed. Then, by employing Lyapunov-Krasovskii approach, a controller design criterion ensuring CDP is derived such that the errors of earth-fixed position and heading between unmanned surface vehicles (USVs) and a reference state vector converge to a bounded region. Finally, the performance analysis verifies the effectiveness of the design approach presented in this paper.

The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code available, which allows machine learning and data mining techniques to exploit abundant patterns within software code. Particularly, the recent breakthrough application of deep learning to speech recognition and machine translation has demonstrated the great potential of neural models’ capability of understanding natural languages. This has motivated researchers in the software engineering and cybersecurity communities to apply deep learning for learning and understanding vulnerable code patterns and semantics indicative of the characteristics of vulnerable code. In this survey, we review the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery. We also identify the challenges in this new field and share our views of potential research directions.

This paper investigates the finite-horizon H∞containment control issue for a general discrete time-varying linear multiagent systems with multileaders. All followers in such a system are driven into a convex hull spanned by multiple leaders, which can be transformed into a problem of tracking a virtual trajectory generated by these leaders. For this purpose, a local state observer is put forward to estimate the state of each agent itself. Then, the estimated state is transmitted to corresponding neighbors governing by an innovation-based event-triggered scheduling protocol. The purpose of the addressed problem is to design both an event-based distributed controller and a state observer such that a prescribed H∞containment index can be achieved over a given finite horizon. First, with the help of the completing the square method, a sufficient condition is established to ensure the desired H∞containment performance. Then, by resort to a novel nominal energy cost index combined with Moore-Penrose pseudoinverse method, the desired controller and observer parameters are obtained by solving two coupled backward recursive Riccati difference equations. Two positive scalars in proposed nominal energy cost index provide a tradeoff among the controlled tracking errors, the energy of transformed control inputs, and the precision of estimated states. Finally, a simulation example is given to illustrate the usefulness of the proposed theoretical results.

Social and Internet traffic analysis is fundamental in detecting and defending cyber attacks. Traditional approaches resorting to manually defined rules are gradually replaced by automated approaches empowered by machine learning. This revolution is accelerated by huge datasets which support machine-learning models with outstanding performance. In the context of a data-driven paradigm, this article reviews recent analytic research on cyber traffic over social networks and the Internet by using a set of common concepts of similarity, correlation, and collective indication, and by sharing security goals for classifying network host or applications and users or Tweets. The ability to do so is not determined in isolation, but rather drawn for a wide use of many different network or social flows. Furthermore, the flows exhibit many characteristics, such as fixed sized and multiple messages between source and destination. This article demonstrates a new research methodology of data-driven cyber security (DDCS) and its application in social and Internet traffic analysis. The framework of the DDCS methodology consists of three components, that is, cyber security data processing, cyber security feature engineering, and cyber security modeling. Challenges and future directions in this field are also discussed.

In this paper, the neural-network (NN)-based consensus control problem is investigated for a class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input constraints. Relative measurements related to local tracking errors are collected via some smart sensors. A local nonquadratic cost function is first introduced to evaluate the control performance with input constraints. Then, in view of the relative measurements, an NN-based observer under the event-triggered mechanism is designed to reconstruct the dynamics of the local tracking errors, where the adopted event-triggered condition has a time-dependent threshold and the weight of NNs is updated via a new adaptive tuning law catering to the employed event-triggered mechanism. Furthermore, an ideal control policy is developed for the addressed consensus control problem while minimizing the prescribed local nonquadratic cost function. Moreover, an actor-critic NN scheme with online learning is employed to realize the obtained control policy, where the critic NN is a three-layer structure with powerful approximation capability. Through extensive mathematical analysis, the consensus condition is established for the underlying MAS, and the boundedness of the estimated errors is proven for actor and critic NN weights. In addition, the effect from the adopted event-triggered mechanism on the local cost is thoroughly discussed, and the upper bound of the corresponding increment is derived in comparison with time-triggered cases. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

An event-triggered distributed state estimation problem is investigated for a class of discrete-time nonlinear stochastic systems with unknown parameters over sensor networks (SNs) subject to switched topologies. An event-triggered communication strategy is employed to govern the information broadcast and reduce the unnecessary resource consumption. Based on the adopted communication strategy, a distributed state estimator is designed to estimate the plant states and also identify the unknown parameters. In the framework of input-to-state stability, sufficient conditions with an average dwell time are established to ensure the boundedness of estimation errors in mean-square sense. In addition, the gains of the designed estimators are dependent on the solution of a set of matrix inequalities whose dimensions are unrelated to the scale of underlying SNs, thereby fulfill the scalability requirement. Finally, an illustrative simulation is utilized to verify the feasibility of the proposed design scheme.

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

This paper is concerned with event-triggered consensus of general linear multiagent systems (MASs) in leaderless and leader-following networks, respectively, in the framework of adaptive control. A distributed dynamic event-triggered strategy is first proposed, in which an auxiliary parameter is introduced for each agent to regulate its threshold dynamically. The time-varying threshold ensures less triggering instants, compared with the traditional static one. Then under the proposed event-triggered strategy, a distributed adaptive consensus protocol is formed including the updating law of the coupling strength for each agent. Some criteria are derived to guarantee leaderless or leader-following consensus for MASs with general linear dynamics, respectively. Moreover, it is proved that the triggering time sequences do not exhibit Zeno behavior. Finally, the effectiveness of the proposed dynamic event-triggered control mechanism combined with adaptive control is validated by two examples.

This paper is concerned with the observer-based event-triggered control for a continuous networked linear system subject to denial-of-service (DoS) attacks, where the attacks are launched periodically to block the data transmission in control channels. First, a new observer state-based resilient event-triggering scheme is developed in the presence of DoS attacks. Second, a novel event-based switched system model is established by considering the effect of the event-triggering scheme and DoS attacks simultaneously. By virtue of this new model combined with a piecewise Lyapunov-Krasovskii functional method, the sufficient conditions are derived to guarantee exponential stability of the resulting switched system. It is shown that the proposed results can establish a quantitative relationship among the launching/sleeping periods of the attacks, the event-triggering parameters, the sampling period, and the exponential decay rate. Third, criteria for designing a desired observer-based event-triggered controller are provided and expressed in terms of a set of linear matrix inequalities. Finally, an offshore structure model is presented to illustrate the efficiency of the developed control method.

This article deals with the problem of leader-following consensus for multiple wheeled mobile robots. Under a directed graph, a distributed observer is proposed for each follower to estimate the leader state in a fixed time. Based on the observer and a constructed nonlinear manifold, a novel protocol is designed such that the estimated leader state is tracked in a fixed time. Moreover, a switching protocol together with a linear manifold is proposed to ensure that fixed-time leader-following consensus is realized for any initial conditions without causing singularity issues. In contrast to alternative fixed-time consensus protocols in some existing results, the protocol proposed in this article is designed by constructing the nonlinear or linear manifold, which builds a new framework for fixed-time leader-following consensus. Furthermore, the obtained upper bound of settling time is explicitly linked with a single parameter in the protocol, which facilitates the adjustment of the bound under different performance requirements. Finally, the proposed protocol is applied to formation control of wheeled mobile robots.

This paper is concerned with secure 11-gain performance analysis and distributed finite-time filter design for a positive discrete-time linear system over a sensor network in the presence of deception attacks. A group of intercommunicating sensors is densely deployed to measure, gather, and process the output of the positive system. Each sensor is capable of sharing its measurement with its neighboring sensors in accordance with a prescribed network topology while suffering from random communication link failure. Meanwhile, the aggregated measurement on each sensor during network transmission is corrupted by stochastic deception attacks which compromise the sensor's measurement integrity. First, a unified sensor measurement transmission model is put forward to account for the simultaneous presence of deception attacks and various network-induced constraints. Second, delicate secure distributed filters are constructed by admitting the corrupted sensor measurement. Third, theoretical analysis on finite-time 11-gain boundedness of the filtering error system and design of desired positive filters are carried out. The solution to the filter gain parameters is characterized by a set of linear programming inequalities. Finally, the effectiveness of the obtained results is verified through the secure monitoring of power distribution in the smart grid.

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

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

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

Software vulnerability has long been an important but critical research issue in cybersecurity. Recently, the machine learning (ML)-based approach has attracted increasing interest in the research of software vulnerability detection. However, the detection performance of existing ML-based methods require further improvement. There are two challenges: one is code representation for ML and the other is class imbalance between vulnerable code and nonvulnerable code. To overcome these challenges, this article develops a DeepBalance system, which combines the new ideas of deep code representation learning and fuzzy-based class rebalancing. We design a deep neural network with bidirectional long short-term memory to learn invariant and discriminative code representations from labeled vulnerable and nonvulnerable code. Then, a new fuzzy oversampling method is employed to rebalance the training data by generating synthetic samples for the class of vulnerable code. To evaluate the performance of the new system, we carry out a series of experiments in a real-world ground-truth dataset that consists of the code from the projects of LibTIFF, LibPNG, and FFmpeg. The results show that the proposed new system can significantly improve the vulnerability detection performance. For example, the improvement is 15% in terms of F-measure.

This article addresses the problem of distributed resilient finite-time control of multiple heterogeneous battery energy storage systems (BESSs) in a microgrid subject to denial-of-service (DoS) attacks. Note that DoS attacks may block information transmission among BESSs by preventing the BESS from sending data, compromising the devices and jamming a communication network. A distributed secure control framework is presented, where an acknowledgment (ACK)-based attack detection strategy and a communication recovery mechanism are introduced to mitigate the impact of DoS attacks by repairing the paralyzed topology graphs caused by DoS attacks back into the initial connected graph. Under this framework, a distributed resilient finite-time secondary control scheme is proposed such that frequency regulation, active power sharing, and energy level balancing of BESSs can be achieved simultaneously in a finite time; meanwhile, operational constraints can be satisfied at any control transient time. Moreover, based on theoretical analysis, the impact of the duration time of DoS attacks on the convergence time of the control algorithm can be explicitly revealed. Finally, validity and effectiveness of the proposed control scheme are demonstrated by case studies on a modified IEEE 57-bus testing system.

This article proposes a continuous adaptive integral-type sliding-mode control approach for a class of mechatronic systems by taking into consideration matched and unmatched uncertainties, and uncertainty in the control gain. Four different sliding-mode controllers are designed to enable: 1) the avoidance of the singularity by preventing differentiating the system states with fractional power; and 2) the attenuation of the chattering by utilizing full-order sliding manifolds. The control gain adaptation in the full-order sliding-mode controller is presented to avoid the overestimation of the gain. With the continuous control in place, the mechatronic systems have a fast response with high precision. The soften action from carefully designed controllers with the gain adaptation guarantees the trajectories of the mechatronic systems to move smoothly and prevents damage to the mechanical components of the systems. Both the simulation and experimental results demonstrate the effectiveness and feasibility of the proposed control approach.

Fault-tolerant cooperative control of multiagent systems has attracted ever-increasing attention in recent years due to the fact that multiple agents can provide much more redundancy than a single agent system, thereby making the fault tolerant cooperative control design more flexible. However, multiagent systems may bring severe challenges that do not exist in single-agent systems. This article aims at presenting a survey of trends and methodologies of fault tolerant cooperative control in multiagent systems. Depending on the countermeasure against the faults, the existing fault-tolerant cooperative control methodologies are first classified into four categories: Individual methodologies, cooperative methodologies, topology reconfiguration-based methodologies, and composition reconfiguration-based methodologies. Then the characteristics and implementation schemes of four categories of methodologies are discussed in detail. Furthermore, the applicability of fault tolerant cooperative control in smart grids is outlined. Finally, several challenging issues are envisioned for future research.

This paper is concerned with the distributed attack detection and recovery in a vehicle platooning control system, wherein inter-vehicle information is propagated via a wireless communication network. An active adversary may launch malicious cyber attacks to compromise both sensor measurements and control command data due to the openness of the wireless communication. First, a distributed attack detection algorithm is developed to identify any of those attacks. The core of the algorithm lies in that each designed filter can provide two ellipsoidal sets: a state prediction set and a state estimation set. Whether a filter can detect the occurrence of such an attack is determined by the existence of intersection between these two sets. Second, two recovery mechanisms are put forward, through which the adversarial effects of cyber attacks can be mitigated in a timely manner. The recovery mechanisms depend on reliable modifications of the attacked signals required for the computation of the two ellipsoidal sets. Finally, simulation is provided to validate the effectiveness of the proposed method in both detection and recovery phases.

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

With the rapid development and widespread utilization of advanced communication, sensing, and computation technologies, increasing attention has been paid to develop new techniques of control and filtering for distributed networked systems. Note that the utilization of communication networks can improve efficiency, flexibility, and scalability in designing networked controllers and filters. However, communication networks usually suffer from communication resource constraints with detrimental consequences, such as long latency, increased packet dropout, reduced throughput, and so on. In order to address the challenges caused by limited communication resources, an event-triggered communication paradigm is an effective and promising technique that can significantly reduce computation/communication utilization while maintaining the desired estimation and the control performance. As a result, a variety of event-triggered control and filtering techniques for distributed networked systems have been proposed.

This paper is concerned with the distributed secondary control problem of multiple battery energy storage systems (BESSs) in an islanded microgrid, where the dynamics of each battery is heterogeneous. It is assumed that each battery can communicate with its neighbors via communication networks whose communication topologies are switching over time. A distributed finite-time secondary control scheme is proposed to ensure frequency regulation, active power sharing and energy level balancing of BESSs in a finite time, while operational constraints can be satisfied at any control transient time. Finally, validity and effectiveness of the proposed control scheme are demonstrated by case studies on a modified IEEE 57-bus testing system.

This paper is concerned with the reliable filtering for discrete-time nonlinear systems with abnormal measurements and probabilistic distributed time-delays. Two binary stochastic sequences are employed to model stochastic occurring nonlinearities and probabilistic distributed time-delays, respectively. The considered abnormal measurements could be outliers or injected data resulting from cyber-attackers. A factitious saturation constraint on innovation is adopted to remove these abnormal measurements in the designed filter. By resorting to the stochastic analysis combined with Lyapunov stability theory, a sufficient condition is proposed to check whether or not the augmented system is bounded in mean square. Furthermore, the desired filter gain depends on the solution of a linear matrix inequality. Finally, an illustrative example is adopted to verify the effectiveness of the developed design scheme.

This paper studies an attack detection problem for a networked leader-following multi-agent system subject to unknown-but-bounded system noises and quantization effects, where an adversary launches malicious cyber attacks on agents' measurement outputs aiming to distrust the leader-following consensus. An effective distributed attack detection algorithm is firstly developed for each follower such that the attack can be identified at the time of its occurrence. The core of the algorithm lies in a set-membership filtering approach from which each designed filter can provide an ellipsoidal state prediction set and an ellipsoidal state estimation set. Whether a filter can detect the occurrence of such an attack is then determined by the existence of intersection between these two sets. Furthermore, a convex optimization algorithm is established to solve out anticipated consensus protocol and two-step set-membership filter by resorting to some recursive linear matrix inequalities. Finally, an illustrative example is given to show the effectiveness of the proposed main results.

This paper is concerned with the distributed target tracking for a moving target of discrete time-varying nonlinear dynamics over a wireless sensor network. A number of spatially distributed sensors are deployed to measure the state of the target, calculate local state predictions as well as local state estimates, and further exchange local information with their underlying neighboring sensors. Due to adversarial attacks, a subset of sensors are deliberately manipulated and thus misbehaving. Accordingly, information exchanges from the misbehaving sensors to their neighbors become antagonistic rather than cooperative as in normal operation. First, a novel distributed target tracking scheme in terms of local state predictors and state estimators is developed for each sensor over a partially misbehaving sensor network. Second, criteria for designing the desired distributed target tracking scheme and the time-varying adjacency matrix are derived such that two ellipsoidal prediction and estimation sets can be recursively computed. It is analytically proved that these two sets guarantee the containment of the true target state at every instant of time regardless of misbehaving sensors as well as unknown-but-bounded process and measurement noises. Third, based on the proposed design criteria, optimization methods are put forward to minimize the calculated ellipsoids. Finally, an application to vehicle tracking is given to show the effectiveness of the results.

The neural-network (NN)-based output-feedback control is considered for a class of stochastic nonlinear systems under round-Robin (RR) scheduling protocols. For the purpose of effectively mitigating data congestions and saving energies, the RR protocols are implemented and the resulting nonlinear systems become the so-called protocol-induced periodic ones. Taking such a periodic characteristic into account, an NN-based observer is first proposed to reconstruct the system states where a novel adaptive tuning law on NN weights is adopted to cater to the requirement of performance analysis. In addition, with the established boundedness of the periodic systems in the mean-square sense, the desired observer gain is obtained by solving a set of matrix inequalities. Then, an actor-critic NN scheme with a time-varying step length in adaptive law is developed to handle the considered control problem with terminal constraints over finite-horizon. Some sufficient conditions are derived to guarantee the boundedness of estimation errors of critic and actor NN weights. In view of these conditions, some key parameters in adaptive tuning laws are easily determined via elementary algebraic operations. Furthermore, the stability in the mean-square sense is investigated for the discussed issue in infinite horizon. Finally, a simulation example is utilized to illustrate the applicability of the proposed control scheme.

This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor's local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system's true state always resides in each sensor's bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach.

This paper is concerned with distributed event-triggered H∞consensus filtering for a discrete-time linear system over a sensor network. Different from some existing event-triggered communication schemes (ETCSs), a new distributed ETCS is first developed to reduce the communication frequency of neighboring sensors, where the threshold parameter in an event triggering condition is time-varying with attainable upper and lower bounds. Then a threshold-parameter-dependent approach is proposed to derive criteria for designing the desired H∞consensus filters and the ETCS such that the resultant filtering error system is asymptotically stable with the prescribed H∞performance while maintaining satisfactory resource efficiency. Furthermore, a polytope-like transformation with regard to time-varying threshold parameters is performed and a recursive algorithm is presented to determine the threshold-parameter-dependent filter matrix sequences and event triggering weighting matrix sequence. Two illustrative examples are employed to show the effectiveness of the developed approach.

In this paper, the observer-based stabilization problem is investigated for a class of discrete-time nonlinear stochastic networked control systems (NCSs) with exogenous disturbances. The signal transmission from the sensors to the observer is implemented via a shared digital network, in which both uniform quantization effect and stochastic communication protocol (SCP) are taken into account to reflect several network-induced constraints. The notion of input-to-state stability in probability is introduced to describe the dynamical behaviors of the closed-loop stochastic NCS that is effectively characterized by a general nonlinear stochastic difference equation with Markovian jumping parameters. A theoretical framework is first established to felicitate the dynamics analysis of the closed-loop system in virtue of the switched Lyapunov function method and the stochastic analysis techniques. By making full use of the quantized measurement output under the scheduling of the SCP, the existence conditions for an observer-based controller are established under which the closed-loop system is input-to-state stable in probability. Then, the explicit expression of the gain matrices of the desired controller is given by resorting to a set of feasible solutions of certain matrix inequalities. The effectiveness of the theoretical results is demonstrated by a numerical simulation example.

This paper deals with the problem of environmental monitoring by designing and analyzing a cooperative receding horizon temporal logic (CRH-TL) control approach for mobile robot networks. First, a radial basis function network is used to model the distribution of environmental attributes in the monitored environment. On the basis of the established environment model, the problem of environmental monitoring can be formulated as a dynamical optimization problem. Second, an acceptable node set is obtained by enforcing appropriate constraints from linear temporal logic (LTL) specifications on the task of environmental monitoring. Third, by designing a cooperative energy function and using the acceptable node set, the CRH-TL control approach is proposed to generate the movement trajectory of each robot, which satisfies the given LTL specifications while guiding mobile robot networks to trace the peaks of environmental attributes. Finally, the effectiveness of the proposed CRH-TL control approach is illustrated for the problem of environmental monitoring.

This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.

This paper deals with the problem of distributed optimization of a multiagent system with network connectivity preservation. In order to realize cooperative interactions, a connected network is the prerequisite for high-quality information exchange among agents. However, sensing or communication capability is range-limited, so it is impractical to simply make an assumption that network connectivity is preserved by default. To address this concern, a class of generalized potentials including discontinuities caused by unexpected obstacles or noises are designed. For a class of quadratic cost functions, based on the potentials, a new distributed protocol is proposed to formally guarantee the network connectivity over time and to realize the state agreement in finite time while the sum of local functions known to individual agents is optimized. Since the right-hand side of the proposed protocol is discontinuous, some nonsmooth analysis tools are applied to analyze system performance. In some practical scenarios, where initial states are unavailable, a distributed protocol is further developed to realize the consensus in a prescribed finite time while solving the distributed optimization problem and maintaining network connectivity. Illustrative examples are provided to demonstrate the effectiveness of the proposed protocols.

This paper is concerned with H∞synchronization of coupled oscillators in a master-slave framework, in which the oscillators cannot be stabilized by nondelayed sampled position data, but can be stabilized by sampled position data with delays restricted by nonzero lower bounds and upper bounds. A configuration of networked master-slave oscillators with a remote controller is first constructed. Then the positive effects of delays on master-slave synchronization are investigated. Some delay-dependent H∞synchronization criteria are derived by constructing augmented discretized Lyapunov-Krasovskii functionals for determinate sampling and stochastic sampling, respectively. The controller can be designed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to verify the theoretical results. It is shown that the maximum allowable sampling period in the case of stochastic sampling is larger than the one in the case of determinate sampling. Stochastic sampling can also provide a tradeoff between network-induced delays and the sampling periods, enhancing the master-slave synchronization performance.

This paper is concerned with the distributed H∞state estimation for a discrete-time target linear system over a filtering network with time-varying and switching topology and partial information exchange. Both filtering network topology switching and partial information exchange between filters are simultaneously considered in the filter design. The topology under consideration evolves not only over time but also by an event switch which is assumed to be subject to a nonhomogeneous Markov chain. The probability transition matrix of the nonhomogeneous Markov chain is time-varying. In the filter information exchange, partial state estimation information and channel noise are simultaneously considered. In order to design such a switching filtering network with partial information exchange, stochastic Markov stability theory is developed. The switching topology-dependent filters are derived to guarantee an optimal H∞disturbance rejection attenuation level for the estimation disagreement of the filtering network. It is shown that the addressed H∞state estimation problem is turned into a switching topology-dependent optimal problem. The distributed filtering problem with complete information exchanges from its neighbors is also investigated. An illustrative example is given to show the applicability of the obtained results.

The synchronization problem of multiple/coupled reaction-diffusion neural networks with time-varying delays is investigated. Differing from the existing considerations, state delays among distinct neurons and coupling delays among different subnetworks are included in the proposed model, the assumptions posed on the arisen delays are very weak, time-varying, heterogeneous, even unbounded delays are permitted. To overcome the difficulties from this kind of delay as well as diffusion effects, a comparison-based approach is applied to this model and a series of algebraic criteria are successfully obtained to verify the global asymptotical synchronization. By specifying the existing delays, some M -matrix-based criteria are derived to justify the power-rate synchronization and exponential synchronization. In addition, new criterion on synchronization of general connected neural networks without diffusion effects is also given. Finally, two simulation examples are given to verify the effectiveness of the obtained theoretical results and provide a comparison with the existing criterion.

Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

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

This paper deals with the finite-horizon quantized H∞state estimation problem for a class of discrete timevarying genetic regulatory networks with quantization effects under stochastic communication protocols (SCPs). To better reflect the data-driven flavor of today's biological research, the network measurements (typically gigabytes in size by highthroughput sequencing technologies) are transmitted to a remote state estimator via two independent communication networks of limited bandwidths. To lighten the communication loads and avoid undesired data collisions, the measurement outputs are quantized and then transmitted under two SCPs introduced to schedule the large-scale data transmissions. The purpose of this paper is to design a time-varying state estimator such that the error dynamics of the state estimation satisfies a prescribed H∞performance requirement over a finite horizon in the presence of nonlinearities, quantization effects, and SCPs. By utilizing the completing-the-square technique, sufficient conditions are derived to ensure the H∞estimation performance and the parameters of the state estimator are designed by solving coupled backward recursive Riccati difference equations. A numerical example is given to illustrate the effectiveness of the design scheme of the proposed state estimator.

This paper is concerned with the input-to-state stabilization problem for a class of delayed differential systems. Both time-delay in state and bounded exogenous disturbances are taken into account in the model. An event-triggered strategy, which depends simultaneously on the latest sampled state and a non-negative threshold, is proposed to reduce the transmission frequency of the feedback control signals with guaranteed performance requirements. The notion of input-to-state practical stability is introduced to evaluate the dynamical performance of the controlled systems with considering the effects from both exogenous disturbances and event-triggered scheme. The estimations of the upper bounds for the system state and the measurement error are employed to analyze and further exclude the Zeno behavior for the proposed event-triggered scheme. The controller gain and the event-trigger parameters are co-designed in terms of the feasibility of certain matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of theoretical results.

This paper is concerned with the problem of event-triggered consensus for a nonlinear multiagent system. First, an integrated sampled-data-based event-triggered communication scheme is presented to decide when the current sampled-data should be broadcast. This scheme takes full advantage of both absolute and relative error-based event-triggered transmission schemes, and thus leading to a high efficiency of data transmissions. Second, a state-error-dependent delay system is delicately developed to model the multiagent system under the proposed sampled-data-based event-triggered consensus protocol. By virtue of the Lyapunov-Krasovskii functional method, a stability criterion and sufficient conditions on the existence of the consensus protocol and transmission scheme are derived to ensure that the solution of the state error dependent delay system is uniformly ultimately bounded. Finally, two illustrative examples are employed to show the validity and advantage of the proposed transmission scheme and consensus protocol.

Energy Internet is recognized as a new and advanced paradigm of smart grids, where energy collection devices, distributed energy storage devices, and various types of energy nodes are interconnected by applying advanced power electronics technology, information technology, and intelligent management technology. This emerging Energy Internet brings remarkable improvement for the society from various aspects with its advantages in high efficiency, strong flexibility, great scalability, and improved reliability. However, it in turn generates new challenges in architecture design, control operation, and energy management. As a result, how to deal with these challenges in Energy Internet still needs further investigation by applying advanced techniques, such as multiagent systems, artificial intelligence-based control, big data cloud computing and management, and so on.

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

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

This paper is concerned with cyber attack detection in a networked control system. A novel cyber attack detection method, which consists of two steps: 1) a prediction step and 2) a measurement update step, is developed. An estimation ellipsoid set is calculated through updating the prediction ellipsoid set with the current sensor measurement data. Based on the intersection between these two ellipsoid sets, two criteria are provided to detect cyber attacks injecting malicious signals into physical components (i.e., sensors and actuators) or into a communication network through which information among physical components is transmitted. There exists a cyber attack on sensors or a network exchanging data between sensors and controllers if there is no intersection between the prediction set and the estimation set updated at the current time instant. Actuators or network transmitting data between controllers and actuators are under a cyber attack if the prediction set has no intersection with the estimation set updated at the previous time instant. Recursive algorithms for the calculation of the two ellipsoid sets and for the attack detection on physical components and the communication network are proposed. Simulation results for two types of cyber attacks, namely a replay attack and a bias injection attack, are provided to demonstrate the effectiveness of the proposed method.

This paper is concerned with the collective behaviors of robots beyond the nearest neighbor rules, i.e., dispersion and flocking, when robots interact with others by applying an acute angle test (AAT)-based interaction rule. Different from a conventional nearest neighbor rule or its variations, the AAT-based interaction rule allows interactions with some far-neighbors and excludes unnecessary nearest neighbors. The resulting dispersion and flocking hold the advantages of scalability, connectivity, robustness, and effective area coverage. For the dispersion, a spring-like controller is proposed to achieve collision-free coordination. With switching topology, a new fixed-time consensus-based energy function is developed to guarantee the system stability. An upper bound of settling time for energy consensus is obtained, and a uniform time interval is accordingly set so that energy distribution is conducted in a fair manner. For the flocking, based on a class of generalized potential functions taking nonsmooth switching into account, a new controller is proposed to ensure that the same velocity for all robots is eventually reached. A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots. Simulation results are presented to show the effectiveness of the theoretical results.

This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

The synchronization control problem is investigated for a class of discrete-time dynamical networks with packet dropouts via a coding-decoding-based approach. The data is transmitted through digital communication channels and only the sequence of finite coded signals is sent to the controller. A series of mutually independent Bernoulli distributed random variables is utilized to model the packet dropout phenomenon occurring in the transmissions of coded signals. The purpose of the addressed synchronization control problem is to design a suitable coding-decoding procedure for each node, based on which an efficient decoder-based control protocol is developed to guarantee that the closed-loop network achieves the desired synchronization performance. By applying a modified uniform quantization approach and the Kronecker product technique, criteria for ensuring the detectability of the dynamical network are established by means of the size of the coding alphabet, the coding period and the probability information of packet dropouts. Subsequently, by resorting to the input-to-state stability theory, the desired controller parameter is obtained in terms of the solutions to a certain set of inequality constraints which can be solved effectively via available software packages. Finally, two simulation examples are provided to demonstrate the effectiveness of the obtained results.

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

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

One of the fundamental and challenging issues in microgrids is to guarantee fairness of load sharing while realizing voltage regulation of distributed generations. In order to address this issue, a new multiobjective optimization problem with tunable weighting coefficients is first formulated for dc microgrids. Second, a new distributed control scheme, which only requires local communications among neighbors, is proposed to solve the optimization problem. It is theoretically proved that the distributed control scheme can exponentially achieve the global optimal outputs of voltages and currents at distributed generations. Compared with a centralized control scheme, the proposed distributed control scheme provides remarkable advantages in improving reliability and scalability of microgrids. Third, the distributed control scheme is extended to accommodate a constant communication delay. The effects of the communication delay on the stability of microgrids are explicitly characterized. Finally, the performance of the proposed control schemes is evaluated by a modified six-bus microgrid with dc-powered trolleybus systems in terms of their convergence, robustness to load variations, plug-and-play functionality, tradeoff ability, and effects of communication delays.

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

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

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

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

This paper is concerned with the security control problem with quadratic cost criterion for a class of discrete-time stochastic nonlinear systems subject to deception attacks. A definition of security in probability is adopted to account for the transient dynamics of controlled systems. The purpose of the problem under consideration is to design a dynamic output feedback controller such that the prescribed security in probability is guaranteed while obtaining an upper bound of the quadratic cost criterion. First of all, some sufficient conditions with the form of matrix inequalities are established in the framework of the input-to-state stability in probability. Then, an easy-solution version on above inequalities is proposed by carrying out the well-known matrix inverse lemma to obtain both the controller gain and the upper bound. Furthermore, the main results are shown to be extendable to the case of discrete-time stochastic linear systems. Finally, two simulation examples are utilized to illustrate the usefulness of the proposed controller design scheme.

This paper is concerned with finite-time L2leader-follower consensus of networked Euler-Lagrange systems in the presence of external disturbances. A distributed finite-time L2control protocol is proposed by using backstepping design such that a group of follower agents modeled by Euler-Lagrange systems can follow a desired leader agent and achieve leader-follower consensus in finite time. Moreover, the finite-time L2gain is less than or equal to a prescribed value. A simulation example of a network composed of seven two-link manipulators is given to show the effectiveness of the theoretical results.

In this paper, we study the fixed-time cooperative tracking control problem for multi-robot systems with doubleintegrator dynamics. First, a novel distributed observer is proposed for each follower to estimate the leader state in a fixed time, then a local tracking controller based on sliding mode technique is proposed such that the estimated leader state is tracked in a fixed time. Both cases of a stationary leader and a dynamic leader are investigated. Since nonholonomic dynamics can better describe the mobile robots in reality, we further extend the results to achieve fixed-time cooperative tracking for multi-robot systems with nonholonomic dynamics. Different from the conventional finite-time cooperative tracking strategies, the fixed-time approach in this work guarantees that an upper bound of settling time can be prescribed without dependence on initial states of robots, which provides additional system information in advance. Finally, numerical simulations are given to demonstrate the effectiveness of the theoretical results.

This paper investigates event-triggered distributed control of networked large-scale systems under simultaneous consideration of network dynamics. Based on an information dispatching middleware, a framework of networked distributed large-scale systems is established. In terms of the middleware, the information selection module is developed to regulate the transmission of the sampled data by means of a novel event-triggered scheme. In the congestion avoidance module, a real-time scheduling strategy is proposed such that the released sampled output measurement data is transmitted within the allowable transmission network-induced delay. By employing Lyapunov-Krasovskii functional theory, sufficient conditions are derived such that the large-scale distributed system can achieve a prescribed H∞performance. Then the suitable distributed controllers and network congestion controllers can be co-designed provided that a set of linear matrix inequalities is feasible. Finally, an example is given to illustrate the merits and effectiveness of the proposed method.

This paper addresses the consensus problem for a continuous-time multiagent system (MAS) with Markovian network topologies and external disturbance. Different from some existing results, global jumping modes of the Markovian network topologies are not required to be completely available for consensus protocol design. A network topology mode regulator (NTMR) is first developed to decompose unavailable global modes into several overlapping groups, where overlapping groups refer to the scenario that there exist commonly shared local modes between any two distinct groups. The NTMR schedules which group modes each agent may access at every time step. Then a new group mode-dependent distributed consensus protocol on the basis of relative measurement outputs of neighboring agents is delicately constructed. In this sense, the proposed consensus protocol relies only on group and partial modes and eliminates the need for complete knowledge of global modes. Sufficient conditions on the existence of desired distributed consensus protocols are derived to ensure consensus of the MAS with a prescribed $H_{\infty }$ performance level. Two examples are provided to show the effectiveness of the proposed consensus protocol.

This paper is concerned with sampled-data leader-following consensus of a group of agents with nonlinear characteristic. A distributed consensus protocol with probabilistic sampling in two sampling periods is proposed. First, a general consensus criterion is derived for multiagent systems under a directed graph. A number of results in several special cases without transmittal delays or with the deterministic sampling are obtained. Second, a dimension-reduced condition is obtained for multiagent systems under an undirected graph. It is shown that the leader-following consensus problem with stochastic sampling can be transferred into a master-slave synchronization problem with only one master system and two slave systems. The problem solving is independent of the number of agents, which greatly facilitates its application to large-scale networked agents. Third, the network design issue is further addressed, demonstrating the positive and active roles of the network structure in reaching consensus. Finally, two examples are given to verify the theoretical results.

This paper deals with the problem of environmental monitoring by developing an event-triggered finite-time control scheme for mobile sensor networks. The proposed control scheme can be executed by each sensor node independently and consists of two parts: one part is a finite-time consensus algorithm while the other part is an event-triggered rule. The consensus algorithm is employed to enable the positions and velocities of sensor nodes to quickly track the position and velocity of a virtual leader in finite time. The event-triggered rule is used to reduce the updating frequency of controllers in order to save the computational resources of sensor nodes. Some stability conditions are derived for mobile sensor networks with the proposed control scheme under both a fixed communication topology and a switching communication topology. Finally, simulation results illustrate the effectiveness of the proposed control scheme for the problem of environmental monitoring.

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

This paper proposes an event-based networked set-membership filtering method to detect islanding fault for distributed grid-connected solar photovoltaic generation systems. The method enables each set-membership filter to offer an ellipsoidal estimation set, which is used to judge whether or not the islanding fault happens. When islanding fault happens, the intersection of the ellipsoids is empty, and when islanding fault is free, the intersection of the ellipsoids is nonempty. In the filtering scheme, a novel event-triggered mechanism is proposed to reduce the transmission frequency for saving the communication resources. The condition of the existence of the set-membership algorithm is derived by a time-varying convex optimization approach. A simulation experiment and a comparative experiment are provided using Sim-Power-Systems implementation based on a 2-kW single-phase grid-connected power generation system to illustrate the effectiveness of the proposed method for the detection of the islanding fault and the reduction of the resource consumption, respectively.

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

This paper is concerned with the synchronization of multiagent systems connected via different types of interactions, known as multilayer networks. Additive coupling and Markovian switching coupling are proposed to capture the layered connections with two kinds of mathematical models constructed. First, based on simultaneously diagonalization of multiple Laplacian matrices, a general criterion is derived, ensuring that the synchronization problem with additive coupling can be decoupled. Then, an alternative condition is presented, which is related to the number of layers, regardless of the number of agents. With the derived criteria, a concept of joint synchronization region is introduced and further discussed as a network design problem. Synchronization with Markovian switching layers is also analyzed in parallel, exemplified by some special cases of two-layer networks. Finally, a group of cellular neural networks coupled by two-layer connections are chosen to illustrate the effectiveness of the theoretical results.

This paper addresses an event-based consensus problem for a continuous-time nonlinear multi-agent system subject to communication resource constraints. The agents' states are measured by a multi-sensor network, in which sensor nodes are dispersedly deployed in a sensor field. Each sensor node can gather information from its neighboring nodes, process aggregated information by a prescribed network topology, sample processed information at a synchronous sampling period and then transmit sampled information to a remote controller node via a shared network medium. First, to reduce the frequency of transmitting sensors' sampled-data, an event-based communication strategy is presented to schedule sensors' data transmission. Second, based on transmitted aggregated measurement, a delicate event-based consensus protocol is proposed. Compared with some existing event-based consensus protocols based on transmitted local measurement, the proposed protocol offers some advantages. Third, a criterion for designing desired event-based consensus protocol and event triggering strategy is provided such that the proposed event-based consensus is achieved. It is shown that the consensus protocol gain and the event strategy threshold parameter can be jointly determined by solving a Riccati inequality. Finally, a vertical taking-off and landing aircraft model is employed to show the effectiveness of the proposed result.

Consensus problems of multi-agent systems have attracted an ever-increasing interest in the control community due to their great potential in various applications such as cooperative unmanned air vehicles, automated highway systems scheduling, air traffic control, sensor networks. This paper presents a brief overview of theoretical development of consensus in a sampled-data setting, paying special attention to those published since 2011. Recent results in this area are categorized into several directions, such as consensus based on periodic sampling, variable sampling, stochastic sampling, and event-triggered communication. In particular, for event-triggered consensus, some typical patterns of event-triggering conditions are reviewed based on different definitions of a transmission error and a threshold.

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

This paper proposes a novel islanding fault detection scheme based on the networked ellipsoidal estimation method for distributed grid-connected photovoltaic (PV) generation systems. The detection of the islanding condition is determined by computing the intersection of the global ellipsoidal estimations. While the global estimations are integrated at each sampling instant with all the local ellipsoidal estimations provided respectively by the local estimators in the communication network. If the islanding fault exists, the intersection of the global ellipsoids would be empty. If the islanding condition does not happen, the intersection of the global ellipsoids would be non-empty. The conditions on the existence of the local and the global ellipsoidal estimation methods are derived by two convex optimization approaches respectively. A simulation is provided by Matlab based on a model of 2-kW single-phase grid-connected power generation system to illustrate the effectiveness of the designed scheme on the detection of the islanding fault.

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

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

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

This paper deals with the problem of odor source localization by designing and analyzing a cooperative control framework (CCF) for the particle swarm optimization (PSO) algorithm. The CCF consists of three items: 1) a position coordination item; 2) a velocity coordination item; and 3) a movement direction coordination item. The position coordination item is used to coordinate relative positions between particles and to improve the exploration and exploitation capabilities of particles. The velocity coordination item enables the velocities of particles to reach consensus in order to realize orderly movement behaviors of particles. The movement direction coordination item guides particles to trace plumes and to locate odor sources. Stability of dynamic systems of particles with the proposed CCF is analyzed and the corresponding stability conditions are given such that the functions of three items in the CCF are realized. The orderly movement behaviors of particles under the CCF are also investigated based on benchmark functions. Finally, the effectiveness of the PSO algorithm with the proposed CCF is illustrated for the problem of odor source localization.

The papers in this special section provide a timely discussion on technical trends and challenges of some classical and emerging issues, such as networked control and filtering, fault detection and tolerant control, event-triggered control, security control, distributed control, sensor networks, and real-time network protocol design, over networks with resource constraints for industrial systems.

This paper is concerned with the network-based modeling, and observer-based fault detection filter (FDF) and controller coordinated design for an unmanned surface vehicle (USV) in network environments. Network-based models for the USV subject to actuator faults and wave-induced disturbances are established for the first time by introducing an observer-based FDF, and considering network-induced characteristics such as delays and packet dropouts in the sampler-to-control station communication network channel and the control station-to-actuator communication network channel. Based on these models, network-based FDF and controller coordinated design criteria are derived to asymptotically stabilize the residual system. The designed network-based FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the USV to external disturbances. Fault detection performance analysis verifies the effectiveness of the proposed network-based FDF and controller coordinated design scheme for the USV in network environments.

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

This paper is concerned with optimal communication network-based H∞ quantized control for a discrete-time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is quantized by a logarithmic quantizer before transmission, and communication network-induced packet dropouts can be described by a Bernoulli distributed white sequence. A new approach is developed such that controller design can be reduced to the feasibility of linear matrix inequalities, and a desired optimal control gain can be derived in an explicit expression. It is worth pointing out that some new techniques based on a new sector-like expression of quantization errors, and the singular value decomposition of a matrix are developed and employed in the derivation of main results. An illustrative example is presented to show the effectiveness of the obtained results.

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

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

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

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

This paper is concerned with distributed event-triggered H∞filtering for a discrete-time linear system over a sensor network. Sensor nodes are physically distributed and communicated through network medium. Firstly, a distributed event-triggered communication scheme is presented to determine when the measurement output on each sensor node should be broadcast and transmitted to its neighboring nodes. Each sensor node is equipped with an event monitor which consists of an event generator and a store. The event generator is configured to produce a series of events according to an event triggering rule. The store is employed to reserve the latest data packets. Secondly, based on the Lyapunov functional approach, criteria for analyzing H∞filtering performance and designing desired distributed event-triggered H∞filters are derived such that the resultant filtering error system is asymptotically stable with a prescribed H∞performance index. The filter design problem is posed in terms of linear matrix inequalities. Finally, an illustrative example is given to demonstrate the effectiveness of the obtained theoretical results.

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

This paper investigates network-based output tracking control for a T-S fuzzy system that can not be stabilized by a nondelayed fuzzy static output feedback controller, but can be stabilized by a delayed fuzzy static output feedback controller. By intentionally introducing a communication network that produces proper network-induced delays in the feedback control loop, a stable and satisfactory tracking control can be ensured for the T-S fuzzy system. Due to the presence of network-induced delays, the fuzzy system and the fuzzy tracking controller operate in an asynchronous way. Taking the asynchronous operation and network-induced delays into consideration, the network-based tracking control system is modeled as an asynchronous T-S fuzzy system with an interval time-varying delay. A new delay-dependent criterion for L2 -gain tracking performance is derived by using the deviation bounds of asynchronous normalized membership functions and a complete Lyapunov-Krasovskii functional. Applying a particle swarm optimization technique with the feasibility of the derived criterion, a novel design algorithm is presented to determine the minimum L2 -gain tracking performance and control gains simultaneously. The effectiveness of the proposed method is illustrated by performing network-based output tracking control of a Duffing-Van der Pol's oscillator.

This paper studies global synchronization between a heterogeneous dynamical network and a known target trajectory via distributed impulsive control. Synchronization with an error level, called quasi-synchronization, is analyzed by utilizing the time-varying Lyapunov function. Some sufficient quasi-synchronization conditions are presented and explicit expressions of error levels are derived. Furthermore, the effects of the pinning control matrix and the coupling strength are explored. Unlike the continuous pinning feedback control, it is shown that a large coupling strength will destroy synchronization in the present of impulsive controller, which is also verified by our simulations.

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

This paper is concerned with control for a sampled-data system. A saturated quantizer-based controller is introduced. The corresponding closed loop system is transformed into a system with input saturation and bounded external disturbance. A new Lyapunov functional is constructed to derive a sample-interval dependent condition on the existence of a state feedback controller such that the closed-loop system is exponentially convergent to an ultimate ellipsoid for the initial condition starting from some initial ellipsoid. Based on the condition, the desired controller is designed. An example is given to illustrate the effectiveness of the proposed method.

This paper is concerned with network-based H∞control for an offshore steel jacket platform subject to nonlinear wave-induced force and external disturbance. A network-based dynamic model of the offshore platform with an active tuned mass damper mechanism is presented, and a network-based H∞control scheme is presented to attenuate the vibration of the offshore platform. The effects of the network-induced delays on the H∞control for the offshore platform is investigated. Simulation results show that the proposed network-based H∞control scheme can improve the control performance of the offshore platform significantly. In addition, the control force and the oscillation amplitudes of the offshore platform under the network-based H∞controller with proper network-induced delays are smaller than the ones under the H∞controller without network settings, which means that the proper network-induced delays are of the positive effects on the H∞control of the offshore platform.

In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.

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

This paper considers the H∞control problem for discrete-time Takagi-Sugeno (T-S) model fuzzy systems with event-triggered output feedback. The measurement output is transmitted to a fuzzy controller when the output error exceeds a pre-given threshold. The parallel distribution compensation (PDC) can not be used for controller design since the controller may not receive enough information about premise variables of the plant due to the event-triggered transmission scheme. A fuzzy dynamical output feedback controller is proposed to regularly generate the control input, which makes the controlled system stable with a certain H∞disturbance attenuation level. A numerical example is given to show the effectiveness of the proposed approach.

This paper is concerned with event-triggered H∞filtering for networked systems. A novel event-triggering scheme is proposed by taking network dynamics into account simultaneously. First, an information dispatching middleware is constructed to establish a novel framework for networked systems, where two modules namely information selection module and congestion avoidance module are introduced. The information selection module aims to regulate the transmission of the sampled data in terms of a predefined event-triggering condition. The congestion avoidance module is used to schedule those sampled data released by the information selection module to the filter. Second, the on-line scheduling strategy is proposed under this framework. Then the filtering error system based on network dynamics is formulated as a system with an interval time-varying delay. Third, Lyapunov-Krasovskii functional approach is employed to formulate a new sufficient condition to ensure the stability and to guarantee a prescribed H∞noise attenuation performance for the filtering error system. Based on this condition, H∞filtering parameters, network dynamic controllers and event-triggering parameters can be co-designed provided that a set of linear matrix inequalities are feasible. Finally, an example is given to illustrate the merits and effectiveness of the method proposed in this paper.

This paper is concerned with event-triggered output feedback dissipative control of network-based systems. A novel distributed discrete event-triggered control strategy, in which whether or not the sampled data should be transmitted is determined by a pre-specified event, is proposed by introducing distributed discrete event-triggered mechanisms. Under this strategy, a novel dissipative control protocol is presented, with which the closed-loop system can be transformed into a time-delay system. Then, by Lyapunov-Krasovskii functional theory, a new dissipative criterion is established such that the resulting system is (Q, S, R)-dissipative. Correspondingly, based on this condition, the design method of the output feedback dissipative controller is proposed. An illustrative example is given to show the effectiveness and feasibility of the proposed method.

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

This paper is concerned with network-based control for an offshore steel jacket platform subject to nonlinear wave-induced force. A network-based dynamic model of the offshore platform is presented and a network-based state feedback control scheme is developed to reduce the internal oscillations of the offshore platform. Then the effect of a network-induced delay on the control performance of the offshore platform is investigated. It is found through simulation results that (i) the network-based state feedback controller is of a smaller gain in the sense of Euclidean norm than the state feedback controller without network setting, which means the network-based state feedback controller requires less control force; and (ii) the network-induced delay is of the positive effect on state feedback control of the offshore platform.

This paper first proposes a discrete event-triggered communication scheme for a class of networked Takagi-Sugeno (T-S) fuzzy systems. This scheme has two main features: 1) Whether or not the sampled state should be transmitted is determined by the current-sampled state and the error between the current-sampled state and the latest transmitted state. Compared with those in a periodic time-triggered communication scheme, the communication bandwidth utilization is considerably reduced while preserving the desired control performance; and 2) it is a discrete event-triggered communication scheme due to the fact that the triggered conditions are only measured and checked at a constant sampling period. Compared with a continuous event-triggered communication scheme, the special hardware for continuous measurement and computation is no longer needed. Second, a networked T-S fuzzy model is delicately constructed, which not only considers nonuniform time scales in the networked T-S fuzzy model and the parallel distributed compensation fuzzy control rules but includes the aforementioned state error as well. Third, a stability criterion and a stabilization criterion about the networked T-S fuzzy system are derived, respectively. The stability criterion and stabilization criterion can provide a tradeoff to balance the required communication resource and the desired performance: Lowering the desired performance allows the network to allocate more limited bandwidth to other nodes in need. Finally, a numerical example is given to show the effectiveness of the proposed method.

This paper deals with a class of optimization problems by designing and analyzing a finite-time particle swarm optimization (FPSO) algorithm. Two versions of the FPSO algorithm, which consist of a continuous-time FPSO algorithm and a discrete-time FPSO algorithm, are proposed. Firstly, the continuous-time FPSO algorithm is derived from the continuous model of the particle swarm optimization (PSO) algorithm by introducing a nonlinear damping item that can enable the continuous-time FPSO algorithm to converge within a finite-time interval and a parameter that can enhance the exploration capability of the continuous-time FPSO algorithm. Secondly, the corresponding discrete-time version of the FPSO algorithm is proposed by employing the same discretization scheme as the generalized particle swarm optimization (GPSO) such that the exploiting capability of the discrete-time FPSO algorithm is improved. Thirdly, a Lyapunov approach is used to analyze the finite-time convergence of the continuous-time FPSO algorithm and the stability region of the discrete-time FPSO algorithm is also given. Finally, the performance capabilities of the proposed discrete-time FPSO algorithm are illustrated by using three wellknown benchmark functions (global minimum surrounded by multiple minima): Griewank, Rastrigin, and Ackley. In terms of numerical simulation results, the proposed continuous-time FPSO algorithm is used to deal with the problem of odor source localization by coordinating a group of robots.

This paper considers H∞tracking control for a class of network-based T-S fuzzy systems with available asynchronous constraints on membership functions. Using a fuzzy controller associated with available sampled-data of both premise variables and feedback states, the network-based control system is equivalent to an asynchronous T-S fuzzy system with an interval time-varying sawtooth delay. Notice that the routine relaxation methods in traditional T-S fuzzy systems can not be used for stability analysis and controller design of the asynchronous fuzzy system since common product terms of membership functions can not be grouped. Instead, a new relaxation method is proposed by using asynchronous constraints on fuzzy membership functions to introduce some free weighting matrices. By using the proposed relaxation method and a new discontinuous Lyapunov-Krasovskii functional, some criteria on H∞tracking performance analysis and controller design are derived. Compared with the existing results, the derived criteria are of less conservatism and allow the existence of a network-based fuzzy controller with different gains, which can ensure a better H∞tracking performance. The effectiveness of the proposed method is illustrated by an example.

This paper is concerned with network-based static output feedback tracking control for a class of nonlinear systems that can not be stabilized by a static output feedback controller without a time-delay, but can be stabilized by a delayed static output feedback controller. For such systems, network-induced delay is intentionally introduced in the feedback loop to produce a stable and satisfactory tracking control. The nonlinear network-based control system is represented by an asynchronous T-S fuzzy system with an interval time-varying sawtooth delay due to sample-and-hold behaviors and network-induced delays. A new discontinuous complete Lyapunov-Krasovskii functional, which makes use of the lower bound of network-induced delays, the sawtooth delay and its upper bound, is constructed to derive a delay-dependent criterion on H∞tracking performance analysis. Since routine relaxation methods in traditional T-S fuzzy systems can not be employed to reduce the conservatism of the stability criterion, a new relaxation method is proposed by using asynchronous constraints on fuzzy membership functions to introduce some free-weighting matrices. Based on the feasibility of the derived criterion, a particle swarm optimization algorithm is presented to search the minimum H∞tracking performance and static output feedback gains. An illustrative example is provided to show the effectiveness of the proposed method.

This paper investigates network-based fuzzy static output feedback tracking control for Van der Pol's oscillators using positive effects of network-induced delays. Taking network-induced delays into account, the network-based nonlinear control system is modeled as an asynchronous T-S fuzzy system with an interval time-varying delay. A discontinuous complete Lyapunov-Krasovskii functional and a new relaxation method involving the asynchronous constraints on membership functions are proposed to derive some delay-dependent criteria on H∞tracking performance analysis and controller design of the asynchronous fuzzy system. The effectiveness of the proposed method is shown by a numerical example.

This paper is concerned with sampled-data stabilization for Takagi-Sugeno (T-S) fuzzy systems by using the property of membership function deviations. A new lemma is presented to obtain an explicit estimate for the bounds of membership function deviations in sampled-data fuzzy control systems and to establish the quantitative relationship between the deviation bounds and the upper bound of sampling intervals. By using a piecewise Lyapunov-Krasovskii functional and a generalized Jensen integral inequality, a stability criterion is derived. Based on the stability criterion, a membership function deviation approach for designing a sampled-data fuzzy controller is proposed. Compared with some existing ones, the obtained results can reduce the conservativeness, which are confirmed by a numerical example.

This paper is concerned with robust sampled-data fuzzy control for Takagi-Sugeno (T-S) model-based fuzzy systems with interval time-varying delay and norm-bounded parameter uncertainties. By constructing a new Lyapunov-Krasovskii functional, a less conservative sufficient condition on the existence of the sampled-data fuzzy controller for the fuzzy system is formulated in the form of matrix inequalities, which is dependent on both the size of delay-derivative and allowable upper bound of time-varying sampling period, by using a new tighter bounding technique. A feasible solution can be obtained by solving a minimization problem in terms of linear matrix inequalities. Finally, the backing-up control of a computing simulated truck-trailer is discussed to show the effectiveness of the obtained design approach.

This paper investigates stability and passivity of negative feedback interconnection of two passive systems, which are interconnected through communication networks. The insertion of communication networks between negative feedback interconnected passive systems inevitably induces delays and data packet dropouts. To model the network-based negative feedback interconnected system, an appropriate network scheduling method is presented to deal with time-varying network-induced delays and data packet dropouts. By constructing a novel discontinuous Lyapunov-Krasovskii functional, a less conservative sufficient condition for the network-based feedback interconnected system to be asymptotically stable is derived. Based on the stability condition, a new sufficient condition to make the negative feedback interconnected system in network environments remain passive is developed. A numerical example is provided to demonstrate the effectiveness of the design method.

In this paper, we present a novel iterative LMI approach to deal with the control problem for networked control systems (NCSs) with multiple packet dropouts. Two channel packet dropouts are simultaneously considered due to limited communication capacity. One is measurement channel packet dropout which is from the sensor to the controller. The other is control channel packet dropout which is from the controller to the actuator. The NCSs with both multiple measurement and control packet dropouts are first modeled as a stochastic parameter system which contains two independent Bernoulli distributed white sequences. A dynamic output controller is then designed to exponentially stabilize the networked system in the sense of mean square, and also achieve the prescribed H∞disturbance attenuation level. An iterative algorithm is developed to compute the optimal H∞disturbance attenuation and the controller parameters by solving the semi-definite programming problem via interior-point approach. Finally, two illustrative examples are provided to show the applicability of the proposed method.

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

This paper is concerned with non-fragile H∞control for offshore steel jacket platforms subject to self-excited nonlinear wave force and external disturbances. A delayed non-fragile H∞controller is designed to attenuate the oscillation amplitudes of the offshore platform. The positive effects of the time-delays on non-fragile H∞control for the offshore platform are investigated. It is shown through simulation results that (i) the proposed non-fragile H∞controller is effective to reduce the vibration of the offshore platform; (ii) the control force required by the delayed non-fragile H∞controller is smaller than the one by the delay-free non-fragile H∞controller; and (iii) the allowable maximum time-delay under the delayed non-fragile H∞controller is much larger than the one under the delayed dynamic output feedback controller.

Shift variance bounds of critically sampled multirate filter banks are usually defined by using eigensystem analysis of the commutator operator. Computational methods are provided for two-channel FIR filter banks by Aach and Fuhr (2009). This paper investigates shift variance bounds in oversampled multi-channel filter banks and derives some general results which include some of Aach and Fuhr (2009) as special cases. Using polyphase representation, numerical methods are provided to compute the shift variance bounds for arbitrary filter banks. Examples are given to demonstrate the effectiveness of the obtained results.

This paper investigates the problem of master-slave synchronization of two delayed Lur'e systems in the presence of parameter mismatches. First, by analyzing the corresponding synchronization error system, synchronization with an error level, which is referred to as quasi-synchronization, is established. Some delay-dependent quasi-synchronization criteria are derived. An estimation of the synchronization error bound is given, and an explicit expression of error levels is obtained. Second, sufficient conditions on the existence of feedback controllers under a predetermined error level are provided. The controller gains are obtained by solving a set of linear matrix inequalities. Finally, a delayed Chua's circuit is chosen to illustrate the effectiveness of the derived results.

This brief is concerned with delay-dependent stability for neural networks with two additive time-varying delay components. By constructing a new Lyapunov functional and using a convex polyhedron method to estimate the derivative of the Lyapunov functional, some new delay-dependent stability criteria are derived. These stability criteria are less conservative than some existing ones. An example is given to demonstrate the less conservatism of the stability results.

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

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

This paper is concerned with controller design for a class of nonlinear networked control systems. These systems are approximated by uncertain linear networked Takagi-Sugeno (T-S) models with both network-induced delay and data packet dropout. Sufficient conditions are derived for the existence of a fuzzy controllers. Then, an iterative algorithm for the controller design is proposed. A control problem of a flexible-joint robot arm system is studied to show the effectiveness of the iterative algorithm.

This paper is concerned with the problem of robust Hinfincontrol for a class of uncertain time-delay fuzzy systems. The time-delay is assumed to be a time-varying continuous function belonging to a given interval, which means that the lower and upper bounds for the time-varying delay are available. No restriction on the derivative of the time-varying delay is needed, which allows the time-delay to be a fast time-varying function. The Takagi-Sugeno (T-S) uncertain fuzzy model with interval time-varying delay is adopted. Based on the Lyapunov-Krasovskii functional approach, some delay-dependent conditions for the existence of robust Hinfincontroller are formulated in the form of linear matrix inequalities (LMIs). When these LMIs are feasible, an Hinfincontroller is presented. A numerical example is given to demonstrate the effectiveness of the proposed method

This paper is concerned with the problem of local and global asymptotic stability for a class of discrete-time recurrent neural networks, which provide discrete-time analogs to their continuous-time counterparts, i.e., continuous-time recurrent neural networks with distributed delay. Some stability criteria, which include some existing results as their special cases, are derived. A discussion about the dynamical consistence of discrete-time neural networks versus their continuous-time counterparts is provided. An unconventional finite difference method is proposed and an example is also given to show the effectiveness of the method.

The uniform asymptotic stability of recurrent neural networks (RNNs) with distributed delay is analyzed by comparing RNNs to linear Volterra integro-differential systems under Lipschitz continuity of activation functions. The stability criteria obtained have unified and extended many existing results on RNNs.