A collaborative cloud-based control and defense framework is presented to address the challenges of low-carbon economy and compound attacks such as false data injection (FDI) and denial-of-service (DoS) in distribution networks. The framework consists of two main layers: the cloud-based multi-objective optimization layer and the control & defense layer. The upper cloud computing layer focuses on multi-objective optimization, aiming to minimize generation cost, line losses, and node voltage deviations in low-carbon conditions. Meanwhile, the lower layers combine control with attack defense strategies. State-feedback control is utilized to regulate the dynamics of the distributed generation, and defense strategies are employed to protect against potential compound attacks (FDI and DoS attacks). The defense strategy employs a dual control law sliding mode observer for attack reconfiguration, complemented by periodic event triggering. Also, the input-state stability of the control strategy is demonstrated. To validate the effectiveness of the proposed control strategy, simulations are conducted both on a computer and on the StarSim hardware-in-the-loop experimental platform.

This paper investigates an adaptive predefined-time distance regulation control strategy for a two-dimensional (2-D) vehicle platoon with actuator faults and input saturation. First, a novel distance regulation scheme is introduced for scenarios where a ramp vehicle platoon merges into the main lane. After multi-lane fusion is completed, a designated vehicle in the main-lane platoon that receives the merging request actively increases its inter-vehicle distance to create a safe gap, while the distances of the other vehicles remain unchanged. This adjustment may temporarily enlarge tracking errors, which can potentially violate the prescribed performance function (PPF) constraints and threaten platoon stability. To address this issue, a modified prescribed performance function (MPPF) is developed to dynamically relax the error boundary within a specified time window and restore it after merging. Subsequently, an adaptive neural network-based dynamic surface control (DSC) approach is employed to design saturation and fault-tolerant throttle/brake inputs. A predefined-time angle controller is also designed based on an angle sliding mode surface. The resulting position and angle controllers enable the platoon to complete multi-lane fusion and achieve predefined-time stability despite ramp merging. Finally, simulation results validate the effectiveness of the proposed strategy.

This study investigates the mean-square reachable set (RS) consensus of nonlinear Markov-switched multiagent systems (MASs) with time-varying delays, in which a multirate sampled-data consensus (MRSDC) control scheme is designed for the first time under general uncertain semi-Markov transition (GUST) switched topologies. First, the nonlinear Markov-switched MAS is transformed into quasilinear subsystems by applying the Takagi-Sugeno (T-S) fuzzy modeling technique, where the GUST-based Markov model characterizes both the operation mode and abrupt variations in the communication network topologies among all agents. Second, an aperiodic MRSDC control strategy is developed to reduce the sampling frequency of certain sensors below the single-rate threshold by adaptively adjusting their sampling rates, thereby enhancing flexibility and improving consensus performance. Furthermore, a new free-weighting integral inequality is introduced to handle the integral quadratic term involving time-varying delay bounds. Subsequently, an appropriate looped-side Lyapunov functional is designed, leveraging aperiodic multirate sampling and time-varying delay characteristics. Next, by combining the constructed Lyapunov functional with the proposed integral inequality and an improved reciprocally convex combination inequality, sufficient conditions are derived in the form of linear matrix inequalities (LMIs). These conditions not only ensure the mean-square leaderless consensus of the resulting MASs but also guarantee that all reachable states remain confined within ellipsoidal attracting-like regions under the MRSDC scheme. Finally, numerical validations are conducted to demonstrate the effectiveness of the proposed MRSDC control strategies using interconnected single-link robot arm systems (SLRASs), while a comparative numerical example further illustrates the superiority of the proposed method.

This article focuses on the 2-D plane finite-time multilane fusion control problem with velocity sensor faults and actuator faults. First, considering the velocity sensor faults, radial basis function neural networks (RBFNNs) are introduced to approximate sensor fault functions. A finite-time fault-tolerant position controller is designed by employing a hyperbolic tangent function to address the partial failure of the position actuator. Second, in case of complete actuator failure, the backup actuator is activated without altering the controller structure. The majority of prescribed performance functions (PPFs) currently in use are unsuitable for addressing complete actuator failure. As tracking errors may exceed PPF constraints before the backup actuator is activated upon such faults, this could result in vehicle platoon instability. To address this issue, this article designs a modified PPF (MPPF) that is independent of the initial conditions and can adjust the performance boundary according to the error change at specific times by introducing a shifting function. When a complete actuator failure occurs, the MPPF can sacrifice part of the transient performance to enclose the increased tracking error within the range of the MPPF, thus maintaining the stability of the vehicle platoon. When the actuator is working normally or has a partial failure, it can restore the user-specified performance. Then, by constructing a finite-time angle sliding-mode surface, an angle controller is designed. The designed position and angle of finite-time controllers can ensure that the vehicle platoon achieves multilane fusion within a finite time. Finally, through simulation and comparative results, the effectiveness of the proposed MPPF and finite-time fault-tolerant algorithm is demonstrated.

The widespread application of intelligent storage systems has urged more requirements for system security. In this work, we consider a resilient pickup and delivery task of the multi-robot systems against strategic attacks. The strategic attacks terminate the normal motion of a fraction of key operating robots with a well-designed strategy, and thus jeopardize the cooperation among robots. A three-layered decision framework is proposed to suppress the above strategic attacks: The first layer introduces defensive strategies, which incorporate reputation mechanisms and counterfactual rescue mode allocations. The counterfactual rescue-mode allocation mechanism dynamically assesses the benefit differences between rescuing others for resilience and conducting self-tasks for efficiency. Based on the reputation mechanisms, the second layer employs a bi-level programming method for pickup/delivery mode allocations and task assignments. Then, the third layer calculates the collision-free path for the swarm using a mixed integer linear programming model. The practicality and resilience of this algorithm against strategic attacks have been demonstrated through numerical simulations involving various robot scales and task burdens.

This paper addresses the consensus control problem of heterogeneous multi-UAV systems against Byzantine attacks. A drone compromised by Byzantine attacks transmits erroneous values to its neighbors while applying wrong input signals for itself, which is aggressive and challenging to defend. Inspired by the concept of digital twin technology, we introduce a new hierarchical protocol equipped with a virtual twin layer (TL), which decouples the challenges into two defense schemes: one against Byzantine edge attacks on the TL and the other against Byzantine node attacks on the cyber-physical layer (CPL). In the TL, we provide a topology reconfiguration strategy that enhances the resilience of the communication network by judiciously adding a minimal number of key edges. We rigorously demonstrate that the control strategy attains asymptotic consensus within a finite timeframe, given that the topology on the TL adheres to a strongly$(2f+1)$-robustness criterion. Within the CPL, decentralized chattering-free controllers are proposed to ensure the resilient output consensus for the heterogeneous multi-UAV systems against Byzantine node attacks. Furthermore, the derived consensus controller exhibits an exponential convergence characteristic. The effectiveness and practicality of the obtained theoretical results are verified by a UAV swarm flight experiment. Note to Practitioners—Cooperative control of UAVs presents significant prospects for application and development, becoming a focal point of automatic control. By modeling UAV swarms as multi-agent systems, various complex distributed control methods have been conveniently proposed and implemented economically in practical systems. However, when certain agents are compromised and interfere with their neighbors, the whole network may become highly susceptible to failure. This paper specifically studies the resilient consensus control against the significant active internal threats, Byzantine attacks. The published results have primarily focused on cases where the leader UAV has no input signals. In practical applications, however, the leader often has a pre-established trajectory sent by the host and the followers are unaware of this input information. This significantly complicates the task of identifying Byzantine attackers. In this work, we introduce a new hierarchical protocol inspired by the concept of digital twin technology, which decouples the challenges into defense against Byzantine edge attacks on the TL and the defense against Byzantine node attacks on the cyber-physical layer. The experiment shows the feasibility and security of our control scheme, which provides valuable guidance for the practical applications of drones.

This paper addresses the problem of distributed estimation and motion control (DEMC) in multi-agent systems (MASs) with both linear and Lipschitz nonlinear dynamics. Unlike conventional DEMC methods designed for MASs under ideal conditions, this work investigates scenarios where all agents are vulnerable to various forms of attacks. The considered attacks comprise false-data injection (FDI) attacks and denial of service (DoS) attacks that affect the communication channels among agents to destabilize the MAS. Also, the unbounded actuator attacks which exist in practical environments to intentionally degrade the MAS performance is considered. To cope with these kinds of attacks, two novel resilient approaches are established aimed at estimating and following a mobile target under attacks. The proposed distributed attack-resilient control strategies are designed based on a dual-layer structure, guaranteeing effective DEMC with an ultimately bounded error. The results from two simulation examples are provided to validate the presented algorithms. Note to Practitioners—The motivation of this work is to deal with the DEMC problem for MASs under multiple attacks. In most of the existing DEMC schemes for MASs, having a healthy network and dynamics is a requirement. However, in practical environments, MASs as an important subclass of cyber-physical systems are subject to different types of attacks that affect the network and dynamics of MASs and may seriously jeopardize the performance of the DEMC algorithm, or even worse, lead to instability. Therefore, a resilient hierarchical DEMC algorithm is proposed for MASs which allows agents to estimate and follow a mobile target under multiple attacks. The proposed scheme is resilient to most existing cyber-attacks and is designed for MASs with both linear and nonlinear dynamics. It can be applied to various practical engineering systems such as autonomous vehicles, mobile robots, and intelligent transportation systems. The stability and convergence of the proposed algorithms are analyzed mathematically, and it is shown that the agents not only track the estimated target but also can cope with multiple attacks through simulation experiments.

This paper investigates the bipartite event-triggered output consensus problem in heterogeneous linear multi-agent systems (MASs) with a leader operating under signed jointly connected digraphs. The research addresses both cooperative and adversarial communication among agents by introducing a novel edge-based bipartite event-triggering mechanism (ETM), as well as a dynamic ETM for communication between the leader and followers. Subsequently, a distributed bipartite compensator utilizing the composite ETMs is proposed to estimate the states of the leader, and serves as a reference for the states of followers. Moreover, a significant feature of the compensator is that it reduces the frequency of communication between the leader and followers. Besides, it is proven that the system with the compensator can exclude Zeno behavior. Furthermore, observers designed to estimate the states of followers, as well as a new distributed control protocol, are proposed to address the output tracking problem of heterogeneous linear MASs. The results demonstrate that, through the proposed protocol, the output tracking error of the closed-loop control system converges to zero exponentially. Finally, the theoretical findings of this study are validated through a numerical example and an application example.

In this article, we discuss the event-based secure estimation issue for 2-D cyber-physical systems subject to deception attacks in the sensor-to-estimator channel. The game-theoretic framework is applied to established the equilibrium defense policy against the malicious attacks, and meanwhile the dynamic event-triggered mechanism is proposed for the limited communication resource. By resorting to Lyapunov functional approach and matrix techniques, sufficient conditions are attained to assure that the considered state estimation error dynamic is asymptotically mean square stable with an$H_\infty$performance. Then, based on the zero-sum game theory, a valid mixed defense mechanism is proposed. A defense-based remote estimator design algorithm that considers the interaction of the physical layer and the cyber layer is established. Finally, the validity of the developed estimation scheme is certificated by a simulation example.

Safe reinforcement learning (SRL) aims to realize a safe learning process for deep reinforcement learning (DRL) algorithms by incorporating safety constraints. However, the efficacy of SRL approaches often relies on accurate function approximations, which are notably challenging to achieve in the early learning stages due to data insufficiency. To address this issue, we introduce, in this work, a novel generalizable safety enhancer (GenSafe) that can overcome the challenge of data insufficiency and enhance the performance of SRL approaches. Leveraging model order reduction techniques, we first propose an innovative method to construct a reduced order Markov decision process (ROMDP) as a low-dimensional approximator of the original safety constraints. Then, by solving the reformulated ROMDP-based constraints, GenSafe refines the actions of the agent to increase the possibility of constraint satisfaction. Essentially, GenSafe acts as an additional safety layer for SRL algorithms. We evaluate GenSafe on multiple SRL approaches and benchmark problems. The results demonstrate its capability to improve safety performance, especially in the early learning phases, while maintaining satisfactory task performance. Our proposed GenSafe not only offers a novel measure to augment existing SRL methods but also shows broad compatibility with various SRL algorithms, making it applicable to a wide range of systems and SRL problems.

Discovering and tracking wildlife targets are essential for gaining insights into the behavioral patterns and habits of animals within their natural habitats. With low cost and high maneuverability, mini autonomous aerial vehicles (AAVs) can achieve robust and rapid locating and tracking of multiple targets through collaboration. This work proposes a method for multitarget task allocation and path planning for AAV swarms, addressing the challenges of locating and tracking multiple wildlife with very high frequency (VHF) radio tags while avoiding potential disturbances to the wildlife. Our approach proposes a layered framework for the multi-AAV multitarget wildlife tracking problem: 1) the state estimation layer performs fast receiver signal strength indicator (RSSI) signal acquisition and employs the particle filtering algorithm to localize targets’ positions; 2) the task assignment layer uses a quadratic allocation method for AAVs’ real-time target allocation, starting with reasonable initial target sets via mixed-integer programming and efficiently readjusting targets based on real-time environment; and 3) the motion planning layer introduces an optimization-based approach to generate smooth and executable trajectories that can simultaneously ensure desired safe distances from objects of interest. Simulation experiments validate the effectiveness of the obtained AAV swarm tracking scheme.

Routing is a key to achieving high-performance data center networks. However, due to the significant variations in network temporal information, traditional routing protocols fail to effectively capture the sequential characteristics and patterns of network traffic. To address this issue, a new dynamic routing approach is proposed through deep reinforcement learning with a modified transformer architecture, enabling an effective identification of sequential features in network traffic. By testing Software Defined Networking (SDN) in two simulated Data Center Network (DCN) topologies, we compare our approach with a traditional routing protocol and a nontemporal DRL routing protocol, demonstrating the efficacy and merits of our proposed solution.

Motivated by the substantial achievements of Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics. LLMs are advantageous in offering the potential to enhance the generalizability as task-agnostic planners and facilitate flexible interaction between human instructors and planning systems. However, task plans generated by LLMs often lack feasibility and correctness. To address this challenge, we introduce ISR-LLM, a novel framework that improves LLM-based planning through an iterative self-refinement process. The framework operates through three sequential steps: preprocessing, planning, and iterative self-refinement. During preprocessing, an LLM translator is employed to convert natural language input into a Planning Domain Definition Language (PDDL) formulation. In the planning phase, an LLM planner formulates an initial plan, which is then assessed and refined in the iterative self-refinement step by a validator. We examine the performance of ISR-LLM across three distinct planning domains. Our experimental results show that ISR-LLM is able to achieve markedly higher success rates in sequential task planning compared to state-of-the-art LLM-based planners. Moreover, it also preserves the broad applicability and generalizability of working with natural language instructions.

This article studies the problem of the resilient cruise control in heterogeneous vehicle platoons against f-local Byzantine attacks (BAs). Agents under BAs become traitors of the swarm, who try to mislead its neighbors while adopting wrong inputs. Thus, BAs are extremely challenging to be suppressed. This study introduces a novel hierarchical protocol characterized by a virtual twin layer (TL), motivated by the rationale of digital twin. This protocol separates the defense scheme against f-local BAs into two parts: one defense scheme against Byzantine edge attacks (BEAs) via the TL and another scheme against Byzantine node attacks (BNAs) via the cyber-physical layer (CPL). The TL employs a trusted-edge strategy, enhancing the network resilience by incorporating a minimal fraction of the key edges. It is rigorously proven that a TL topology meeting strong -robustness is sufficient for achieving distributed resilient estimation against BEAs. On the CPL, a series of decentralized chattering-free controllers is proposed, guaranteeing the resilient cruise tracking of heterogeneous platoons against exponentially unbounded BNAs. Besides, these controllers can achieve uniformly ultimately bounded convergence. The theoretical results' effectiveness and practicality are validated through a numerical simulation example and an unmanned ground vehicle experiment involving heterogeneous platoons against f-local BAs.

This work solves the countermeasure design problems of distributed resilient output time-varying formation-tracking (TVFT) of heterogeneous multiagent systems (MASs) against general Byzantine attacks (GBAs). Inspired by the concept of Digital Twin, a hierarchical protocol equipped with a twin layer (TL) is proposed, which decouples the above problem into the defense against Byzantine edge attacks (BEAs) on the TL and the defense against Byzantine node attacks (BNAs) on the cyber-physical layer (CPL). First, a secure TL with respect to (w.r.t.) the high-order leader dynamics is designed, which achieves resilient estimation against BEAs. A trusted-node strategy against BEAs is proposed, which promotes network resilience by protecting almost the smallest fraction of crucial nodes on the TL. It is proven that strongly (2f+1) -robustness w.r.t. the above trusted nodes is sufficient for the resilient estimation performance of the TL. Second, a decentralized adaptive and chattering-free controller against potentially unbounded BNAs is designed on the CPL. This controller has the merit of uniformly ultimately bounded (UUB) convergence and an assignable exponential decay rate when converging into the above UUB bound. To the best of our knowledge, this article is the first to achieve resilient output TVFT against GBAs, rather than under GBAs. Finally, the practicability and validity of this new hierarchical protocol are illustrated via a simulation example.

This article deals with the problem of multiple-event-triggered dissipative tracking control for nonlinear Markov jump systems with incomplete transition probabilities. An interval type-2 fuzzy model with partially known transition probability matrix is used to capture the underlying nonlinearities and a hidden Markov model with an incomplete conditional probability matrix is employed to describe the possible asynchronous phenomenon between the plant and the tracking controller. A multiple-event-triggered methodology involving two adaptive event-triggered schemes for the actuator channel and the sensor channel is proposed. By using the Lyapunov and dissipativity theory, sufficient conditions for the desired tracking controller are established in terms of linear matrix inequalities. Last, two examples, involving one numerical and one practical model named the Hénon system, are utilized to show the effectiveness of the proposed tracking control algorithm.

Recommendation systems rely heavily on behavioural and preferential data (e.g., ratings and likes) of a user to produce accurate recommendations. However, such unethical data aggregation and analytical practices of Service Providers (SP) causes privacy concerns among users. Local differential privacy (LDP) based perturbation mechanisms address this concern by adding noise to users’ data at the user-side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in recommendation accuracy. We propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG) to address this problem. The LDP perturbation mechanism, i.e., Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy$\varepsilon$-LDP. We use the MoG model at the SP to estimate the noise added locally to the ratings and the MF algorithm to predict missing ratings. Our LDP based recommendation system improves the predictive accuracy without violating LDP principles. We demonstrate that our method offers a substantial increase in recommendation accuracy under a strong privacy guarantee through empirical evaluations on three real-world datasets, i.e., Movielens, Libimseti and Jester.

This article studies the resilient formation tracking control problem for spacecraft swarm while considering actuation attacks and input saturation. A distributed Lyapunov-based model predictive controller (DLMPC) framework is designed for spacecraft swarm to track the target trajectory in a preset formation shape and achieve attitude consensus. To ensure formation safety, a collision avoidance term is introduced into the DLMPC framework. To guarantee the feasibility and stability of the DLMPC, we first construct the Lyapunov-based adaptive auxiliary controller and then use its stability to construct the stability constraint. The DLMPC inherits the characteristic of the Lyapunov-based adaptive auxiliary controller and employs online optimization to guarantee better formation tracking performance. As a novel framework for spacecraft formation control, the proposed DLMPC has the advantage of improving the formation tracking performance through persistently online optimization, especially, in adversarial dynamic environments. The simulation results validate the superiority and resilience of the DLMPC, and the proposed DLMPC framework shows improvement in formation tracking performance.

This work focuses on the stability analysis of positive Takagi-Sugeno (T-S) fuzzy descriptor systems with time-varying delays. An equivalent augmented system is constructed to investigate the positivity and stability of T-S fuzzy descriptor time-delay systems. By using this transformed system, a necessary and sufficient positivity condition is first derived for systems, which can be verified by linear programming (LP). Then, based on the positivity of T-S fuzzy descriptor systems, a sufficient condition is put forward for the asymptotic stability of systems with bounded and unbounded time-varying delays. Finally, several examples are provided to show the effectiveness of the obtained results.

This article investigates the nonnegative consensus tracking problem for networked systems with a distributed static output-feedback (SOF) control protocol. The distributed SOF controller design for networked systems presents a more challenging issue compared with the distributed state-feedback controller design. The agents are described by multi-input multi-output (MIMO) positive dynamic systems which may contain uncertain parameters, and the interconnection among the followers is modeled using an undirected connected communication graph. By employing positive systems theory, a series of necessary and sufficient conditions governing the consensus of the nominal, as well as uncertain, networked positive systems, is developed. Semidefinite programming consensus design approaches are proposed for the convergence rate optimization of MIMO agents. In addition, by exploiting the positivity characteristic of the systems, a linear-programming-based design approach is also proposed for the convergence rate optimization of single-input multi-output (SIMO) agents. The proposed approaches and the corresponding theoretical results are validated by case studies.

This article considers the problem of designing lead, lag, and lead-lag compensators for multi-input-multi-output (MIMO) linear systems under the H∞performance measure. This is the first time that the lead, lag and lead-lag compensators are generalized to the MIMO cases by preserving their classical compensator structures. Theoretical results on the stability analysis and synthesis of MIMO systems under the H∞control performance with the proposed lead, lag, and lead-lag compensators are obtained. Then relevant algorithms for designing lead, lag, and lead-lag compensators are provided to determine the compensator parameters. Differing from traditional design methods, which mostly rely on some trial-and-error procedures, the proposed methods are algorithmic and the compensators can be synthesized systematically. Illustrative examples are used to demonstrate the effectiveness and advantages of the proposed methods.

This paper considers the deployment of a hybrid wireless data/power access point in an 802.11- based wireless powered IoT network. The proportionally fair allocation of throughputs across IoT nodes is considered under the constraints of energy neutrality and CPU capability for each device. The joint optimization of wireless powering and data communication resources takes the CSMA/CA random channel access features, e.g. the backoff procedure, collisions, protocol overhead into account. Numerical results show that the optimized solution can effectively balance individual throughput across nodes, and meanwhile proportionally maximize the overall sum throughput under energy constraints.

This paper is concerned with the consensus of multiagent systems with nonlinear dynamics through the use of aperiodic sampled-data controllers, which are more flexible than classical periodic sampled-data controllers. By input delay approach, the resulting sampled-data system is reformulated as a continuous system with time-varying delay in the control input. A continuous Lyapunov functional, which captures the information on sampling pattern, together with the free-weighting matrix method, is then used to establish a sufficient condition for consensusability. For a more general case that the sampled-data controllers are subject to constant input delays, a novel discontinuous Lyapunov functional is introduced on the basis of the vector extension of Wirtinger's inequality. This functional can lead to simplified and efficient stability conditions for computation and optimization. Further results on the estimate of maximal allowable sampling interval upper bound is given as well. Numerical example is provided to show the effectiveness and merits of the proposed protocol.

This paper is concerned with the transient and steady-state estimates of a class of genetic regulatory networks (GRNs). Some sufficient conditions, which do not only present the transient estimate but also provide the estimates of decay rate and decay coefficient of the GRN with interval parameter uncertainties (interval GRN), are established by means of linear matrix inequality (LMI) and Lyapunov-Krasovskii functional. Moreover, the steady-state estimate of the proposed GRN model is also investigated. Furthermore, it is well known that gene regulation is an intrinsically noisy process due to intracellular and extracellular noise perturbations and environmental fluctuations. Then, by utilizing stochastic differential equation theory, the obtained results are extended to the case with noise perturbations due to natural random fluctuations. All the conditions are expressed within the framework of LMIs, which can easily be computed by using standard numerical software. A three-gene network is provided to illustrate the effectiveness of the theoretical results.