It is crucial to improve the robustness of the IoT against malicious attacks. For scenarios where the network topology is split due to malicious attacks, but can still reconnect fragmented networks by adjusting links between nodes, existing algorithms offer limited robustness improvements, exhibit high computational complexity, incur significant energy consumption, and produce optimized network topologies lacking scalability. To address these issues, we propose a network robustness optimization algorithm called the ’Recovery Algorithm’. Based on this algorithm, the IoT can automatically adjust its topology to effectively resist malicious attacks. Compared with other algorithms, the optimized network of this algorithm possesses higher robustness and higher communication efficiency without affecting the scalability of the original network. Furthermore, the computational complexity and energy consumption of this algorithm are several orders of magnitude lower than existing algorithms.

Federated learning (FL) has emerged as a promising technique in the Industrial Internet of Things (IIoT) by enabling distributed devices to collaboratively train models without sharing raw data. In FL, ensuring data privacy and secure authentication becomes essential due to the sensitivity of industrial data and the potential for adversarial attacks. This paper highlights a security flaw in a recently proposed FL authentication protocol designed for IIoT environments. Specifically, the scheme is analyzed to be susceptible to public-key replacement attacks. We propose a secure, lightweight authentication scheme for privacy-enhanced federated learning (SLAPE) to address vulnerabilities in participant registration, group key distribution, local data training, and aggregation processes. SLAPE leverages the Elliptic Curve Cryptography with the Chinese Remainder Theorem to support malicious group member traceability, revocation of compromised identities, and efficient batch verification of multiple messages. It effectively resists Type-I attacks that previous schemes could not, while also incorporating forward and backward security essential for IIoT applications. We rigorously demonstrate SLAPE’s resilience against prevalent threats through both formal and informal analyses. Our evaluation results indicate that SLAPE demonstrably enhances the security and privacy of existing schemes, with improvements in computational efficiency for both proof generation and verification, while keeping communication overhead relatively low.

In this paper, we analyze a recent algorithm for resilient consensus control in distributed multi-agent systems. While effective in theory, its reliance on security in communication and strong connectivity assumptions limits its practicality in dynamic electronic and cyber-physical systems, such as embedded device networks and uncrewed platforms. To address these limitations, we propose a State-repairing Multi-agent Resilient Consensus (SMARC) scheme to eliminate the need for normal agents to collect trustworthy state values from a fixed bounded threshold of neighbors to achieve reliable consensus. The core innovation of SMARC is a decentralized state-repair mechanism, which enables agents to obtain additional information from their reachable sets to repair unavailable or corrupted state values of malicious or faulty agents, and autonomously adjust their convergence speed. Additionally, without negatively impacting the consensus performance, SMARC employs a noise-masked surface state to protect the initial states of agents from eavesdropping. This approach avoids extra storage and reduces computations without adhering to strict security prerequisites, making it more suitable for resource-constrained electronic systems compared to existing methods. Theoretical convergence and security proofs demonstrate that SMARC can successfully resist passive attacks while ensuring accurate convergence on multi-dimensional data. Most importantly, from small- to large-scale networks, SMARC achieves a three- to four-fold increase in convergence speed compared to the most competitive recent state-of-the-art resilient consensus algorithm in both passive and active attacks. A prototype electronic of a MAS was also built using six Raspberry Pi devices to validate its performance and robustness in practical environments.

For space missions such as deep space exploration and on-orbit operations, a high-precision 3D model of the target is a prerequisite for achieving autonomous navigation and precise manipulation. However, natural uncontrolled orbits impose strong geometry constraints and require long observation periods, while active orbital maneuvering accelerates data acquisition but increases fuel consumption and reduces mission endurance. This trade-off between maneuvering efficiency and observation completeness has become a bottleneck limiting spacecraft operations on-orbit. To address these challenges, this paper proposes a sensing-planning framework that integrates active observation with orbital maneuvering. First, a 3D reconstruction scheme based on 2D Gaussian splatting (2DGS) is designed, taking uncertainty into account. Next, the optimal observation views are estimated using Bayesian theory, followed by orbit selection combined with fuel consumption and observation time derived from orbital mechanics. Simultaneously, discrete point filtering is applied to improve the reconstruction quality of the 3D mesh in the space environment. Finally, the effectiveness of the proposed method is validated through simulations and experimental comparisons with state-of-the-art (SOTA) in a newly constructed multi-orbital observation space environment darkroom. Code and data are available at: https://github.com/YD-96/Active-2DGS and https://bhpan.buaa.edu.cn/link/AAA6508AF1B8714EF0B91A992489F2228F.

This article proposes a novel discounted inverse reinforcement learning (DIRL) algorithm for linear quadratic (LQ) control of unknown continuous-time (CT) systems with partially observable states and an unknown discounted value function. Existing DIRL methods predominantly rely on full-state feedback, limiting their applicability to practical scenarios where only input-output data are available. To this end, a state reconstruction method is designed for the system controlled by an expert using the measured desired output. Based on this, a model-free output-feedback (OPFB) DIRL algorithm is presented to iteratively solve the unknown value function and the corresponding optimal OPFB control policy equivalent to the expert control policy. The convergence of the proposed algorithm and the nonuniqueness of solutions are rigorously analyzed. Finally, comprehensive simulations reveal the effectiveness of the proposed algorithm in recovering the expert control policy and its superior computational efficiency compared to state-of-the-art (SOTA) methods.

This article develops a dual-link coded event-triggered control (DL-CEC) for consensus in nonlinear multiagent system. To reduce the communication burden of signal transmission between the control box and actuator box or among agents and to enhance the security of information exchange, a dual-link coded scheme is proposed to compress each transmitted information into an L-length string. Furthermore, since the intrinsic complexity of nonlinear systems often causes traditional prescribed performance methods to fail in meeting constraints during the initial stages of operation, an adaptive prescribed performance (APP) scheme is introduced. By utilizing auxiliary functions, the APP is capable of dynamically adjusting performance boundaries, enabling seamless adaptation to varying initial system conditions. As a result, it ensures the tracking error is rigorously guaranteed to remain within a user-defined range over a prescribed time horizon, effectively accommodating diverse initial conditions of the system. By integrating DL-CEC with the APP method, the proposed control strategy ensures bounded consensus tracking with reduced communication cost and prescribed-time performance under arbitrary initial conditions. Simulation experiments corroborate the effectiveness and feasibility of the proposed approach.

Accurate segmentation of multiple on-orbit spacecraft remains difficult in deep-space imagery because the scenes contain large uniform backgrounds, fine structural details, and limited labeled data. To address this problem, we propose SpaceSeg, a segmentation framework that adapts a vision foundation model to the spacecraft domain. The framework introduces a Multi-Scale Hierarchical Attention Refinement Decoder (MSHARD) to improve cross-scale feature decoding, a Spatial Domain Adaptation Transform (SDAT) training strategy to improve robustness to representative space-imaging disturbances, and a task-oriented objective that jointly optimizes segmentation accuracy and IoU-prediction quality. A lightweight connected-component-analysis module is also integrated into the pipeline for instance-aware target organization in multi-spacecraft scenes. We further construct SpaceES, a multi-scale on-orbit multi-spacecraft semantic segmentation dataset covering four space backgrounds and 17 spacecraft types. On SpaceES, SpaceSeg achieves 89.87% mIoU and 99.98% mAcc, setting a new state of the art among all evaluated baselines, surpassing the strongest competing method by 1.38 percentage points in mIoU with 59.6% fewer parameters, and exceeding the vanilla SAM2 baseline by 5.71 percentage points. Hardware-in-the-loop simulation and real satellite-to-satellite imagery experiments further support the practical relevance of the proposed method. Dataset and code are publicly available at https://github.com/Akibaru/SpaceSeg.

Cheeger’s inequality is used to estimate a lower bound on the number of edges that need to be cut off to split a regular graph into two subgraphs with the same number of nodes. Therefore, we design a network topology that is robust under malicious attacks based on Cheeger’s inequality. Considering that Cheeger’s inequality cannot be directly used to optimize complex networks in reality, we generalize the conclusion of Cheeger’s inequality and use this conclusion to propose the Good Regular Graph algorithm (GRG) for generating strongly robust network topologies under malicious attacks. In addition, we explain the design principle of the algorithm from a graph-theoretic perspective and give the corresponding network topology expansion algorithm. GRG, after appropriate processing, can be applied to optimize the substructures of a wide range of real-world network topologies to enhance their robustness. Experiments show that the algorithms proposed in this research perform better compared to other algorithms. We prove from both theoretical and experimental perspectives that Cheeger’s inequality can serve as a basis for designing robust network topologies under malicious attacks.

Serving as a critical component in laser pointing systems, fast steering mirrors (FSMs) encounter various control challenges in precise tracking tasks. In response to these challenges, a disturbance-rejection model predictive control (DR-MPC) method is proposed in this work. Initially, an auxiliary state space model of the tracking error is formulated by exploiting the minimal polynomials of the reference and disturbance signals. Subsequently, an implicitly constrained optimization problem is constructed, eliminating the need for explicit modeling of the unmeasurable disturbance. The minimal polynomial framework enables the transformation of the original problem into an explicit constrained optimization problem by linking past and predicted control inputs. Finally, the coordinate descent method is employed to iteratively solve the constrained optimization problem, enabling the successful deployment of the proposed controller on 10kHz high-speed hardware. Experimental results indicate that the proposed DR-MPC method exhibits substantial advantages regarding disturbance rejection, constraint handling, and dynamic tracking.

Performing target tracking and surveillance in dynamic obstacle environments requires maintaining continuous visual focus on the target while ensuring collision avoidance. This paper presents a safety-critical tracking control method that ensures dynamic obstacles remain outside the camera’s line of sight while simultaneously avoiding collisions between the chaser vehicle and obstacles. A novel real-time occlusion detection function is developed, and motion constraints are systematically integrated using a hybrid framework combining the artificial potential field (APF) method with an observer-based control strategy. To address temporal-sensitive tasks, a prescribed time controller (PTC) based on time-scale transformation technique has been proposed. Furthermore, a prescribed time linear extended state observer (PTESO) is proposed, featuring a simplified structure to enable rapid and accurate estimation of unknown environmental disturbances and non-linear terms. Finally, the effectiveness of the proposed method was verified via simulation in a simplified physical scenario.

Real-world applications of nonlinear systems tracking control are always challenging due to the existence of uncertainties and disturbances. To design a robust optimal tracking controller for uncertain nonlinear systems with disturbances and actuator saturation, this paper investigates the prescribed performance robust optimal tracking control problem. A prescribed performance mechanism is constructed to convert the dynamics of tracking error into transformed error dynamics, which keeps the system’s operating states within specific bounds, ensuring tracking with predefined error constraints. For the optimal tracking controller design, an optimal index is established to optimize the performance of tracking control, and a robust optimal index is established to optimize the disturbance effect on the tracking error. To achieve robust optimal tracking control that minimizes both optimal and robust optimal indexes, a Stackelberg game is constructed, which provides a hierarchical game structure for the optimal controller and the worst disturbance. The robust optimal controller is approximated online using reinforcement learning techniques. An actor-critic-identifier algorithm is designed to approximate the optimal value function, optimal controller, and drifted system parameters. Lyapunov theory is utilized to analyze the closed-loop system’s stability. To demonstrate the effectiveness of the proposed robust optimal control method, two numerical simulations and a hardware experiment on a quadcopter system are conducted. The experiment results demonstrate that our method successfully achieves prescribed performance tracking control when actuators are saturated and disturbances are present. Note to Practitioners—In this paper, the probelm of mixed$H_{2}/H_{\infty }$prescribed-performance optimal tracking control for nonlinear systems with input saturation is investigated. To constrain the operating states of the system within certain bounds, the prescribed performance transformation is designed to achieve tracking with predefined error constraints. For the optimal controller design, the$H_{2}$index is established to minimize the optimal tracking performance, and the$H_{\infty }$index is designed to minimize the disturbance effect on the tracking error. A Stackelberg-based non-zero sum game between the optimal controller and the worst disturbance is established to design the mixed$H_{2}/H_{\infty }$optimal tracking controller. The designed optimal controller is approximated online using reinforcement learning. Effectiveness of the proposed method is demonstrated by two numerical simulations and a hardware experiment on a quadcopter system. Based on the proposed high-performance controller, engineers can design a high-performance robust optimal tracking controller for uncertain nonlinear systems with extreme conditions of disturbances and actuator saturation.

In asteroid exploration and orbital servicing missions with space robots, accurate 3D structural of the target is typically relied upon for planning landing trajectories and controlling movements. Unlike conventional neural radiance fields (NeRF) studies, which rely on full-view random sampling of targets that can be easily achieved on the ground, spacecraft operations present unique challenges due to the kinematic orbit constraint, the high cost of controlled motion, and limited fuel reserves. This results in limited observation of space targets. In order to obtain 3D structure under close-flybys and restricted observation, we proposed Uncertainty Neural Surfaces (UNS) model based on Bayesian uncertainty estimation. UNS enhance the precision of reconstructed target surfaces under constrained-views, providing guidance for subsequent imaging view design. Specifically, UNS introduces Bayesian estimation based surface uncertainty on neural implicit surfaces. The estimation is calculated based on the degree of self-occlusion of the target and the difference between rendered and actual colors. This approach enables uncertain estimation of 3D space and arbitrary view. Finally, extensive systematic evaluations and analyses of spacecraft model sampling in a local darkroom validate the sophistication of UNS in uncertainty estimation and surface reconstruction quality. Code is available athttps://github.com/YD-96/UNS.

This brief proposes an optimized time-synchronized control method based on reinforcement learning for the bipartite consensus tracking problem. The study considers multiagent system comprising leaders and followers, where followers interact through signed directed graphs. Some agents track the leader's state, while others converge to its opposite value. The proposed method employs a time-synchronized sliding mode control framework to ensure fixed-time bipartite consensus among agents with signed interaction topology. Reinforcement learning is integrated to optimize the control process, wherein an actor-critic architecture is utilized to minimize the Bellman residual, enabling optimal control performance. Theoretical analysis proves the fixed-time convergence and Bellman optimality of the system, with the upper bound of convergence time explicitly determined by controller parameters. Simulation experiments validate the effectiveness of the proposed method: all followers simultaneously achieve bipartite consensus within a fixed time, while reinforcement learning significantly and adaptively optimizes the control process.

Linear quadratic control with unknown value functions and dynamics is extremely challenging, and most of the existing studies have focused on the regulation problem, incapable of dealing with the tracking problem. To solve both linear quadratic regulation and tracking problems for continuous-time systems with unknown value functions, this article develops a discounted inverse reinforcement learning (DIRL) method that inherits the model-independent property of reinforcement learning (RL). More specifically, we first formulate a standard paradigm for solving linear quadratic control using DIRL. To recover the value function and the target control gain, an error metric is elaborately constructed, and a quasi-Newton algorithm is adopted to minimize it. Furthermore, three DIRL algorithms, including model-based, model-free off-policy, and model-free on-policy algorithms, are proposed. The latter two rely on the expert's demonstration data or the online observed data, requiring no prior knowledge of the system dynamics and value function. The stability, convergence, and existence conditions of multiple solutions are thoroughly analyzed. Finally, numerical simulations demonstrate the effectiveness of the theoretical results.

Addressing blockchain's insufficient throughput and scalability is imperative for practical viability. Off-chain approaches, such as state channels (including Hash Time Lock Contract (HTLC), virtual channels), demonstrate enhanced throughput by enabling parallel transaction processing. While virtual channels introduce execution complexity, HTLC suffers from high update delays. Moreover, existing methods face network attacks. We present Sharding-based Hybrid State Channel (SharHSC) to address these issues. First, we introduce a novel off-chain sharding architecture, which partitions proxy nodes into multiple shards. Thus, when the off-chain node count increases, adding shards enhances system throughput. Second, each shard establishes a supervisory committee to record latest channel statuses to ensure accurate fund distribution upon channel closure. Third, we combine the strengths of HTLC and virtual channels. In particular, SharHSC constructs a single virtual channel across all the nodes involved in the payment by treating the nodes between payer and payee as an intermediate entity, which utilizes HTLC for fund routing. This realizes both low latency and streamlined complexity. Finally, our work is substantiated by security analysis and experiments. As the node number varies, compared with HTLC and virtual channels, the latency is reduced by 49.32% and 31.82%, and the throughput is increased by 8.93 and 1.89 times.

In Web 3.0’s pursuit of a decentralized and user-autonomous network, traditional access control methods, such as central servers and weak decentralized algorithms, are insufficient regarding security, fault tolerance ability, and scalability. To solve this, we first design a decentralized multi-committee attribute-based encryption, X-ABE, to address the weak decentralization and low fault tolerance in Multi-Authority Attribute-Based Encryption (MA-ABE). X-ABE replaces MA-ABE’s fragile attribute authorities with robust attribute committees, each composed of multiple nodes. By developing dual-wrapped shares techniques, we address the increased dimensionality challenge of secret sharing while maintaining only 1 distributed key generation instance. Also, a formal security definition and proof under the partial adaptive model are given using dual system encryption. Second, X-LOCK, an X-ABE based decentralized access control utilizing consensus plus sharding, is proposed for Web 3.0, to achieve full decentralization, consistency, fault tolerance, user autonomy, and scalability. Third, X-ABE-R is proposed for attribute revocation and is demonstrated in X-LOCK-R with sharding blockchain as an immutable revocation ledger. Fourth, a formal definition and comparative analysis of X-ABE’s fault tolerance abilities are demonstrated, covering aspects of liveness and safety, along with the complexity analysis. Fifth, practical evaluations are conducted, demonstrating that while improving fault tolerance, the overhead remains acceptable.

Point set registration is an essential technique in the field of machine vision. In this article, we propose a robust global optimal solution to for the point set registration of feature points extracted from visual images, used in remote (300–120 km) space target tracking and targeting tasks. Specifically, we begin with cases where correspondences among point sets are known, establishing a cost function centered on maximizing the consensus set, wherein rotational and translational parameters are determined using voting methods and the branch-and-bound (BnB) algorithm, respectively. We then adapt this foundation to tackle the more challenging scenario of unknown correspondences in simultaneous pose and correspondence registration by adjusting the cost function and BnB bounding functions, supplemented with nested iterations to accurately determine rotation and translation parameters. Finally, the comprehensive experimental comparisons executed across synthetic and real datasets, along with ground-based spacecraft pose measurement setup, illustrate that, compared to existing methods, our proposed approach achieves precise estimations under the influence of noise and outliers. Moreover, compared to the globally nested BnB scheme, our method reduces computational complexity and enhances solution speeds.

This article solves the entry capture problem (ECP) such that for any initial tracking error, it can be regulated into the prescribed performance constraints within a user-given time. The challenge lies in how to remove the initial condition limitation and to handle the ECP for nonlinear systems under unknown control directions and asymmetric performance constraints. For better tracking performance, we propose a unified tunnel prescribed performance (TPP) providing strict and tight allowable set. With the aid of a scaling function, error self-tuning functions (ESFs) are then developed to make the control scheme suitable to any initial condition (including the initial constraint violation), where the initial values of ESFs always satisfy performance constraints. In lieu of the Nussbaum technique, an orientation function is introduced to deal with unknown control directions while such way is capable of reducing the control peaking problem. Using ESFs, together with TPP and an orientation function, the resulted tunnel prescribed control (TPC) leads to a solution for the underlying ECP, which also exhibits a low complexity level since no command filters or dynamic surface control is required. Finally, simulation results are provided to further demonstrate these theoretical findings.

This work develops an output feedback adaptive tracking control method based on dynamic regressor extension and mixing (DREM) for discrete-time uncertain parameter systems. A piecewise DREM estimator is designed for the uncertain parameters under conditions strictly weaker than the persistently excited condition, exhibiting the ability to capture the actual system dynamics in finite time. Accurate parameter estimation guarantees the performance of the controller utilizing the DREM estimator. Then, an adaptive optimal controller for any given reference trajectory is designed within the framework of receding horizon control. The system state and control input are theoretically guaranteed to remain bounded during tracking. The adaptive controller is restructured in a nonminimal state space to achieve output feedback without a state estimator. The proposed output feedback adaptive controller is fully consistent with its state-feedback counterpart. Simulation results for tracking different reference signals demonstrate the efficacy of the proposed strategy.

The DC link capacitor discharge in the event of a DC fault is rapid and contains high frequencies. This rapid discharge of high current and frequency interface with DC bus, Voltage Source Converter (VSC), and AC source. This interface damages the equipment and possibly living beings in proximity. Therefore, it is necessary to develop a topology to detect and identify fault types promptly, along with the isolation and restoration of the system. This study introduces an improved novel fault detection technique using the Highpass Chebyshev type 2 filter due to its flatter pass-band response. Further, the polarities of peaks obtained from the fault detection calculation are used again for another proposed novel fault location method. It identifies fault types to locate and isolate the faulty system using only polarities of amplitude response peaks. In the simulation, both methods were fast and accurate in detecting, locating, and identifying appropriate fault types only by High Pass Filter (HPF) amplitude response peaks and their polarities.

The inherent massive heterogeneous devices and open channels in the Internet of Things (IoT) present significant challenges for identity authentication between devices and cloud servers. For this issue, reliable protocols ensure the legality of participants and act as a crucial method to provide security for authentication. In previous research, schemes devised by researchers exhibit certain security vulnerabilities, making it challenging to withstand comprehensive network attacks, e.g., stolen device attacks, replay attacks, impersonation, etc. Additionally, some protocols have complex interaction processes, which incur significant computational redundancy and resource loss. Motivated by this, this article proposes an anonymous and certificateless lightweight authentication protocol (ACLAP) for device-to-server and device-to-device based on elliptic curve cryptography. It improves the communication quality between devices and cloud servers and solves the security risks in authentication. In the scheme, we utilize device users’ passwords and biometric features as verification credentials without storing any trusted proofs on the cloud server. We address the issue of resource consumption caused by numerous devices in the IoT environment. From formal security analysis and comparisons with other works, our protocol has preferable security performance and effectively saves communication resources for authentication. Simulation results demonstrate the feasibility and practical significance of the scheme.

Accurate and fast motion prediction such as pedestrian motion prediction (PMP) is crucial for safe autonomous driving. Much research effort has been devoted to studying the reactive behaviors of pedestrians, such as the interaction between pedestrians or the interaction between pedestrians and the environment. However, compared with behavioral logic, the current motion state of pedestrians has a greater influence on the future trajectory. In this work, we propose a motion logic network (MLN) to improve both the accuracy and efficiency of pedestrian motion prediction. Compared with the traditional data-driven neural networks, the concept of motion logic is directly introduced into the network so that the training of the network is not required. In particular, instead of fitting the network based on inputs and target outputs, the proposed method directly adopts motion logic to predict the future trajectory. To illustrate the performance of the proposed MLN, several experiments have been performed. For acceleration and deceleration motion in practical experiment, the average displacement error (ADE) of MLN has an improvement of as high as 6.25% than CVM’s, while the final displacement error (FDE) of MLN has an improvement of 11.1%. In terms of efficiency, MLN is a hundred times faster than LSTM. It indicates that motion logic plays an important role in the development of prediction algorithms for pedestrian motion.Note to Practitioners—This article is motivated by the effects of pedestrians’ different motion states on the future trajectory. For example, the pedestrians’ state may also change drastically in some situations such as dashing across the road or stopping in anticipation of a bicycle crossing the path. This work explores the use of pedestrians’ physical motion states to develop a network that emphasizes the logical features of pedestrian motion so as to directly estimate the future motion trajectory and trend based on the motion logic. For the first time, the concept of motion logic and the resultant training-free motion logic network (MLN) is proposed, which takes into account the accuracy and efficiency performance of the algorithm. Compared with the state-of-the-art pedestrian prediction methods, the proposed method can better predict the trajectory of pedestrians in more complex motion states. Moreover, a series of simulations and practical experiments with pseudo-constant velocity motion and acceleration/deceleration motion was taken to verify the performance of the proposed MLN.

In this article, saturation-tolerant prescribed control (SPC) is investigated for a class of multiinput-multioutput (MIMO) nonlinear systems. The key challenge lies in how to guarantee both input and performance constraints simultaneously for nonlinear systems especially under external disturbance and unknown control directions. We propose concise finite-time tunnel prescribed performance (FTPP) for better tracking performance, which features tight allowable set and user-specified settling time. To comprehensively tackle the conflict between the above two constraints, an auxiliary system is designed to explore their interconnections instead of neglecting their contradictions. By introducing its generated signals into FTPP, the obtained saturation-tolerant prescribed performance (SPP) has the ability to degrade or recover the performance boundaries in the light of different saturation conditions. Consequently, the developed SPC, together with nonlinear disturbance observer (NDO), can effectively improve the robustness and reduce the conservatism against external disturbances, input, and performance constraints. Finally, comparative simulations are presented to showcase these theoretical findings.

This article introduces a novel approach called terminal sliding-mode control for achieving time-synchronized convergence in multi-input-multi-output (MIMO) systems under disturbances. To enhance controller design, the systems are categorized into two groups: 1) input-dimension-dominant and 2) state-dimension-dominant, based on signal dimensions and their potential for achieving thorough time-synchronized convergence. We explore sufficient Lyapunov conditions using terminal sliding-mode designs and develop adaptive controllers for the input-dimension-dominant case. To handle perturbations, we design a multivariable disturbance observer with a super-twisting structure, which is integrated into the controller. By utilizing the sliding-mode technique and the disturbance observer, the proposed controller ensures simultaneous convergence of all output dimensions. In the state-dimension-dominant case, where a full-rank system matrix is absent, only specific output elements converge to equilibrium simultaneously. We conduct comparative simulations on a practical system to highlight the effectiveness of our proposed method for the input-dimension-dominant case. Statistical results reveal the benefits of shorter output trajectories and reduced energy consumption. For the state-dimension-dominant case, we present numerical examples to validate the semi-time-synchronized property.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

Due to the promising scalability property, sharding technology has gained widespread attention. It improves the transaction throughput of blockchain systems but also introduces cross-shard transactions. Current two-phase commit (2PC) protocols process different cross-shard transactions sequentially, resulting in significant system overhead and low throughput. Besides, current sharding blockchains rely on Byzantine fault tolerance (BFT) as a black box, lacking specific designs to efficiently handle cross-shard proposals. Moreover, cross-shard communication complexity is high, and transaction processing parallelism is low. In this paper, we first propose P-2PC, a general framework to process cross-shard transactions of different phases in a pipelined way, suitable for most sharding blockchains. Further, we design Cherubim with improved quadruple 2PC, 4P-2PC. By combining P-2PC with an intra-shard pipelined BFT, 4P-2PC achieves both intra-shard and cross-shard pipelined processing. Combined with a newly designed batch processing method, each shard processes 4 transaction batches simultaneously through 1 round of calculation and communication, compared to 4 rounds in previous work. In particular, Cherubim seamlessly integrates a multi-signature algorithm supporting further aggregation, reducing communication complexity. Furthermore, we evaluate our work through theoretical analysis and implementation, proving that Cherubim has a communication complexity linear to the node number. We also propose horizontal and vertical consensus parallelism degrees to evaluate the parallelism ability. Compared to the state-of-the-art solutions, the evaluation demonstrates that Cherubim achieves a transaction throughput improvement of at least 2.28×.

In voltage source converter-based low-voltage dc systems (LVDC), the fault current of the dc-link capacitor is considerably high and destructive to system infrastructure. It is necessary to develop effective fault detection methods with sufficient sensitivity and accuracy. To achieve these targets, a complex domain analysis-based fault detection method is proposed in this article. Specifically, the proposed method first fits the transient current into a linear combination of exponential functions, which is solved in the Z-domain-based on the Padé approximation. Second, exponents of the fitted function are projected into the complex plane. A state circle centered on the origin is defined on the complex plane to detect dc faults according to the position of projection points relative to the state circle. The proposed method can differentiate several typical situations via theoretical analysis, including fault line transients, healthy line transients, and load switching. The performance of proposed method is validated with an experimental multiterminal LVDC system to reveal its effective performance compared with present frequency domain based methods, including the wavelet transform, the short-time Fourier transform, the S transform, and the Hilbert–Huang transform.

This paper studies synchronized optimization control for the cooperatively connected autonomous vehicle platoon formulation applicable in various driving scenarios and accommodates multiple vehicles dynamically entering or exiting the platoon. More specifically, the proposed algorithm consists of bi-layered synchronized optimization that enables the ultimate optimized control to attain the synchronized convergence property, and also importantly, ensures the satisfaction of safety performance requirements. The first layer of the proposed approach involves formulating the platoon dynamics and ensuring that the platoon operates within safe boundaries while optimizing its overall performance. To achieve this, the prescribed performance control is utilized to ensure that the state-variables remain within a predefined region throughout the synchronized optimization process. In the second layer, the control optimization takes into account the vehicle dynamics and actuators of either heterogeneous or homogeneous individual vehicles, improving performance and coordination within the platoon. In each optimization layer, the optimized backstepping is utilized, and the norm-normalized sign function is appropriately incorporated with the decomposition design to establish the learning framework with the outcome that attains the synchronized properties simultaneously. The adaptive dynamic programming and gradient-constrained method are utilized in the learning design to iteratively optimize system control while keeping the learning parts within the admissible policy region. Importantly, it is rigorously shown that this particular development and methodology attains the noteworthy time-synchronized stability property and outcome that all vehicle agents arrive at the desired relative position at the same time with synchronized convergence. Additionally, it is also shown that the methodology of our specific algorithmic strategy significantly also attains the desired outcomes of “string stability” (jointly with the above-mentioned desired outcomes of “time-synchronized stability”). To evaluate its effectiveness, comparative studies with different methods are carried out to showcase the significantly better desired outcomes attained with this methodology of synchronized optimization. Further evaluations in scenarios involving dynamic entry and exit of multiple vehicles demonstrate the corresponding capability and effectiveness in achieving the desired objectives.

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton-Jacobi-Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

Under the persistent excitation (PE) condition, the real dynamics of the nonlinear system can be obtained through the deterministic learning-based radial basis function neural network (RBFNN) control. However, in this scheme, the learning speed and accuracy are limited by the tradeoff between the PE levels and the approximation capabilities of the neural network (NN). Inspired by the frequency domain phase compensation of linear time-invariant (LTI) systems, this paper presents an adaptive phase compensator employing the pure time delay to improve the performance of the deterministic learning-based adaptive feedforward control with the reference input known a priori. When the adaptive phase compensation is applied to the hidden layer of the RBFNN, the nonlinear approximation capability of the RBFNN is effectively improved such that both the learning performance (learning speed and accuracy) and the control performance of the deterministic learning-based control scheme are improved. Theoretical analysis is conducted to prove the stability of the proposed learning control scheme for a class of systems which are affine in the control. Simulation studies demonstrate the effectiveness of the proposed phase compensation method.

In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler-Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.

This article studies the problem of saturation-tolerant prescribed control (SPC) for a class of nonlinear time-delay systems with unknown control directions. We propose a unified finite-time tunnel prescribed performance (FTPP), which not only provides more tight allowable set leading to smaller overshoot, but also drives the tracking error into the prescribed set within a known time. To remove the implicit assumption that both input and performance constraints need to be satisfied simultaneously, an auxiliary system is developed to establish a balance between these two constraints instead of ignoring their interconnection and conflict. With aid of its generated nonnegative signals, the developed saturation-tolerant prescribed performance (SPP) possesses flexible performance. Namely, SPP can temporarily enlarge the performance boundaries to guarantee both constraints when input saturation occurs, and fast recover back to the prescribed boundaries when input saturation disappears. Consequently, a low-complexity SPC for uncertain nonlinear systems is developed, which can always guarantee both input and performance constraints even under unknown time delay and unknown control directions. Finally, comparative simulation is provided to illustrate the merits of the presented control strategy.

Synthetic Aperture Radar (SAR) image segmentation stands as a formidable research frontier within the domain of SAR image interpretation. The fully convolutional network (FCN) methods have recently brought remarkable improvements in SAR image segmentation. Nevertheless, these methods do not utilize the peculiarities of SAR images, leading to suboptimal segmentation accuracy. To address this issue, we rethink SAR image segmentation in terms of sequential information of transformers and cross-modal features. We first discuss the peculiarities of SAR images and extract the mean and texture features utilized as auxiliary features. The extraction of auxiliary features helps unearth the distinctive information in the SAR images. Afterward, a feature-enhanced FCN with the transformer encoder structure, termed FE-FCN, which can be extracted to context-level and pixel-level features. In FE-FCN, the features of a single-mode encoder are aligned and inserted into the model to explore the potential correspondence between modes. We also employ long skip connections to share each modality’s distinguishing and particular features. Finally, we present the connection-enhanced conditional random field (CE-CRF) to capture the connection information of the image pixels. Since the CE-CRF utilizes the auxiliary features to enhance the reliability of the connection information, the segmentation results of FE-FCN are further optimized. Comparative experiments conducted on the Fangchenggang (FCG), Pucheng (PC), and Gaofen (GF) SAR datasets. Our method demonstrates superior segmentation accuracy compared to other conventional image segmentation methods, as confirmed by the experimental results.

This article addresses the event-triggered optimal tracking control problem for leader-follower spacecraft formation flying system using the adaptive dynamic programming technique. In order to solve the Hamilton–Jacobi–Bellman equation, a single-critic neural network (NN) is developed to approximate the optimal cost function. Moreover, by combining the parameter projection rule and gradient descent algorithm, a semiglobal adaptive update law is derived to tune the critic NN. In doing so, a continuous near optimal tracking controller is presented. Subsequently, an input-state-dependent event-triggered mechanism is designed to ensure that the near optimal tracking controller is implemented only when specific events occur, which significantly reduces the execution frequency of the control command. Remarkably, benefiting from the construction of an input-based triggering error, the conventional assumption on the Lipschitz continuity of the controller is tactfully removed, thus erasing the computable demand on the unknown Lipschitz constants. Rigorous analysis on the system stability and Zeno-free behavior is provided successively. Finally, numerical simulations on two formation satellites in low Earth orbit validate the effectiveness of the theoretical scheme.

MOTIVATION: The interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell-cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell-cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell-cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell-cell interactions at the cell type level, which hinders the development of computational approaches to identify cell-cell interactions. RESULTS: We develop a publicly accessible database, CITEdb (Cell-cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell-cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell-cell interactions from different tools. CITEdb contains 728 pairs of cell-cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand-receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell-cell interactions. Users can search for cell-cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell-cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell-cell interactions. AVAILABILITY AND IMPLEMENTATION: CITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-quality data transmission synchronization process is frequently expected in light of secure communication mechanisms (SCMs), especially for space laser communication among the satellite constellation. To improve security and reliability during the data transmission processes prominently, control problems of chaotic synchronization synchronouslyat the same timeare explored. In this paper, several novel error synchronization control protocols are proposed to solve these problems. First, by introducing a norm-normalized sign function (NNSF), unique (fixed-) time-synchronized stability is manifested, such that all non-zero state elements reach the origin synchronously at the same time. And upper bounds of synchronous resident time calculated by offered protocols are irrelevant/relevant to initial states of the error systems. Second, integrated with the (fixed-) time-synchronized stability theories, the (fixed-) time-synchronized sliding mode controllers with special convergent performance are established for two representative types of chaotic systems. Third, the ratio-persistent performance plays a dominating role for simultaneous convergence of the errors. Further, the innovation of the algorithms is reflected in that the decrypted signal is completely consistent with the transmitted message signal within synchronized settling time. Finally, in the simulation, not only theoretical verifications, but also practical verifications of image encryption and decryption processes verify the effectiveness of the SCMs.

This article addresses the formation control problem of flapping-wing vehicles (FWVs) under the model uncertainty and the measurement inaccuracy. A two-layer formation strategy is adopted, which consists of a formation control layer for the leaders, and a containment control layer for the followers. In both layers, attitudes and positions are required by the formation geometry. A formation state estimation algorithm is designed to achieve the desired formation states from local neighborhoods. In FWVs, attitude angles are usually achieved from angular velocities, whose measurement error accumulates during integration and leads to divergence of the system. In order to solve this problem, we explore the coupling property between the translational motion and the rotational motion of FWVs, and design a coupling-based estimation method for attitude angles. To compensate for the model uncertainty, the measurement error, and the estimation error, adaptive neural networks are developed together with the control algorithm. The stability of both the control algorithm and the estimation algorithm is guaranteed based on the Lyapunov stability theory. Simulations are conducted to validate our method, and the results illustrate its effectiveness.

In this technical correspondence, a multilayer formation (MLF) control problem is considered and solved by a unified framework. The agents in each layer present a sort of hierarchical distinction: receive information from former layers, communicate inside the current layer, and send information to subsequent layers. With an arbitrary number of layers, we extend the previous result from undirected graphs to directed ones. The proposed controller achieves MLF without using the distributed estimators and the acceleration information. This removes the induced discontinuities and alleviates the system complexity. It is then proved that the closed-loop errors are semiglobally uniformly ultimately bounded. Simulations are presented to illustrate the effectiveness of this approach.

Finite-time control is concerned with steering a system state to the origin before a certain settling-time limit, ignoring any consideration of when each state element converges relative to the others. In this article, a control problem called time-synchronized control is investigated, where all the system state elements have to converge to the origin at the same time. To facilitate this problem formulation, we introduce the notion of time-synchronized stability together with sufficient Lyapunov conditions. Based on these, the analytical solution of a time-synchronized stable system is obtained and discussed, explicitly offering a quantitative method to preview and predesign the control system performance in prior. Following these results, a robust time-synchronized control law is designed for multivariable systems under external disturbances and model uncertainties. Finally, comparative numerical simulations between time-synchronized control and finite/fixed/prescribed-time control are conducted to showcase the time-synchronized features attained.

In this article, we investigate the saturation-tolerant prescribed control (SPC) for multiinput and multioutput nonlinear systems with unknown control directions and actuator faults. We propose a concise tunnel prescribed performance (TPP) with the control design independent of initial conditions and smaller overshoots achieved due to its tight feasible region. A novel auxiliary system, to tactfully establish a feedback mechanism between input saturation and prescribed performance, is constructed. By introducing the generated nonnegative modifications into the TPP, the resulted saturation-tolerant prescribed performance (SPP) is capable of flexibly degrading performance constraints in the case of saturation; and recovering back to the user-specified performance in the case without saturation. Furthermore, the proposed control scheme guarantees not only finite-time convergence, but also SPP-constrained tracking performance despite the input saturation and uncertainties. Finally, comparative results are provided to demonstrate the distinctive merit of the proposed SPC more than the traditional prescribed performance control.

This article proposes a new design of an event-based dissipative asynchronous controller for the interval type-2 (IT2) fuzzy Markov jump systems (MJSs) subject to sensor saturation and actuator nonlinearity. By resorting to a generalized performance index, the${H_\infty }$, passive, and dissipative fuzzy control problems are solved in a unified framework. The event-based scheme is developed for the IT2 fuzzy MJSs subject to sensor saturation and actuator nonlinearity, and the energy consumption of communication can be reduced. Moreover, the system and controller modes are asynchronous, and a hidden Markov model is employed to observe the modes of the original system. The membership-function-dependent approach is applied to analyze the stability of the closed-loop system. Finally, two examples are given to demonstrate the effectiveness of the proposed algorithms.

This article addresses the event-triggered asynchronous fault detection (FD) problem of fuzzy-model-based nonlinear Markov jump systems (MJSs) with partially unknown transition probabilities. For this objective, the nonlinear plant is modeled as an interval type-2 (IT2) fuzzy MJS with the aid of the IT2 fuzzy sets capturing the uncertainties of the membership functions. An adaptive event-triggered scheme is introduced to bring down the costs of the communication network from the system to the fuzzy fault detection filter (FDF), in which the triggering parameter can be adaptively tuned with the system dynamics. A hidden Markov model (HMM) is employed to characterize the asynchronous phenomenon between the system and the FDF. Unlike the existing results, the transition probabilities of the plant and the FDF are allowed to be partially known. By using the Lyapunov and the membership-function-dependent methods, the existence conditions of the FDF are derived. Finally, the proposed FD methods are verified by a numerical simulation.

Previous research on finite-time control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. In the present work, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability (TSS), whereat the same time, all the system state elements converge to the origin, and fixed-TSS, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) TSS are presented. On the basis of these formulations of the time-synchronized convergence property, the classical sign function, and also anorm-normalized sign function, are first revisited. Then in terms of this notion of TSS, we investigate their differences with applications in control system design for first-order systems (to illustrate the key concepts and outcomes), paying special attention to their convergence performance. It is found that while both these sign functions contribute to system stability, nevertheless an important result can be drawn that norm-normalized sign functions help a system to additionally achieve TSS. Furthermore, we propose a fixed-time-synchronized sliding-mode controller for second-order systems; and we also consider the important related matters of singularity avoidance there. Finally, numerical simulations are conducted to present the (fixed-) time-synchronized features attained; and further explorations of the merits of the proposed (fixed-) TSS are described.

Distributed formation control is presented for networked Euler-Lagrange systems (ELSs) over a directed interaction topology. This problem is defined by a layered framework in which information flow both among the leaders and among the followers is described by different layers. To empower the formation to make a variety of geometric transformations, we present the necessary and sufficient conditions for affine maneuverability under a directed graph. Unlike most existing results using a diagonal stabilizing matrix to achieve the stabilizability of affine formation, this fully distributed approach is feasible without any global information. Next, we propose an adaptive control law for agents in each layer, where the closed-loop errors are driven to a neighborhood of the origin in finite time. Adaptive neural networks are integrated to tackle the model uncertainties in ELSs by updating the norm of the weight matrix, which can simplify the control design and alleviate the computational burden compared with traditional ones. The simulation results are given to show the effectiveness of the proposed approach.

This article addresses the finite-time tracking control for multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems with actuator faults and saturations. First, a fuzzy state observer is constructed to approximate the unmeasured system states, where the restrictions of the known actuator faults are removed from the observer design. Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur. Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. Furthermore, the dynamic surface control technique is adopted to avoid tedious analytic computations inherent in the backstepping procedure. It can be proved that the proposed control can not only guarantee the closed-loop system states bounded, but also regulate the tracking errors to a small neighborhood around the equilibrium in finite time despite the existence of the actuator saturations and faults. Finally, comparative simulations are carried out to demonstrate the feasibility and effectiveness of the theoretical results.

In order to maintain the submarine equipment, autonomous underwater vehicle (AUV) is usually assigned to track the submarine cables or pipes. The capabilities of navigation and control are critical to track the target accurately. Ultra-short baseline (USBL) is essential equipment for AUV, which uses sound waves for positioning. Unfortunately, due to the low frequency of USBL, it inevitably limits the frequency of control and ultimately affects the tracking effect. In order to improve the aforementioned issue and achieve better tracking tasks, intelligent collaborative navigation and control (CNaC) was herein proposed in this article. First, we proposed nonlinear state reconstruction neural network navigation, which used the neural networks to reconstruct the state between two adjacent USBL valid values online. Combined with the valid USBL and reconstructed states, the online process model generated by neural networks are applied to give the estimate position for AUV. At last, intelligent CNaC use the estimated position and valid USBL as inputs to control AUV to achieve tracking tasks. This strategy makes the control frequency free from the limitation of the USBL frequency. The proposed intelligent CNaC is demonstrated by simulation and real experiments. Compared to mechanically combining the traditional navigation and control algorithm, the tracking accuracy of intelligent CNaC improves by 81.96%.

While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers. However, speech, signal and audio data are naturally complex-valued after Fourier Transform, and studies have shown a potentially richer representation of complex nets. In this paper, we propose a Complex Transformer, which incorporates the transformer model as a backbone for sequence modeling; we also develop attention and encoder-decoder network operating for complex input. The model achieves state-of-the-art performance on the MusicNet dataset and an In-phase Quadrature (IQ) signal dataset. The GitHub implementation to reproduce the experimental results is available at https://github.com/muqiaoy/dl_signal.

Several frequency-domain based fault detection methods were developed in the high voltage (HVDC) system in recent years. In this paper, a modified empirical mode decomposition (EMD) designated as median mode decomposition (MMD) is introduced, and a new fault detection method is developed based on that. Compared with the traditional EMD, MMD is able to obtain the intrinsic median mode function (IMMF) with less sifting iterations, leading to less computation burden in the actual use. This characteristic is useful for dc fault detection because the detection delay of dc fault should be limited within 3 ms. By obtaining the Hilbert spectrum of the IMMF after MMD, the fault transient is able to be differentiated within 0.35 ms detection delay with reliable discrimination. This method is verified by simulation results in a 4-bus ring multi-terminal dc (MTDC) system in PSCAD. It is also compared with other fault detection methods to test its validity.

This paper addresses the trajectory analysis, mission design, and control law for multiple microsatellites to cooperatively circumnavigate a host spacecraft. This cooperative circumnavigation (CCN) problem is defined to drive a group of networked microsatellites to a predefined planar ellipse concerning a host spacecraft while maintaining a geometric formation configuration. We first design several potential functions to guide the microsatellites to the given planar elliptical orbit with a proper radius. Next, the affine Laplacian matrix is introduced to characterize the desired formation shape of microsatellites. Based on the potential functions and the Laplacian matrix, a CCN control law is finally proposed. Then, the simulation results of eight microsatellites with earth-orbiting mission scenarios are given, where the natural trajectory motion is incorporated which consumes nearly zero-fuel.

This paper addresses the distributed formation-containment (DFC) problem for multiple Euler-Lagrange systems with model uncertainties via output feedback in both constant and time-varying formation cases. First, a novel definition of the DFC problem is proposed using a two-layer framework. Since only parts of the followers can acquire the states of the dynamic leader, we design a distributed finite-time sliding-mode estimator to obtain accurate estimations of the desired position and velocity for each agent. Next, to deal with the absence of velocity sensors, we propose two DFC control laws combined with the high-gain observer for the leaders and the followers, respectively, while the time-varying formation in the first layer and the leader-based containment in the second layer can be achieved. Further, the adaptive neural networks are applied to deal with the model uncertainties due to their superior approximation capability. The uniform ultimate boundedness of all the state errors can be guaranteed by Lyapunov stability theory. In addition, a unified framework is given which can be transformed to four other basic distributed problems. Finally, simulation examples are presented to illustrate the feasibility of the theoretical results.