In this paper, the problem of proportional-integral observer (PIO) design is investigated for a class of discrete-time multi-rate systems with multiple sensors, with the sensor sampling periods being allowed to differ from the system updating periods. The facilitation of communication between sensors and the remote PIO through wireless networks, which are subject to probabilistic packet dropouts, is achieved through the utilization of a decode-and-forward relay-based strategy. The occurrence of packet dropouts is governed by a Bernoulli-distributed random variable whose probability is dependent on the available transmission power. A decode-and-forward relay-based strategy, developed based on different components, is capable of processing information from different encoders at different physical locations. For the convenience of observer design, the lifting technique is employed with aim to cast the multi-rate system into a single-rate one. By establishing sufficient conditions, the combined effect of external noises and relaying-aided communication on estimation performance is intuitively illustrated. Subsequently, a PIO with an adjustable parameter is designed by solving certain optimization problems. A simulation example is finally provided to validate the theoretical results.

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.

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.

This paper is concerned with the problem of distributed Nash equilibrium seeking (DNES) for first-order multi-agent systems (MASs) in unreliable networks. Two scenarios of the network environment are considered: no-attack scenario and attack scenario. Firstly, a dynamic event-triggered mechanism (DETM) is designed for each player to transmit state and estimation when necessary. In the no-attack scenario, a DNES strategy based on the DETM is proposed. With a well-designed Lyapunov function, some sufficient conditions are derived to guarantee the convergence of each player to the Nash equilibrium (NE). In the attack scenario, we consider denial-of-service (DoS) attacks and model the effect of the attacks on the system as the communication topology switching obeying a Markov process. A distributed dynamic event-triggered Nash equilibrium seeking strategy in the framework of Markov switching is designed for attack scenario. By analyzing the characteristics of the ratio between attack frequency and duration, some sufficient conditions are derived to ensure that each player can successfully find the NE under DoS attacks. Finally, a heating, ventilation, and air conditioning (HVAC) system is employed to verify that the DNES can be achieved in both no-attack and DoS attack scenarios via the proposed DNES strategy with the presented DETM.

This paper studies optimal aggregative coordination in open multi-agent systems, where agent membership, communication topology, and local objectives may vary over time. The problem is formulated as a distributed aggregative optimization task, in which each agent’s local objective depends on its own decision variable and an aggregate generated by all currently active agents. An open-network distributed aggregative gradient tracking algorithm with a residual handoff mechanism for departing agents is developed, which preserves aggregate and gradient-tracking balance across topology transitions. Furthermore, an auxiliary frozen closed-network iteration is introduced, based on which a one-step open-network error recursion is derived by bounding the effects of optimal-point variation, arrivals, departures, and objective switching. Under suitable contraction and handoff small-gain conditions, an explicit ultimate bound is established for the normalized error relative to the optimal solution trajectory. The bound separates the effects of optimal-point variation, objective-switching variation, and open-network membership changes, and quantifies the influence of arrivals, departures, and residual handoff on the tracking accuracy. Numerical experiments on open multi-robot surveillance problems support the theoretical findings and demonstrate the effectiveness of the proposed method under dynamic network changes.

This article studies the finite-time time-varying formation control problem for multiple mobile robots. First, an edge-based bounded control protocol, constructed using the hyperbolic tangent function $\tanh (\cdot)$, is proposed to achieve global finite-time formation under a fixed undirected topology. The protocol is then extended to handle switching undirected topologies, adapting to dynamic communication networks. To alleviate the communication burden, an event-triggered mechanism is incorporated, resulting in an event-based protocol that ensures practical finite-time formation under both fixed and switching topologies while significantly reducing data transmission. The control inputs in all proposed protocols remain within predefined saturation bounds, which can be directly specified according to actuator limits. Finite-time stability is rigorously established via Lyapunov analysis, with explicit upper bounds derived for the closed-loop settling time. Finally, experimental results validate the effectiveness of the proposed control strategies.

Nonintrusive load monitoring (NILM) is an effective approach for energy management that disaggregates the total power measured at the main power inlet into appliance-level power signals. NILM algorithms have achieved remarkable progress in recent years. However, accurately reconstructing appliance-level power signals from unseen, complex, and diverse aggregated data remains a formidable challenge. To address this challenge, this article proposes a novel hybrid load disaggregation model, the Latent Abstraction Bridge (LAB) Transformer, built on a sequence-to-sequence (S2S) framework that integrates a convolutional neural network (CNN) and a Transformer architecture with an embedding-constrained generative network termed LAB. The LAB effectively balances local discrete details and global information by leveraging a soft vector-quantized variational autoencoder (SoftVQ-VAE) and a beta-variational autoencoder (Beta-VAE) to constrain the encoder’s output representations, thereby considerably improving the model’s ability to generalize and discriminate in latent space. Moreover, we use parameter-free linear interpolation to recover the lengths of Beta-VAE output vectors, preserving essential global information while suppressing unnecessary local details, thereby substantially reducing the parameter count. The effectiveness of the proposed model is validated on two datasets: UK-DALE and REFIT. Experimental results indicate that it achieves the best F1 score, while lowering the mean absolute error (MAE) and signal aggregation error (SAE) by 22.8% and 24.7%, respectively, compared to several recent state-of-the-art models.

As a closed-loop learning control method, repetitive control has been widely used in a variety of areas from appliances to aviation. A repetitive control system features perfect reference tracking and disturbance rejection in the steady state for periodic signals with a fixed period. This characteristic is important not only for conventional technologies and conventional industries but also for advanced technologies and emerging industries. This paper first explains the concept of repetitive control from its original idea. Next, it describes the structure of a repetitive controller as an internal model and shows the respective points of continuous- and discrete-time repetitive control. It presents a categorized list of practical applications of repetitive control. Moreover, two concrete applications, namely the control of a robotic manipulator and a rotating system, demonstrate the validity of the method with experimental results. Several current studies in this field are also reviewed, and some challenges and future studies for repetitive control are provided.

Accurate 3D object detection is essential for ensuring the safety of autonomous vehicles. Cooperative perception, which leverages vehicle-to-everything (V2X) communication to share perceptual data, enhances detection but is vulnerable to channel impairments, such as noise, fading, and interference. To strengthen the reliability of intelligent transportation systems, this work improves the robustness of V2X cooperative perception under communication conditions that reflect common channel impairments. This paper proposes an Adaptive Feature Fusion Transformer (AFFormer), a Transformer-based framework that mitigates the adverse effects of corrupted features by modeling temporal, inter-agent, and spatial correlations. AFFormer introduces three key modules: Multi-Agent and Temporal Aggregation for context-aware fusion across agents and over time, Dual Spatial Attention for efficient modeling of spatial dependencies, and Uncertainty-Guided Fusion for entropy-driven refinement of fused features. A teacher-student knowledge distillation strategy further enhances robustness by aligning fused features with reliable early-collaboration supervision. AFFormer is validated on the V2XSet and DAIR-V2X datasets, where it consistently outperforms existing methods under both ideal and impaired communication conditions, demonstrating improved robustness to communication-induced feature degradation while maintaining a competitive efficiency-accuracy trade-off.

The rapid evolution of large language models (LLMs) towards autonomous Agentic artificial intelligence (AI) necessitates a systemic overhaul across algorithms, infrastructure, and architectures. This paper presents a unified view of the “Agentic AI Infrastructure,” connecting research threads often studied in isolation. First, post-training algorithms are reviewed, contrasting traditional reinforcement learning (RL) with emerging reasoning-centric methods and test-time scaling strategies. Next, the transition of RL training frameworks is analyzed from monolithic, colocated designs to disaggregated, asynchronous architectures tailored for the extreme variance of agentic rollouts. Furthermore, progress in agent construction is synthesized, covering reflection, planning, tool use, and multi-agent collaboration. By integrating these layers, the paper elucidates how agentic AI systems impose unique demands on underlying training systems. Finally, open challenges are outlined by covering capability scaling, efficiency, safety, privacy, and governance for reliable real-world agentic AI deployment.

This letter investigates a data-driven model predictive control (MPC) scheme for time-delay systems with unknown dynamics as well as input and state constraints. An infinite-horizon optimization problem is first formulated, in which a data-driven system representation is employed as a predictive model, and a delay-dependent state feedback controller is designed. By introducing a Lyapunov function, the control problem is systematically reduced to a tractable form, with a sufficient condition for the controller existence derived based on linear matrix inequality techniques. Then, the recursive feasibility of the MPC optimization and the stability of the resulting closed-loop system are rigorously established. Finally, the effectiveness of the proposed method is verified through numerical simulation.

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 and self-attention mechanisms, which efficiently formulate 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. Our proposed method utilizing LiDAR alone has shown exceptional performance compared to other online tracking approaches, including LiDAR-based and LiDAR-camera fusion-based methods. LEGO ranked 1st at the time of submitting results to KITTI object tracking evaluation ranking board and remains 2nd at the time of submitting this paper, among all online trackers in the KITTI MOT benchmark for cars1

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.

Autonomous self-hosted AI agent platforms are rapidly evolving from prompt-response assistants into persistent systems that can maintain long-lived state, invoke tools, ingest external content, and execute environment-changing actions. While this transition enables practical automation, it also introduces lifecycle security risks that cannot be fully explained by prompt-level analysis alone. In this paper, a security analysis of OpenClaw is presented, with OpenClaw serving as a representative autonomous agent operating environment and a concrete case study for broader security challenges in emerging agent ecosys-tems. A trust-boundary-first perspective is adopted to examine how attacks propagate across five boundary classes: Channel-Access, Session-and-State, Tool-Execution, External-Content, and Extension Supply-Chain. The results presented in this paper show that threats such as indirect prompt injection, memory poisoning, unsafe tool invocation, data exfiltration, and malicious skill abuse are not isolated anomalies; rather, they are stage-specific mani-festations of a common systems problem in which untrusted influence progressively crosses into higher-privilege contexts. Based on this analysis, the defense-in-depth implications for OpenClaw deployments are discussed, including boundary-aware isolation, capability-scoped tool mediation, memory integrity controls, extension governance, and evidence-oriented operational oversight. This study provides a practical framework for evaluating and hardening long-running, tool-capable, autonomous AI agents in realistic deployment settings.

This paper is concerned with event-triggered control that deals with noisy data for both discrete-time and continuous-time linear systems with unknown system matrices. First, based on a sufficiently rich finite set of noisy data collected in an experiment, the pair of system matrices is represented as a data-based nominal matrix plus an uncertain matrix with a bounded norm. This formulation enables classical robust control techniques to be applied to tackle the robust control problem. Second, for discrete-time systems, a novel event-triggering condition is proposed, by which an event is triggered if the sum of the squares of the weighted error exceeds the square of the weighted state from the previous event. For continuous-time systems, the event-triggering condition is devised as a monotonically increasing function that starts with a negative value and triggers an event when it reaches zero. This condition can exclude the so-called Zeno behaviour due to its monotonic increase property. Third, by employing a looped functional method, several criteria are derived to co-design suitable state feedback controllers and event-triggering parameters for the systems under study. Finally, the effectiveness of the proposed method is demonstrated through a case study involving a batch reactor system.

With the rapid advancement of artificial intelligence, multi-agent systems (MASs) are evolving from classical paradigms toward architectures built upon large foundation models (LFMs). This survey provides a systematic review and comparative analysis of classical MASs (CMASs) and LFM-based MASs (LMASs). First, within a closed-loop coordination framework, CMASs are reviewed across four fundamental dimensions: perception, communication, decision-making, and control. Beyond this framework, LMASs integrate LFMs to lift collaboration from low-level state exchanges to semantic-level reasoning, enabling more flexible coordination and improved adaptability across diverse scenarios. Then, a comparative analysis is conducted to contrast CMASs and LMASs across architecture, operating mechanism, adaptability, and application. Finally, future perspectives on MASs are presented, summarizing open challenges and potential research opportunities.

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.

This paper considers a multi-Unmanned Surface Vehicle (multi-USV) escort problem, in which a group of slave USVs need to achieve formation and interception of intruders so as to protect the master vessel against the intruders. The mission scope includes maintaining a protective formation around the master vessel, selecting appropriate interceptors upon detect intruders while retaining followers to preserve coverage, and adapting online to uncertain intruder maneuvers through real-time decision making. An Adaptive Model Predictive Control (AMPC) escort model is established for the considered problem, which takes the future maneuvers of the intruders into consideration. An offline-learned, online-corrected maneuver predictor based on a Mixture Density Network (MDN) and a Kalman Filter (KF) is developed: the MDN captures multi-modal uncertainty in intruder strategies, while the KF corrects predictions when strategies shift, thereby providing robustness even with limited historical data. To enhance solution efficiency, the Crested Porcupine Optimizer is improved via chaotic initialization, random restarts and enhanced cyclic population reduction (CPR) mechanism as desensitization strategies, jointly accelerating convergence and strengthening exploration. Numerical simulations show improvements in interception performance, prediction accuracy, generalization to unknown intruder strategies, and computational efficiency.

This paper studies the problem of aggregative optimization in open multi-agent systems (OMAS), where agents are allowed to join and leave the system in a free manner during the decision-making process. A multi-aggregator communication mechanism is proposed to facilitate information exchange among agents, in which the aggregators are responsible for collecting information from agents and exchanging them with neighboring aggregators. Based on the multi-aggregator communication mechanism, a novel semi-distributed algorithm is designed. Dynamic regret is taken as a metric to evaluate the performance of the proposed algorithm. It is analytically shown that the dynamic regret can be bounded by the sum of agents' individual regrets, each of which grows sub-linearly with its active period length under locally diminishing step-sizes and a slowly changing environment. Moreover, to quantify the impact of agents' joining and leaving on algorithm performance, the regret is analyzed in the case of agent replacement, wherein some of the agents are replaced by new ones though the number of agents remains constant over time. Additionally, the algorithm is simplified in some special cases, resulting in a tighter regret bound. Finally, two numerical examples on target surrounding problems and price based energy management are given to verify the effectiveness of the proposed methods.