6-DoF object grasping is a crucial skill for embodied intelligent robots. Previous methods often rely on large-scale networks for feature extraction, followed by grasp pose prediction, which increases the network's parameter count and overlooks the geometric and graph features of the point cloud. To address these challenges, we propose GraphGrasp, a graph-guided 6-DoF grasping pose prediction method. It performs graph analysis from the perspectives of scene, object, and grasping graphs. First, we introduce a graph feature embedding method based on local-global features to model the scene graph effectively. Then, we use a graph transformer strategy to represent spatial relationships between objects in the object graph. Finally, we propose a multi-metric, multi-level grasp pose evaluation algorithm to predict and explore graspable points, enabling effective construction of grasp graphs and accurate grasp pose evaluation. We test GraphGrasp on the GraspNet-1Billion dataset, and the results show that, compared to previous methods, it achieves nearly the same performance with about 1/5 of the parameters of state-of-the-art methods, significantly improving grasp pose prediction speed. Additionally, in real-world robot grasping scenarios, GraphGrasp outperforms previous methods in practical grasp pose prediction tasks.

This paper investigates the time-varying output formation tracking (TVOFT) problem for heterogeneous multi-agent systems (MASs) under the jointly observable condition (JOC). In practical applications, the neighboring followers of the leader often have access only to partial information due to external disturbances and occlusions, rendering existing formation control approaches ineffective. To address this challenge, we propose a set of distributed observers based on the JOC and a hybrid time-dynamic event-triggering mechanism (ETM) to estimate the leader’s state. Unlike standard ETMs, the triggering condition in the proposed hybrid triggering approach is evaluated only after a predetermined time interval, which serves as an explicit lower bound on the minimum inter-event time (MIET). This design ensures that the MIET remains positive. Theoretical analysis demonstrates that the proposed hybrid triggering approach effectively reduces redundant triggering actions while ensuring the robustness of communication performance against external disturbances. Building upon the developed distributed observer framework, we further propose a novel observer-based TVOFT scheme, which overcomes the conventional requirement that at least one follower must have full access to the leader’s state or output. Finally, numerical simulations are provided to validate the effectiveness of the proposed scheme.

High-precision control of constrained robot manipulators under uncertainties remains a fundamental challenge. While conventional nonlinear model predictive control (NMPC) often struggles with real-time requirements due to its heavy computational burden, linear MPC (LMPC) typically relies on terminal invariant sets that are often computationally intractable for complex nonlinear systems. To address these limitations, this paper proposes a computationally efficient data-driven linear model predictive control (DLMPC) framework that achieves control accuracy comparable to NMPC while ensuring real-time performance. A variable-length sliding-window dynamic mode decomposition with control (DMDc) method is developed to identify a time-varying local affine model from recent input–output data, enabling accurate linearization under uncertainties. Based on this model, a novel time-varying terminal constraint is designed to substitute the conventional terminal set, thereby obviating the need for uncertainty upper bounds that are difficult to obtain in practice. The recursive feasibility and stability of the proposed framework are established using Lyapunov stability theory. Finally, experimental results on a Franka Emika Panda robot demonstrate the effectiveness and superior performance of the proposed method.

With the increasing complexity of mobile robot systems, multi-robot collaboration has become essential. However, such collaboration places heavy demands on both computational power and communication bandwidth. Cloud computing offers elastic and scalable infrastructure, allowing robotic functions to be encapsulated and delivered as on-demand services. Meanwhile, edge computing provides low-latency processing near the data source. The integration of these paradigms enables efficient coordination between cloud and edge infrastructures, forming a solution for real-time, scalable, and intelligent robotic control. This paper presents a cloud-edge collaborative system for mobile robots. Based on cloud-native technologies, the cloud control platform is implemented on Kubernetes (K8s) to orchestrate containerized control services, enabling dynamic resource allocation and automated deployment. An edge offloading mechanism is designed to support real-time data exchange and task coordination between robots and the cloud. Furthermore, a collaborative mapping and control framework is developed, where computationally intensive tasks such as map construction and trajectory generation are offloaded to the cloud, while real-time control and data pre-processing are executed at the edge. Experimental results in outdoor environments verify that the proposed framework significantly enhances system scalability and performance. These findings confirm the feasibility and generality of the proposed approach, highlighting its strong potential for large-scale deployment in cloud-based robotic systems.

Despite growing interest in data-driven analysis and control of linear systems, descriptor systems (or singular systems)—which are essential for modeling complex engineered systems with algebraic constraints like power and water networks— have received comparatively little attention. This paper develops a comprehensive data-driven framework for analyzing and controlling discrete-time descriptor systems without relying on explicit state-space models. We address fundamental challenges posed by non-causality through the construction of forward and backward data matrices, establishing data-based sufficient conditions for controllability and observability in terms of input-output data, where both R-controllability and C-controllability (R-observability and C-observability) have been considered. Building on them, we then extend Willems’ fundamental lemma to incompletely controllable descriptor systems. These methodological advances Data-Enabled Predictive Control (DeePC) for descriptor systems to achieve output tracking and to maintain performance under incomplete controllability conditions, as demonstrated in two case studies: i) Frequency regulation in an IEEE 9-bus power system with 3 generators, where DeePC maintained the frequency stability of the power system despite deliberate violations of R-controllability, and ii) Pressure head control in an EPANET water network with 3 tanks, 2 reservoirs, and 117 pipes, where output tracking was successfully enforced under algebraic constraints.

In this paper, an artificial reference-based terminal-free nonlinear model predictive control (NMPC) strategy is proposed for autonomous parking in constrained scenarios. Artificial references are introduced as additional decision variables in terminal-free NMPC to effectively integrate local planning and motion control. Both a terminal constraint and a terminal cost are removed in terminal-free NMPC to improve practical feasibility. Smooth and differentiable collision avoidance constraints are employed to ensure safety in irregularly confined environments. Stability and recursive feasibility are analyzed through a sufficiently large prediction horizon and forward invariant set theory. Experiment results demonstrate effectiveness and superiority of the proposed artificial reference-based terminal-free NMPC strategy for autonomous parking among irregularly placed vehicles.

RGB-D-based category-level object pose estimation has achieved very good pose estimation results in robotic grasp tasks. However, these methods rely on accurate depth information, and in industrial scenes where depth information is unknown or subject to significant interference, these methods are unable to apply. Therefore, this paper investigates the problem of category-level object pose estimation based solely on RGB images. First, to accurately predict the Normalized Object Coordinate Space (NOCS) map of objects in the scene, we build an encoding-decoding structure to achieve accurate NOCS map prediction. Then, to ensure an accurate transformation from NOCS maps to Intra-class Variation-Free Consensus (IVFC) maps, we propose a new deformable convolution that reduces the network’s computational load while improving the prediction accuracy of the IVFC maps. Finally, to fully utilize the information contained in multiple pose maps (NOCS maps and IVFC maps), we propose a Multi-Pose Map-based Pose (MPMPose) computation method to accurately predict the object pose. We test our method on the CAMERA25, REAL275, and Wild6D datasets, and the experimental results show that our proposed MPMPose can effectively complete the pose estimation task of unknown objects in scenes based solely on RGB images. Finally, we apply MPMPose to the robot grasping task in a real-world scenario. The experimental results show that MPMPose can effectively assist robots in completing the pose estimation task of objects in real scenes, enabling stable object grasping by robots.

The advent of edge computing has made real-time intelligent video analytics feasible. Previous works, based on traditional model architecture (e.g., CNN, RNN, etc.), employ various strategies to filter out non-region-of-interest content to minimize bandwidth and computation consumption but show inferior performance in adverse environments. Recently, visual foundation models based on transformers have shown great performance in adverse environments due to their amazing generalization capability. However, they require a large amount of computation power, which limits their applications in realtime intelligent video analytics. In this paper, we find visual foundation models like Vision Transformer (ViT) also have a dedicated acceleration mechanism for video analytics. To this end, we introduce Arena, an end-to-end edge-assisted video inference acceleration system based on ViT. We leverage the capability of ViT that can be accelerated through token pruning by only offloading and feeding Patches-of-Interest to the downstream models. Additionally, we design an adaptive keyframe inference switching algorithm tailored to different videos, capable of adapting to the current video content to jointly optimize accuracy and bandwidth. Through extensive experiments, our findings reveal that Arena can boost inference speeds by up to 1.58×, 1.82× and 1.98× on average while consuming only 47%, 31% and 27% of the bandwidth, respectively, all with high inference accuracy.

This paper proposes a resilient output feedback tube-based model predictive control (MPC) approach for constrained cyber-physical systems (CPSs) to handle the impact of stochastic hybrid attacks, where the hybrid attacks include false data injection (FDI) attacks and denial-of-service (DoS) attacks that occur in the sensor-controller (S-C) and controller-actuator (C-A) channels, respectively. The anomalous behavior of the attacker is revealed by the designed attack detector and comparator, which provide alert signals that guide the primary and auxiliary controllers to collaboratively generate control inputs as well as a nominal trajectory. The tolerable attack duration is determined by using the concept of$\mu $-step robust positive invariant ($\mu $-RPI) set, which limits the size of the deviation between the observer and the nominal trajectory under the hybrid attacks. Robust constraint satisfaction and robust asymptotic stability are ensured by restricting the state of the system to a tube centered on a nominal trajectory that converges gradually to the origin, and theoretical guarantees are provided. Finally, the effectiveness of the designed algorithm is validated through a supply chain model, which includes comparisons with an inelastic scheme.

An accurate 6D object pose estimation is essential for robotic manipulation and augmented reality applications. Existing methods typically require extensive training for new objects, limiting their effectiveness in dynamic environments where new objects are frequently introduced. In this paper, we propose FreePose, an efficient free-trained zero-shot 6D pose estimation method leveraging pre-trained visual and geometric foundation models. Our approach includes an offline onboarding stage, in which multiple viewpoint templates of a reference object are rendered, then visual and geometric features are extracted using visual and geometric pretrained models, respectively. These visual features are then back-projected onto corresponding 3D points, enabling a precise alignment between appearance and geometry, and subsequently fused with geometric features to form a robust unified representation. During inference stage, target object instances are segmented from RGB-D image using SAM2 coupled with an object-matching algorithm. Visual features of each target instance is similarly extracted, back-projected, and fused with geometric features. Robust 3D-3D correspondences are then established using nearest-neighbor search. Finally, pose estimation is obtained using the TEASER registration algorithm. Extensive evaluations conducted on the BOP5 core datasets show that our approach achieves results comparable to state-of-the-art methods. To highlight the effectiveness and potential of FreePose in real-world scenarios, FreePose is deployed on a real UR3 robot to perform grasping experiments reaching a success grasp rate of 65.0%.

This article investigates the problem of robust trajectory design for low-thrust spacecraft subject to state and observation uncertainties. An adaptive latent reinforcement learning (RL) scheme based on sequential latent variable models (SLVMs) is proposed to address this issue. First, an SLVM is employed for the representation learning of uncertain environments and for predicting future observations. Subsequently, by integrating representation learning based on the SLVM with proximal policy optimization (PPO), a stochastic latent PPO (SLPPO) scheme is introduced. Distinct from existing methods, the control policy is derived from learned stochastic latent variables rather than raw uncertain observations, which effectively mitigates the adverse impact of uncertainties on control performance. Furthermore, to enhance training efficiency, an improved dense reward shaping mechanism is designed based on the observation predictions from the SLVM and adaptive techniques. Finally, numerical simulations of two rendezvous missions validate the effectiveness of the proposed approach.

In this article, cycle-time configuration is realized using max-plus algebra for a parallel processing system via a synchronous feedback controller. As a key efficiency metric of parallel processing systems, throughput is determined by cycle time, which is threatened by clock asynchrony and the curse of dimensionality. Using instruction dependency and weak linear independence, the parallel processing system is equivalent to a max-plus nonautonomous system to mitigate the curse of dimensionality caused by numerous processing tasks. Based on the max-plus nonautonomous system, the cycle-time configuration is achieved via a synchronous feedback controller while adhering to time restrictions of the parallel processing system. Numerical simulations validate the effectiveness of the proposed cycle-time configuration in parallel processing systems.

This study addresses the cooperative tracking control for multiple vertical take-off and landing (VTOL) drones operating in networked environments. The problem is particularly challenging due to the strongly nonlinear dynamics of drones, uncertain time-varying disturbances, limited communication bandwidth, and control chattering on the first-order sliding manifold. Existing approaches often address only part of these challenges or lack rigorous fixed-time convergence guarantees. To tackle these issues, this article proposes a novel adaptive neural network event-triggered fixed-time super-twisting (ANEFS) control strategy within a double closed-loop hierarchical framework. In the outer loop, a novel auxiliary variable-based distributed fixed-time estimator (ADFE) is designed, which, unlike conventional asymptotic estimators, guarantees fast and accurate estimation of the leader's trajectory. This is integrated into an event-triggered fixed-time super-twisting (EFST) control law that ensures precise position tracking while significantly reducing network usage. In the inner loop, an adaptive neural network fixed-time super-twisting (ANFST) torque controller robustly handles complex system nonlinearities and uncertainties, ensuring rapid attitude tracking. The effectiveness of the proposed method in providing fast, robust, and resource-efficient cooperative tracking is demonstrated through both numerical simulations and real-world flight experiments using lightweight Crazyflie quadcopters.

Tube-based model predictive control (TMPC), a well-performed control algorithm in disturbed control scenarios, is widely applied in networked control systems (NCSs). Due to the inherent nature of communication networks, NCSs always are at risk of being attacked. Consequently, the pristine TMPC-based systems may undergo severe destruction. However, the field about resilience of this control method against cyber-attacks in communication links is still in its infancy. In this article, we consider a resilience scheme based on an actuator buffer to eliminate the harmful effects caused by denial-of-service attacks. In addition, we assess the inherent attack tolerance of TMPC by calculating the maximal permissible open-loop steps, the maximal steps with no feedback control while provably maintaining recursive feasibility and input-to-state stability, for arbitrary states. The effectiveness and merits of the proposed resilience scheme and algorithm are demonstrated through numerical comparisons, furthermore, implemented on a three-wheeled omnidirectional robot platform.

With the advancement of computer vision, the recently emerged 3D Gaussian Splatting (3DGS) has increasingly become a popular scene analytics algorithm due to its outstanding performance. Existing cloud-based 3DGS architectures overlook the challenges in real-world environments when handling large-scale scene analysis. This exposes issues such as inefficiency, low security, lack of privacy, and limited scalability. In this paper, we propose Radiant, a hierarchical framework for large scene analytics in a heterogeneous cloud-edge-device system, which jointly considers high efficiency, privacy and security, and scalability. Via extensive empirical study, we find that it is crucial to partition the regions for each edge appropriately and allocate varying camera positions to each device for image collection and training. The core of Radiant is partitioning regions based on heterogeneous environment information and allocating workloads to each device accordingly. Furthermore, we provide a 3DGS model aggregation algorithm that enhances the quality and ensures the continuity of models' boundaries. Finally, we develop a testbed, and experiments demonstrate that Radiant improved reconstruction quality by up to 25.7% and reduced up to 79.6% end-to-end latency.

This article addresses the challenge of amplitude-unbounded false data injection (FDI) attacks targeting the sensor-to-controller (S–C) channel in cyber-physical systems (CPSs). We introduce a resilient tube-based model predictive control (MPC) scheme. This scheme incorporates a threshold-based attack detector and a control sequence buffer to enhance system security. We mathematically model the common FDI attacks and derive the maximum duration of such attacks based on the hypothesis testing principle. Following this, the minimum feasible sequence length of the control sequence buffer is obtained. The system is proven to remain input-to-state stability (ISS) under bounded external disturbances and amplitude-unbounded FDI attacks. Moreover, the feasible region under this scenario is provided in this article. Finally, the proposed algorithm is validated by numerical simulations and shows superior control performance compared to the existing methods.

This article deals with a class of strict-feedback multi-input–multi-output (MIMO) nonlinear systems, which are characterized by model uncertainties, unknown control coefficient matrices, nonvanishing external disturbances, and multiple unknown time-varying faults in both sensors and actuators. To achieve accurate reference trajectory tracking while fulfilling fixed time-interval asymmetric output constraints (FTAOCs), a novel filter-backstepping adaptive-neural-fault-tolerant constrained (FBAFC) algorithm is developed based on only partial information from faulty sensors. The controller integrates radial basis function neural networks (RBFNNs) to approximate unknown nonlinearities and compensate for compound uncertainties. A coordinate transformation is introduced for iterative backstepping design, and a new barrier Lyapunov function (BLF) with a shift function is derived in three phases. The convergence of tracking errors is guaranteed theoretically, even in the presence of multiple actuator faults. Finally, simulation results are presented in two scenarios to validate the proposed method.

Category-level object pose estimation is an important task in computer vision. Some prior methods based on assumptions often struggle with drastic changes in object appearance. To address this challenge, we propose a new method for object pose estimation based on object-adaptive keypoints. In this paper, we first introduce a transformer-based keypoint prediction method for adaptive forecasting of point cloud keypoints. This method calculates the similarity between keypoint features and point cloud features, allowing keypoints to represent object geometry more effectively. Furthermore, to enhance the geometric feature construction of keypoints, we propose a graph-based keypoint feature aggregation method, which considers both the structural relationships between keypoints and the point cloud, strengthening the network's understanding of geometric structures. At this stage, keypoints remain at the geometric spatial level of the object and have not been predicted in NOCS. To improve the accuracy of keypoint prediction in NOCS, we design a NOCS voxelization method that divides NOCS into multiple voxels and accurately predicts NOCS keypoints within these voxels. Experimental results on multiple benchmark datasets demonstrate that our proposed KeyPose method outperforms all existing methods, achieving over 20% improvement in pose accuracy on some critical datasets.

While most current RGB-D-based category-level object pose estimation methods achieve strong performance, they face significant challenges in scenes lacking depth information. In this paper, we propose a novel category-level object pose estimation approach that relies solely on RGB images. This method enables accurate pose estimation in real-world scenarios without the need for depth data. Specifically, we design a transformer-based neural network for category-level object pose estimation, where the transformer is employed to predict and fuse the geometric features of the target object. To ensure that these predicted geometric features faithfully capture the object’s geometry, we introduce a geometric feature-guided algorithm, which enhances the network’s ability to effectively represent the object’s geometric information. Finally, we utilize the RANSAC-PnP algorithm to compute the object’s pose, addressing the challenges associated with variable object scales in pose estimation. Experimental results on benchmark datasets demonstrate that our approach is not only highly efficient but also achieves superior accuracy compared to previous RGB-based methods. These promising results offer a new perspective for advancing category-level object pose estimation using RGB images.

With the advent of parameter-efficient fine-tuning techniques for pre-trained vision-language models, interest in adapting them for various incremental learning scenarios has grown, i.e., sequential increments on task, class, and domain. However, no high-performance incremental learning framework has integrated these three incremental scenarios to achieve Unified Incremental Learning (Uni-IL) in complex settings. In this work, we propose an incremental learning framework called Mixture of Attribute-Guided Experts (MAGE) to alleviate the long-term forgetting in vision-language model incremental learning. Our approach involves acquiring image attribute knowledge via LLMs to form an attribute pool. We match the most relevant attributes as inputs to the Mixture of Experts (MoE) to fine-tune the pre-trained CLIP. Then the expert routers learn to select specific expert combinations based on the data and attribute features, alleviating catastrophic forgetting. The attribute pool incorporates both domain and class knowledge, enabling our approach to adapt to the three types of incremental learning scenarios and thus facilitating unified incremental learning. Through extensive experiments on our newly proposed benchmark and existing incremental learning scenarios, the results demonstrate that our proposed method not only performs well on the new Uni-IL tasks but also consistently outperforms previous state-of-the-art methods. Source code is available at https://github.com/ElectricField/Uni-IL.

Abstract Large amounts of data is necessary for deep learning models to semantically segment images. A major issue in the field of medical imaging is accumulating adequate data and then applying specialized skills to label those medical imaging data. Collaboration across institutions might be able to alleviate this problem, but sharing medical data to a centralized place is complicated due to legal, privacy, technical, and data ownership constraints, particularly among international institutions. By guaranteeing user privacy and preventing unauthorized access to raw data, Federated Learning plays a significant role especially in decentralized deep learning applications. Each client is given a unique learning process assignment. Clients first train a machine learning model locally using data from their area. Then, clients upload training data (local updates of model weights and biases) to a server. After that, the server compiles client‐provided updates to build a global learning model. Due to the numerous parameters (weights and biases) employed by deep learning models, the constant transmission between clients and the server raises communication costs and is inefficient from the standpoint of resource use. When there are more contributing clients and communication rounds, the cost of communication becomes a bigger concern. In this paper, a novel federated learning with weight sharing optimization compression architecture FedWSOcomp is proposed for cross institutional collaboration. In FedWSOcomp, the weights from deep learning models between clients and servers help in considerably reducing the amount of updates. Top‐z sparsification, quantization with clustering, and compression with three separate encoding techniques are all implemented by FedWSOcomp. Modern compression techniques are outperformed by FedWSOcomp, which achieves compression rates of up to 1085× while saving up to 99% of bandwidth and 99% of energy for clients during communication.

Event cameras are novel bio-inspired sensors, which have the advantages of high temporal resolution, high dynamic range and low latency compared with traditional cameras. Employing event cameras for semantic segmentation is an area that deserves in-depth research to exploit the great potential of event cameras. Due to the asynchronous nature of data from event cameras, it is challenging to produce accurate labels directly from events for the training of event-based semantic segmentation networks, especially in low-light environments. In this paper, we propose a new automatic label generation method under low-light environment for event-based semantic segmentation. Firstly we propose a modified automatic label generation module to generate more accurate labels for training. Meanwhile, we propose an event-assisted low-light enhancement module to improve the accuracy of the labels of low-light data. Finally we provide an event-based semantic segmentation dataset with high-precision paired labels for both low-light and normal environments. Experiments have shown that our method accomplishes high-quality event-based semantic segmentation in both low-light and normal environments by acquiring high-precision labels. To the best of our knowledge, our work is the first to realize event-based semantic segmentation in low-light environments in the field of event-based semantic segmentation.

With rapid development of industrial internet, industrial cyber-physical systems (ICPSs) have been widely deployed to perform and supervise industrial applications. However, ICPSs still face significant cybersecurity challenges. Traditional defense mechanisms are mostly static and passive, which may fail to provide real-time protection. To solve the aforementioned problem, moving target defense (MTD) technique has been proposed as a proactive solution. However, due to the increasing sophistication and persistence of cyberattacks, it is difficult for single-phase MTD approaches to provide effective defense by only mitigating individual phases of the attack process. Therefore, we present Deep-Shield, a novel multi-phase MTD approach based on hierarchical deep reinforcement learning (HDRL) to improve the defense performance of single-phase MTD approaches when facing advanced persistent threat (APT) in ICPSs. We consider three representative MTD countermeasures, i.e., IP address shuffling, software diversity and components redundancy, which are able to mitigate attacks at different phases of cyber kill chain and protect assets contained in critical infrastructures. Firstly, we formulate the dynamic implementation of multi-phase MTD countermeasures as a semi-Markov decision process. Secondly, we detect current attack patterns by a neural network called PerNet, which is derived from Anomaly Transformer. Then, we design a HDRL-based multi-phase MTD algorithm for defense decision-making. Finally, through extensive experiments on a platform of software defined networks, we show that our proposed approach can achieve better defense performance compared with state-of-the-art solutions when dealing with APT.

In the industrial IoT scenario, where massive data generation occurs, network traffic classification is crucial for operational security. The Internet of Unmanned Agents (IUA) is an emerging concept within the IoT framework. It focuses on the connectivity and interaction of various unmanned agents, such as drones, autonomous robots, and smart sensors. These unmanned agents collect and transmit large amounts of data in real-time, further contributing to the complexity of data in the IoT environment. The IUA aims to enable seamless cooperation and coordination among these agents, enhancing the overall efficiency and intelligence of industrial operations. The primary challenges in the IUA scenario lie in developing effective models and meeting real-time processing demands. Traditional methods struggle with large, high-dimensional data, while transformer-based models, although achieving good results, are difficult to deploy due to their size, training times, and complex tuning. In this article, we introduce a simple distributed architecture MVTC, which incorporates prior domain knowledge and delivers comparable results to transformer-based models but with shorter processing times and easier deployment. And it does not require large-scale unlabeled data for pretraining, which makes it highly suitable for real-world network traffic classification. The experiments demonstrate that the proposed method outperforms most existing approaches by up to 1.53% while using only 15.26% of the parameters.

Leveraging cloud computing for solving nonlinear Model Predictive Control (NMPC) can address issues with slow computational efficiency and effectively handle the complexity of large-scale complex systems (LSS), characterized by numerous variables, nonlinearities, and constraints. However, current studies often overlook critical aspects such as reliability, feasibility, stability, and resource efficiency in cloud-based NMPC, potentially limiting its application in LSS. To address these challenges, this paper explores a cloud-edge cooperative MPC architecture with an event-triggered strategy. The proposed architecture comprises high-fidelity Cloud NMPC, tube-based Edge LMPC, and a Switch Module with an event-triggered strategy, seamlessly combining abundant cloud computing resources with reliable edge computing. In case of Cloud NMPC failure, the tube-based LMPC at the edge layer promptly takes over control, ensuring both the reliability of Cloud NMPC and recursive feasibility under arbitrary switching sequences. By leveraging Lyapunov functions, the minimum stability modedependent dwell time (MDT) is pre-determined offline to guarantee exponential asymptotic stability. To strike a balance between control performance and resource efficiency, an eventtriggered strategy for Cloud NMPC is devised to prevent wastage of resources due to unnecessary communication and computation. Simulations on plug-in hybrid electric vehicles (PHEVs) validate the effectiveness of the theoretical results. The superiority of the proposed scheme is underscored through a comparison with four other MPC schemes.

As more Internet of Things (IoT) applications gradually move toward the cloud-edge collaborative model, the containerized scheduling of workflows extends from the cloud to the edge. However, given the high delay of the communication network, loose coupling of structure, and resource heterogeneity between the cloud and the edge, workflow containerization scheduling in the cloud-edge scenarios faces the difficulty of resource collaboration and application collaboration management. To address these two issues, we propose a KubeEdge-cloud–edge-scheduling scheme named KCES. This workflow containerization scheduling scheme includes a cloud-edge workflow scheduling engine for KubeEdge and incorporates workflow scheduling strategies for tasks’ horizontal roaming and vertical offloading. This article proposes a cloud-edge workflow scheduling model and node model, as well as a workflow scheduling engine designed to maximize cloud-edge resource utilization under the constraint of workflow task delay. A cloud-edge resource hybrid management technology is used to devise the cloud-edge resource evaluation and resource allocation algorithms to achieve cloud-edge resource collaboration. Based on the ideas of distributed functional roles and the hierarchical division of computing power, the horizontal roaming among the edges and cloud-edge vertical offloading strategies for workflow tasks are designed to realize cloud-edge application collaboration. Experimental results using a customized IoT application workflow instance demonstrate that KCES outperforms three comparing algorithms in total workflow time, average workflow time, and resource usage and features horizontal roaming and vertical offloading of workflow tasks.

With the rapid advancement of autonomous driving technology, the increasing computational demands of intelligent vehicles have driven the adoption of cloud and edge computing to augment limited onboard resources. However, this cloud–edge integration presents new challenges for efficient task offloading. In addition, the high mobility of vehicles further complicates the design of reliable offloading strategies. To address these challenges, this paper proposes a Cloud-Edge-Vehicle (CEV) framework that leverages predictable vehicle trajectories for optimized task offloading. A spatio-temporal multi-head self-attention long short-term memory (ST-MHSA LSTM) model is designed to accurately predict vehicle trajectories by capturing motion trends and interactions with neighboring vehicles. Building upon the trajectory prediction information, a deep reinforcement learning (DRL)-based task offloading algorithm is proposed. This algorithm incorporates a dynamic priority assignment strategy to prioritize delay-sensitive tasks according to their urgency, thereby improving offloading performance and reducing system costs associated with task transmission and execution. To mitigate the adverse effects of inevitable trajectory prediction errors, a prediction consistency-based deviation correction strategy is further introduced, enhancing decision robustness in dynamic scenarios. Simulation results show that as task numbers increase, the proposed framework outperforms traditional methods (i.e., local, edge, cloud, and random computing) in task success ratio, processing delay, and energy consumption. Task success ratio improves by 115.79%, 114.44%, 27.09%, and 135.19% over local, edge, cloud, and random computing, respectively. Average processing delay is reduced by 70.47%, 62.23%, 38.69%, and 79.85%, while average energy consumption decreases by 65.33%, 66.59%, 59.12%, and 74.66%. These results highlight the framework’s superior performance for computation-intensive and delay-sensitive vehicular applications.

With the rapid advancement of cloud computing, cloud services have been widely adopted for managing large-scale and complex IoT workflow applications due to their robust computational capabilities. However, efficiently scheduling and deploying these workflows while ensuring quality-of-service (QoS) for diverse users remains a significant challenge for cloud service providers. In this study, we propose a novel multi-stage workflow scheduling algorithm (RE-ACO) for energy-efficient management of reliability-constrained IoT applications in cloud environments. The algorithm operates in three key stages: Task ordering by ACO, reliability constraint distribution with feedback information and energy-aware task assignment. The RE-ACO leverages ACO and an energy-aware task assignment strategy to optimize energy usage without compromising workflow reliability. First, the ACO algorithm determines the optimal task execution sequence. Next, a feedback-based reliability distribution method dynamically assigns sub-reliability constraints to individual tasks. Finally, each task is allocated to a virtual machine (VM) that minimizes energy consumption while meeting its sub-reliability requirement. Simulation results demonstrate that RE-ACO outperforms existing approaches, achieving the lowest energy consumption for reliability-constrained workflow scheduling compared to three benchmark algorithms.

In this paper, event-triggered secure fusion estimation is investigated for a multirate multisensor system under linear man-in-the-middle (MITM) attacks and correlated heavy-tailed noises. A dynamic event-triggered mechanism is proposed to reduce measurement information transmission over communication networks with linear MITM attacks. A watermarking strategy is designed to address impact of linear MITM attacks by fully recovering measurement information. An event-triggered local estimator is designed to obtain local estimates under correlated heavy-tailed noises and event-triggered measurements. A distributed fusion estimator is presented to improve estimation accuracy based on sequential fast covariance intersection fusion technique. Sufficient conditions are given to ensure boundedness on estimation error scale matrices of the event-triggered local estimator and the distributed fusion estimator. Simulation and experimental results are given to illustrate validity of the event-triggered secure fusion estimation.

This paper explores the prescribed-time consensus control problem for a class of high-order strict-feedback nonlinear multi-agent systems with output state constraints. A distributed adaptive$\mathit {C^{1}}$smooth control scheme is developed such that the output-constrained consensus tracking is achieved within a prescribed time regardless of any initial conditions and other design parameters, although in the presence of completely unknown control gains and uncertainties. The prescribed-time control scheme is made possible for high-order systems by constructing a hybrid time-varying gain-based prescribed-time first-order filter and using the time transformation methods. Particularly, a novel uniformly switching mechanism is introduced into the time-varying gains, where the gain function is$\mathit {C^{1}}$smooth and the switching time is regardless of initial conditions, ensuring that the gains are uniformly bounded for all time and thus avoiding infinite time-varying gain problem. Finally, the benefits and effectiveness of the proposed control scheme are confirmed by a numerical simulation and its application to a 2 degree of freedom robotic manipulator. Note to Practitioners—This paper investigates the distributed adaptive$C^{1}$smooth control problem for nonlinear multi-agent systems with high-order strict-feedback dynamics, which can model various physical systems, such as flight systems, robotic systems, and autonomous aerial vehicles. Achieving distributed prescribed-time control for high-order MASs with completely unknown control gains, output constraint, and uncertainties poses significant challenges. By introducing a novel$C^{1}$smooth gain switching mechanism, a hybrid time-varying gain function is constructed to ensure uniform boundedness of the time-varying gain, which effectively avoids the high-gain implementation problem. Moreover, a prescribed-time distributed$C^{1}$smooth controller, operating smoothly for all time and everywhere is also forwarded, which is more desirable in practice. As a result, the proposed control scheme demonstrates high adaptability to practical engineering requirements and exhibits enhanced generalizability, as finally validated by the numerical simulations including a dual-degree-of-freedom robotic manipulator.

This paper investigates a distributed model predictive control (DMPC) for linear heterogeneous systems tracking arbitrary periodic references. The control objective consists of two parts: (i) driving the output of each subsystem consensus; (ii) steering the outputs as close as possible to an exogenous periodic reference. The artificial state reference and control input are considered as decision variables to track unreachable references. The optimal control problem (OCP) is then solved in a distributed manner using Alternating Direction Multiplier Method (ADMM). The proposed method does not need ADMM convergence at each time step, which greatly reduces the computation time. Under several mild assumptions, the feasibility of the OCP and the closed-loop asymptotic stability with respect to an optimal reachable cooperative trajectory are presented. The performance of the approach is demonstrated with some simulation results.Note to Practitioners—The paper is motivated by the problem of cooperative tracking unreachable references for heterogeneous systems. The generation of the reference signal often ignores the dynamics feature of systems, leading to such reference may not be fully tracked (unreachable reference). However, existing methods either lack optimality or cannot achieve cooperative tracking of unreachable references. Therefore, this study develops a novel DMPC approach to make up for the above lack. In addition, the proposed method greatly reduces the computation time while ensuring optimality, and has a wider initial feasibility. The proposed controller can be extended to cooperative track unreachable constant signal. The proposed method can be used for highly collaborative tasks, such as formation missions, collaborative transportation and spacecraft collaboration. In future research, we will address the problem of cooperative tracking unreachable references for nonlinear heterogeneous systems.

This work focuses on the issue of fixed-time tracking control for a class of nonlinear systems affected by unknown uncertainties and external disturbances. First, a fixed-time neuroadaptive approximator is proposed to estimate the lumped uncertainties in nonlinear systems. Unlike existing neural network based methods, the estimation solution presented here introduces an adaptive predictor based learning mechanism, which would improve the estimation performance by removing the effect of tracking errors on the estimation process. Then, based on the reconstructed information, a fixed-time command filtered backstepping controller is developed with a fixed-time compensation system. In the compensation system, a novel bounded function is skillfully utilized such that the order and complexity of the compensation system are effectively reduced. Moreover, it is demonstrated that the designed control scheme can drive the tracking error to a small set near zero in a fixed time. Finally, the validity of the proposed control scheme is illustrated by numerical simulationsNote to Practitioners—This paper is motivated by the tracking control problem for nonlinear systems such as robotic, spacecraft, and unmanned aerial vehicle system. Existing tracking control schemes often suffer from issues such as insufficient tracking speed, redundant design process and the explosion of complexity. A fixed time neuroadaptive backstepping control scheme is proposed in this paper to ensure convergence of the error within a fixed time. An adaptive predictor-based fixed-time neuroadaptive estimator is presented to enhance the speed and accuracy of uncertainty estimation. Furthermore, a novel fixed-time compensation system is presented, which effectively addresses the issue of complexity explosion while reducing coupling of the compensation system, making the controller more concise. The effectiveness of the proposed method is validated through a numerical simulation in an uncertain spacecraft pitch motion system. Future work will involve validation of the proposed method on a hardware-in-the-loop simulation system or experiment platform.

Human-robot collaboration represents a promising avenue for applications in future factory scenarios. Existing work predominantly concentrates on passive assistance based on real-time sensing sensor like force sensors, while only a few studies have ventured into the exploration and realization of active assistance provided by robots with the assistance of predictive sensors. This paper proposes an innovative hybrid framework that combines human bio-signal information with built-in robot force sensors to implement human-robot active collaboration. First, a Hill-type muscle-skeleton model is adopted and calibrated through partial swarm optimization (PSO). With this model, surface electromyography(sEMG) is used to estimate human limb stiffness. Then, an Radial Basis Function neural network (RBFNN) compensator is developed to account for the uncertainty in human-object-robot dynamics. Subsequently, we propose an adaptive variable impedance controller, incorporating a global bias into the neural network architecture. This innovative modification serves to augment the system’s robustness, streamline the network configuration by curtailing the number of hidden neurons, and consequently, facilitate more consistent and efficient human-robot interaction behavior. Finally, we substantiate the effectiveness of the proposed methodology through a two-link robotic simulation experiment and a real-world co-carrying task employing with the Baxter robot and human partner. These rigorous evaluations unveil a significant alleviation of task-related human workload attributed to our proposed framework.Note to Practitioners—This framework aims to address the existing research gap in human-robot collaboration, particularly involving bio-signal utilization, to facilitate perceptive active assistance within a typical industrial assembly scenario. In such representative tasks, many studies primarily employ real-time sensors such as force, position. These sensors, while essential, are limited by their detection principles and require collaborative operation to ascertain stiffness and realize passive assistance. Conversely, bio-signals intrinsically contain stiffness information and exhibit prospective characteristics that can be leveraged for stiffness prediction. In this typical task, we design a human-robot co-transport system with two crucial characteristics: first, the robot is capable of detecting human stiffness tendencies and comprehending human intent, leading to self-adjusting robotic behavior that provides enhanced protection for the transported object. Secondly, the newly proposed controller can manage sudden disturbances and execute self-repairs, thus increasing the task success rate and ensuring worker safety.

This paper addresses the challenges of energy consumption, driving safety, and robustness in tracking control for autonomous vehicles under stochastic disturbances by proposing a robust economic model predictive control (REMPC) algorithm without terminal constraints. The disturbances considered include high-probability small disturbances as well as low-probability large disturbances in tracking control. A tightening constraint is introduced to ensure robustness against small disturbances, leveraging constraint tightening theory. Additionally, a maximum probability interval is derived to account for the large disturbances that the vehicle can withstand. To further enhance driving safety and energy efficiency, a variable space constraint is adaptively designed based on road slope information. The paper demonstrates recursive feasibility and robust asymptotic performance of the optimization problem with variable space constraints. Robust asymptotic stability in probability of the system is ensured by deriving a probability condition and a lower bound on the prediction horizon, with an exhaustive principle for selecting the prediction horizon. The efficiency and superiority of the proposed REMPC algorithm are verified by a comprehensive case study under various operating conditions.

Nonlinear model predictive control (NMPC) is a promising approach for controlling large-scale complex systems (LSS) that exhibit nonlinearity and constraints. However, its computational and real-time limitations hinder its widespread adoption. To address this challenge, we propose a cloud-edge cooperative model predictive control (MPC) scheme that overcomes these limitations while ensuring the desired control performance. Specifically, our proposed approach involves designing a cloud-based NMPC with a high-fidelity nonlinear model for the cloud layer. Meanwhile, the edge layer is equipped with a simplified backup linear model predictive control (LMPC) that uses a linearized model based on constraint tightening to mitigate model mismatch errors. Additionally, we develop an automatic strategy that employs a sliding weighted average method to switch between the cloud and edge controllers, enhancing the system’s reliability under non-ideal networking conditions. We provide a thorough analysis of the recursive feasibility and asymptotic average performance of the control scheme with different prediction models in the cloud and edge layers. To validate our approach, we apply it to a charging system for plug-in hybrid electric vehicles (PHEVs). Furthermore, we compare the performance and computation efficiency of our proposed cloud-edge cooperative MPC scheme with four other MPC schemes.Note to Practitioners—This work aims to overcome the challenge of reducing the computational burden and increasing the applicability of nonlinear model predictive control (NMPC) in large-scale complex systems (LSS) with input nonlinearity. To address these issues, we propose a cloud-edge cooperative MPC scheme that involves deploying the cloud layer on a remote cloud server and the edge layer locally onboard the system. The proposed scheme not only provides a practical solution for LSS with input nonlinearity but is also applicable to general nonlinear systems. Further research will focus on exploring the relevant theory and practical aspects of the scheme.

Serverless is an emerging cloud paradigm that offers fine-grained resource sharing through serverless functions. However, this resource sharing can cause interference, leading to performance degradation and QoS violations. Existing white box-based approaches for serverless resource provision often demand extensive expert knowledge, which is challenging to obtain due to the complexity of interference sources. This paper proposes Qora, a neural-enhanced interference-aware resource provisioning system for serverless computing. We model the resource provisioning of serverless functions as a novel combinatorial optimization problem, wherein the constraints on the queries per second are derived from neural network performance model. By leveraging neural networks to model the nonlinear performance fluctuations under various interference sources, our approach better captures the real-world behavior of serverless functions. To solve the formulated problem efficiently, rather than adopting commercial optimizer solvers like Gurobi, we propose a two-stage-VNS algorithm that searches discrete variables more efficiently and supports Sigmoid activations, avoiding introducing redundant discrete variables. Unlike pure machine learning methods lacking theoretical optimal guarantees, our approach is rigorously proven globally optimal based on optimization theory. We implement Qora on Kubernetes as a serverless system automating resource provisioning. Experimental results demonstrate that Qora reduces the QoS violation rate by 98% while reducing up to 35% resource costs compared with the state-of-the-arts. Note to Practitioners—From the perspective of cloud service providers, this paper considers the automatic resource provisioning for serverless functions. To improve hardware utilization, cloud providers tend to co-locate serverless functions on the same server. However, co-located functions compete for shared resources (memory bandwidth, L3 cache, etc.), which causes interference and leads to performance degradation and QoS violations. We use neural networks to build the performance models of interference-prone serverless functions and form the resource allocation optimization problem with neural network performance models as constraints. Compared to white box modeling methods, our neural network modeling adapts to complex and variable interference. Compared to deep reinforcement learning methods, our combinatorial optimization methods have stronger interpretability. In order to solve this optimization problem efficiently, we design the two-stage-VNS solution algorithm. We implement Qora on Kubernetes as a serverless system, which can automatically allocate computing resources. Experiments with small-scale real clusters and large-scale simulations demonstrate the effectiveness of Qora.

This paper investigates the fixed-time attitude tracking control problem for rigid spacecraft subject to external disturbance and output constraints. First, the state transformed function (STF) technique is employed to convert the constrained spacecraft error dynamics into an unconstrained one. Subsequently, a fixed-time disturbance observer (FXTDO) is designed to estimate and reconstruct the lumped disturbance of unconstrained system. Combined with the developed STF, FXTDO and fixed-time integral terminal sliding mode (FITSM) surface techniques, the proposed fixed-time control law provides zero-error attitude tracking with high control precision and chattering avoidance, while the output constraints are never transgressed. The fixed-time stability of the closed-loop system is conducted via the Lyapunov technique and bi-limit homogeneity theory, and the expression of convergence time is also presented. Simulations illustrate the efficiency of the investigated controller.

In this paper, a novel robust safety-critical control method is proposed to ensure whole-body safety for the robotic manipulator, which is implemented as a sampled-data system with measurement errors. The manipulator and obstacles are approximated as several spherical enclosures, and a whole-body safety constraint with relative degree two is formulated based on the distance function. Robust control barrier function (CBF) constraints are first designed to handle the predefined joint velocity constraints. Building upon this, a robust high-order CBF constraint is derived to enforce the whole-body safety constraint. Each stage of the derivation incorporates the sample-and-hold error and measurement error. These robust CBF constraints are then unified with a nominal controller to form an optimization problem, ensuring that the velocity constraints and the safety constraint are satisfied. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments on a 7-degree-of-freedom (DOF) Franka Emika Panda robot.

In this paper, a joint fault-tolerant and fault diagnosis strategy is proposed for handling multiple actuator faults in full-vehicle active suspension systems. Different from traditional methods where fault detection and isolation must precede the fault-tolerant control to provide the latter with certain fault information, our proposed scheme performs both jointly over the whole operation process with guaranteed suspension performance. For the fault-tolerant control, we develop high gain filters under a fast timescale to estimate the variations of the integrated control inputs caused by actuator faults, and the equivalent fault-free parts of the integrated control inputs are selected as the target control variables. In such a decomposition, we are able to determine the control law of each actuator without using the information of the faulty actuators. Under the framework of the fault-tolerant algorithm, we construct a bank of adaptive fault diagnosis observers to online identify the force constants of the actuators, where information of the heave, pitch, and roll motions are all utilized to ensure persistent excitation and enhance identification accuracy. In particular, actuator faults can be detected via the estimation of the force constants, in which both the location and severity of the actuator faults can be further identified. Subsequently, control commands are reassigned to alleviate the use of faulty actuators and thus protect them from further damage. The effectiveness of our proposed method is validated via extensive simulation results.Note to Practitioners—Satisfactory performance of vehicle active suspension systems is founded upon good reliability and safety of the suspension systems, which motivates us to conduct deep research into fault diagnosis and fault-tolerant problems to improve the significant system reliability. In this paper, a joint fault-tolerant and fault diagnosis (joint FTFD) scheme is proposed for addressing multiple actuator faults of full-vehicle active suspension systems. The proposed method can be used to guarantee suspension performance over the whole operation period even if multiple actuator faults occur, and the location and severity of the actuator faults can be identified at the same time. Different from the traditional methods where fault diagnosis precedes the fault-tolerant control to provide certain fault information, in the proposed joint FTFD scheme, fault diagnosis is carried out under the always-on fault-tolerant framework to ensure system reliability and performance over the whole operation process. The multi-timescale technique is used to compensate for actuator failure and achieve fault-tolerant control. Under the fault-tolerant framework, actuator faults can be detected and both the location and severity of the faults are decided via monitoring the online identification results of the motor force constants. However, one limitation of the proposed method is the influence of sensor noise on high gain filters in real world application, which might degrade fault-tolerant performance. For the future research, one significant work is to carry out real vehicle tests to promote the proposed algorithm to practical application. Also, fault diagnosis and fault-tolerant control for suspension sensor faults is another valuable topic which deserves our efforts. Given that the proposed fault-tolerant framework is independent on a particular control system or a specific type of nominal controller, the fault-tolerant design idea can be transplanted to a broad range of systems besides addressing suspension actuator faults.

In this paper, an independent observers-based fault diagnosis method is proposed for multiple sensor faults of the full-vehicle active suspension system with an inaccurate model. To address the problem of model uncertainties brought by the linearized full-vehicle suspension model, unmodelled dynamics, parametric uncertainties, and external disturbances are first combined into an integrated uncertain term. Disturbance observers are designed to online track the integrated uncertain terms, whose estimates will be employed to decrease the influence of model uncertainties on fault diagnosis. An independent fault diagnosis observer is designed for each sensor separately, where only the measurement of the matched sensor is taken as the observer input. In this way, each fault diagnosis observer works independently and interactions between measurements of multiple faulty sensors can be decoupled to locate the sensor faults. Anomalies of each sensor are monitored by the fault diagnosis observer in a one-to-one relationship such that fault detection and isolation can be realized at the same time. In particular, the sensitivity and robustness of the fault diagnosis method are improved with the estimates of the integrated uncertain terms injected into the fault diagnosis observers to emulate model uncertainties. Effectiveness of the proposed scheme has been validated via simulation results considering multiple sensor faults occurring at different time or simultaneously. Note to Practitioners—Good reliability of vehicle subsystems is demanded to provide stable rideability and high maneuverability for vehicles, especially for electric vehicles. To guarantee the reliability of the essential components in practice motivates us to focus on the fault diagnosis problems for sensors of vehicle suspension systems. In this paper, considering the possible scenario of multiple sensor faults, an independent observers-based fault diagnosis scheme is designed for full-vehicle suspension systems. By using the proposed method, we can detect the sensor failure and isolate the faulty sensors simultaneously. An independent fault diagnosis observer is built for each sensor with its single sensor measurement taken as the input of the observer. In this way, interactions of the incorrect measurements from the multiple faulty sensors can be removed to avoid interference from these measurement couplings in locating the faulty sensors. Anomalies of each sensor are monitored by its matched fault diagnosis observer in a one-to-one relationship. Different from the existing methods, model uncertainties brought by the linearized full-vehicle suspension model are observed online and their estimates are employed to build the fault diagnosis observers to narrow the gap between the reality and model, and promote the accuracy and effectiveness of the model-based fault diagnosis method. However, incipient sensor faults cannot be effectively diagnosed with our method, which is one drawback of this proposed fault diagnosis scheme. For future work, fault accommodation strategy will be investigated to compensate for the sensor failure and retain satisfactory system performance. Real vehicle experiments will be conducted to test and improve the algorithm, and thus promote the designed method into practical use. The proposed fault diagnosis method for multiple sensor faults can be extended to other mechanical control systems with similar structure of system equations, in addition to vehicle suspension systems.

This paper proposes a resilient model predictive control (MPC) strategy for constrained cyber-physical systems (CPSs) subject to disturbances and cyber attacks. The feedback sensor-controller (S-C) channel suffers from replay attack and the forward controller-actuator (C-A) channel suffers from false data injection (FDI) attack, and the defender has no prior information about the intruder. Considering that the abnormal behavior of intruder cannot be predicted, an expected one-step controllable set, and a series of minimally conservative constraints are developed to build attack detector. Two controllers are designed jointly based on infinite-horizon MPC to mitigate the negative effects caused by attack. Compared with the existing resilient control strategies, the attack model considered in this paper is less conservative, the resilient control structure is simpler, and it can avoid continuous channel refreshing caused by close-range attack. Robust constraint satisfaction, recursive feasibility and uniformly ultimate boundedness (UUB) are ensured for any admissible attack scenario and disturbance realization. Finally, simulations on a supply chain model show the efficacy of the algorithm.Note to Practitioners—With the wide application of wireless networks, vulnerabilities in the communication process can be easily exploited by intruders to launch malicious attacks. Resilient control can provide acceptable robustness and improve system safety. Additionally, many practical systems are constrained and disturbed, and MPC is one of the most effective methods for dealing with control problems in such systems. Its rolling optimization characteristics make it robust to disturbances. On the one hand, both the C-A channel and the S-C channel of the networked control system are vulnerable to attack. On the other hand, in a complex environment, the defender may not have prior information about the attacker, such as attack probability. Therefore, it is not practical to assume that only a single channel is attacked or that the algorithm relies on the attacker’s prior information. In this paper, a resilient MPC control structure is proposed to counter two types of attacks: replay attack and false data injection attack. One of its characteristics is that the attack model’s conservativeness is relatively low, and the structure is relatively simple, making the algorithm more applicable to actual needs. Another feature of the proposed approach is that it can be adapted directly to different types of attacks without modifying the overall resilient MPC architecture.

Category-level object pose estimation is crucial for enabling robot grasping. Currently, many methods rely on 3D shape priors for pose estimation, but obtaining priors for specific categories often requires a significant amount of time to generate. Although some methods do not rely on priors, However, these methods struggle to achieve a balance between speed and accuracy. Achieving fast and accurate category-level object pose estimation remains a challenging issue. In this paper, we propose an algorithm called FAPose, which aims to simultaneously achieve speed and accuracy in category-level object pose estimation without any shape priors. Firstly, we design an RGB-point cloud feature aggregation method based on transformers to fuse RGB and point cloud features. Secondly, we develop a dual-constraint object pose estimation method to effectively leverage both feature space and geometric space information. This approach constructs pose constraints in both feature space and geometric space to achieve optimal pose prediction. Finally, we validate FAPose through benchmark datasets and real robot grasping experiments. The experimental results demonstrate that our proposed method surpasses most existing state-of-the-art pose estimation methods, achieving superior pose estimation performance. The code for FAPose will be available after the paper is accepted for publication.

Estimating the poses of new objects is a challenging problem. Although many methods have been developed for instance-level object pose estimation, they often struggle when faced with new/unfamiliar objects. In this paper, we propose a zero-shot pose estimation method for new objects called ZSPose. We leverage SAM’s zero-shot feature to segment objects in cluttered environments and acquire masks for each object. To facilitate the matching of object masks with object models and categories, we propose a novel object matching strategy that aligns masks with the corresponding object models. Subsequently, based on the derived object masks, we produce object point clouds. Utilizing the RGB images of the objects alongside the point clouds, we present a feature-weight-based method for object pose estimation, achieving accurate pose estimation by predicting the matching weights between the model features and the point cloud features. We conduct performance testing on various instance-level object pose estimation datasets, and experimental results show that our proposed method significantly enhances the accuracy of object pose estimation. It demonstrates excellent generalization, making it applicable to pose estimation for a wide range of new objects. Finally, to validate the practical applicability of ZSPose, we apply it to real-world object pose estimation tasks and robotic grasping tasks. The experimental findings indicate that ZSPose effectively estimates the poses of new objects, assisting robots in performing practical grasping tasks, thus holding considerable practical value.

Transparent objects are commonly found in real life and industrial production. Unlike opaque objects, transparent objects are not easily identifiable in RGB images and often require depth information to determine their position in the image. However, due to the influence of other environmental factors such as reflection and refraction, the depth information of transparent objects is often inaccurate. This leads to difficulties for robots in grasping transparent objects, as incorrect depth information can result in the robot being unable to predict or predict incorrectly the grasping pose. Therefore, it is necessary to complete the depth information for transparent objects. Previous methods for depth completion of transparent objects often struggle to balance accuracy and real-time performance simultaneously. To achieve this goal, in this paper, we propose a transparent object depth completion network called TCRNet based on a cascade refinement structure, which balances accuracy and real-time performance simultaneously. First, the network incorporates a cascade refinement structure in the decoding stage to refine features multiple times, improving the accuracy of depth information. Additionally, an attention module is designed to adjust the extracted features, enabling the network to focus on depth information features in transparent object regions. Finally, a transformer-based error module is implemented in the network’s final output stage to predict and adjust the error between the depth image and the ground truth. TCRNet is trained and tested on three datasets: ClearGrasp, Omniverse Object, and TransCG. It outperforms previous methods in terms of performance. Furthermore, TCRNet is applied to existing grasp detection methods to conduct grasping experiments on transparent objects using a real Baxter robot.Note to Practitioners—With the development of RGB-D camera technology, RGB-D cameras are now widely used in various scenarios such as industrial production, autonomous driving, and robot grasping. However, in certain situations where the camera faces transparent or highly reflective objects, the depth information captured by the camera is often not accurate enough, which can lead to subsequent accidents. Therefore, it is necessary to repair and complete the depth images to achieve accurate understanding of the scene’s depth information. In recent years, with the advancement of deep learning, deep learning-based depth image processing and restoration techniques have been widely applied. In this paper, we propose a high-accuracy network for repairing depth images of transparent objects, which can accurately restore and estimate the depth information of transparent objects in various scenarios. Moreover, experimental results demonstrate that our proposed method can generalize well to other unknown scenes, achieving excellent results.

In this paper, the concept of prescribed-time safety is introduced in the case that initial states are not in a safe set, which requires that the trajectories of systems visit a safe set within the prescribed time and then remain in the safe set. In contrast to safety-critical applications that require trajectories to always be in the safe set, prescribed-time safety control presents a greater challenge due to the introduction of a convergence time constraint. This paper presents a method to ensure prescribed-time safety for robotic systems. First, the prescribed-time control barrier function (PTCBF) is proposed for systems with relative degree of one, and it is extended to prescribed-time high order control barrier function (PTHOCBF) for systems with arbitrary relative degrees. Then, quadratic programming (QP) subject to PTCBF condition is constructed to solve control input. However, the proposed method cannot guarantee safety for systems with uncertain models. To address this limitation, a prescribed-time sliding mode disturbance observer (PTSMDO) is proposed to estimate the uncertainty. The estimated error is close to 0 before the time that states stay in the safe set. Based on the observed value of the uncertainty, the control input is solved by QP. Finally, the effectiveness of the proposed method is verified by a simulation and a physical experiment on Franka Emika robot. Note to Practitioners—This paper is motivated by the challenge encountered in robotic systems, which necessitate the control of the system’s state to reach a predefined safe set and subsequently carry out tasks within this safe set in various practical applications, particularly in the realm of robot motion planning. In comparison to existing methods such as finite-time CBF and fixed-time CBF, the proposed prescribed-time CBF approach in this paper offers the advantage of allowing users to preselect the arrival time. Additionally, given that robot systems are typically subject to uncertainties stemming from parameter errors and external disturbances, this paper also introduces a method to the issue of prescribed-time safety for robot systems featuring uncertain models. The uncertain term within the model can be accurately estimated prior to the moment when the state arrives at the safe set, employing the proposed disturbance observer. Subsequently, the prescribed-time CBF is integrated with the estimation results to construct QP for control input. The simulation and experiment conducted on the Franka Emika robot verify that the proposed method is feasible.

In this work, we investigate the measurement noise sensitivity and peaking problem of the extended state observer (ESO) under high-gain characteristics. For a class ofn-order nonlinear systems with measurement noise, a new variable-order filter and the corresponding filtered ESO (FESO) are constructed to achieve arbitrary suppression ability of measurement noise. Such a salient feature lies in the establishment of the variable-order filter with independent parameters such that the observer poles and the order of the proposed filter can be set arbitrarily by the designer, thereby allowing the user to predefine the suppression strength in prior by adjusting parameters. In addition, a peaking-free FESO is further proposed to eliminate the undesired peaking phenomenon during the transient state. Finally, a comprehensive numerical comparison is presented, and the proposed peaking-free FESO is applied to the drag-free spacecraft hardware in-loop platform. The simulation results reveal the advantages of the proposed scheme in measurement noise suppression and peaking elimination.

In this paper, a framework integrating planning and control is designed to deal with the parallel parking problem of autonomous vehicle. In the planning section, efficient planning is achieved by directly setting target points. In the control part, it is mainly based on nonlinear model predictive control (NMPC) scheme for implementation. Firstly, the parking task is divided into four stages by setting different target points to solve the non-convex state constraint problem in parallel parking. Then, a state-dependent switching law is designed to describe the switching process in different stages based on the different constraints present. In addition, the terminal constraints and terminal cost functions of NMPC scheme are removed to increase the solvability of optimization problems and improve the smoothness of parallel parking for autonomous vehicle. Asymptotic stability and recursive feasibility of autonomous vehicle are ensured. The experimental results show that the NMPC scheme is effective in parallel parking of autonomous vehicle. Note to Practitioners—The motivation of this paper is to solve the problem of parallel parking for autonomous vehicles, with a focus on the design of planning and control framework. In recent years, automatic parallel parking technology has received widespread attention. However, there are still two issues that need further consideration: 1. Most existing planning and control schemes rely on nonlinear optimization processes, but multi-layer nonlinear optimization processes inevitably affect the efficiency and real-time performance of automatic parallel parking schemes. 2. The special construction of parallel parking lots makes it difficult to solve nonlinear optimization problems because the constraint domain is non-convex. To overcome these issues, we first divide the parallel parking process into several stages and directly plan the target points for each stage based on the characteristics of the parallel parking lots, rather than planning the complete parallel parking path. Then, with the help of the prediction and rolling optimization characteristics of the NMPC scheme, the optimal approach to each target point is achieved. In addition, the classic NMPC scheme has been improved (by removing terminal constraints and terminal cost functions) to further enhance its solvability and real-time performance. It is worth noting that the framework of this paper is applicable to most automatic parallel parking needs, as the characteristics of standard parallel parking slots (including size, exit and entrance requirements, safety distance requirements, etc.) are consistent with the planning requirements of the target points in this paper. Moreover, most vehicle models are known, which enables the NMPC scheme in this paper to adapt to different types of autonomous vehicles. In future work, we will further explore the possibility of extending our algorithm to more complex and chaotic environments (including but not limited to the presence of obstacles and external interference) for automatic parking systems.

In this paper, a practically finite-time active disturbance rejection control (PFTADRC) method is proposed for angle tracking on a 3-degree-of-freedom (3-DOF) parallel mechanism of pneumatic muscle actuators (PMAs) with couplings and disturbances. A coupling characterization is presented to determine whether the couplings are beneficial for the system. Based on this, a practically finite-time controller is designed, where detrimental and beneficial couplings are eliminated and remained, respectively. Moreover, when couplings change, the practically finite-time stability of the system is analyzed and guaranteed. Two Lyapunov criteria of practically finite-time stability are proposed to improve convergence rate of the system. Both the bounded region and the settling time are calculated. Finally, the numerical simulation results and experiment results are presented to illustrate the effectiveness of the proposed method.

Hierarchical federated learning (HFL) extends traditional federated learning by introducing a cloud-edge-device framework to enhance scalability. However, the challenge of determining when devices and edges should aggregate models remains unresolved, making the design of an effective synchronization scheme crucial. Additionally, the heterogeneity in computing and communication capabilities, coupled with non-independent and identically distributed ( non-IID) data distributions, makes synchronization particularly complex. In this paper, we proposeRobin, a learning-based synchronization scheme for HFL systems. By collecting data such as models' parameters, CPU usage, communication time,etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. Meanwhile, we prove the convergence ofRobin's synchronization scheme. And we build an HFL testbed and conduct the experiments with real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experiments under various settings are conducted to confirm the effectiveness ofRobin, which can improve 31.2% in model accuracy while reducing energy consumption by 36.4%.

COVID-19 has been spreading globally from 2019 to 2022, prompting many countries to establish consistent, timely, and strict intervention policies during this period. However, the existing analysis of central epidemiological parameters (CPPs) falls short in exploring the consistency of transmission laws implied by these policies. To resolve this limitation, we propose a consistent transmission law inference framework. This framework first provides a coarse-to-fine detection method to extract outbreaks and devises an autonomous inference sliding window (ASW) algorithm to estimate the infected-case-dependent CPPs of these outbreaks. Then, we reveal consistent transmission laws through a differential analysis of the estimated CPPs. Focusing on the transmission of COVID-19 in China, a typical country with a consistent intervention policy, our model reveals remarkably consistent outbreak laws. In short, the policy controls the effective growth rate almost to zero (to a 1E-03 scale) within three days since the outbreak started. Specifically: 1) the policy was very effective at the beginning of the outbreak, leading to the exposure rate hitting the bottom on one day between the second and eleventh days and then keeping it at a plateau; 2) under large-scale nucleic acid testing and contact tracking, the incubation rate decreased and reached a plateau mainly on the third day; and 3) the recovery rate fluctuated extremely little on a 1E-03 scale, showing no significant breakthrough in COVID-19 treatment that helped the policy work. Our method can be readily adapted to other countries and SEIR epidemic.

Deep learning has been extensively applied in medical image segmentation, providing significant support for disease diagnosis. However, traditional encoder-decoder networks struggle with segmenting scale-sensitive point target lesions. To address this challenge, this paper proposes an innovative incremental fusion architecture that can integrate different models and achieve significant performance improvements through complementary fusion. Based on this architecture, we developed Focus-TransUnet3D by combining the Trans-FusionNet3D model and the 3D Unet model. This model adopts a global-to-local segmentation strategy, effectively addressing the challenges of medical point target segmentation, thereby expanding the application of deep learning in the field of medical image processing. Furthermore, we design a deep fusion strategy suitable for the transformer model to adapt to multi-scale feature learning. The integration of the transformer model with convolutional neural networks brings improvements in local and global feature extraction capabilities, enhancing the applicability of our model. We evaluate our model on three clinical datasets with different target scales: the Intracranial Artery dataset, the Intracranial Aneurysm dataset, and the LiTS17 dataset. The results indicate that in the external test for intracranial aneurysm auxiliary diagnosis, the model trained with only 47 annotated samples achieved the state-of-the-art performance, attaining a Dice coefficient of 84.14% and a sensitivity of 100%. This effectively addresses the challenges of annotation scarcity and tiny targets. Our code will be released athttps://github.com/caijilia/FTUnet3D.

Cloud control systems (CCSs) are evolving rapidly, requiring secure communication channels to protect critical remote control tasks. This article introduces an efficient encryption mechanism configured for securing the physical layer communication in these systems while transmitting data over optical fiber networks using orthogonal frequency division multiplexing active optical networks. The proposed scheme takes advantage of the hypersensitive chaotic properties of the Lorenz map to provide robust multidimensional encryption and confidentiality. The encryption process involves arranging bulk encoded codewords in a table structure shape, with each column representing individual codewords encoded by pipelined successive cancellation polar encoding. A Lorenz map is then utilized to generate three distinct chaotic keys, which are employed to reindex the rows and columns of the table structured codeword along with the subcarrier remapping in the constellation map. This reindexing operation provides an additional layer of security against potential brute-force attacks. Performance metrics such as computational efficiency, security robustness, and resistance to various noise and distortion sources are being evaluated. The experimental results demonstrate promising levels of security and resilience against potential threats on the physical layer of communications. The scheme's compatibility with the technical environment ensures seamless integration into existing CCSs infrastructure, making it a resilient solution for securing critical physical layer transmissions.

Inner convex approximation is a compelling method that enables the real-time implementation of suboptimal nonlinear model predictive controls (MPCs). However, it suffers from a slow convergence rate, which prevents suboptimal MPC from achieving better performance within a specific sample time. To address this issue, we first reformulate the conventional inner convex approximation procedure as a root-finding problem for a nonlinear equation. Then, under mild assumptions, a comprehensive functional analysis is performed on the derived nonlinear equation, focusing on its continuity, differentiability, and the invertibility of the Jacobian matrix. Building on this analysis, we propose an improved algorithm that applies Broyden's method to accelerate the root-finding procedure of this derived nonlinear equation, thereby enhancing the convergence rate of the conventional inner convex approximation method. We also provide a detailed analysis of the proposed algorithm's convergence properties and computational complexity, showing that it achieves a locally superlinear convergence rate without devoting much additional computational effort. Simulation experiments are performed in an obstacle avoidance scenario, and the results are compared to the conventional inner convex approximation method to assess the effectiveness and advantages of the proposed approach.

This article presents a novel online obstacle avoidance trajectory planning method for autonomous ground vehicles (AGVs) based on long short-term memory-attention (LSTM-Attention) networks. The proposed method can guide AGVs to perform emergency maneuvers when encountering sudden and moving obstacles, while also ensuring high levels of real-time performance and optimality. It consists of two parts: 1) offline training and 2) online planning. In the offline training phase, an AGV obstacle avoidance trajectory dataset is generated using numerical trajectory optimization methods to train the LSTM-Attention network. This training allows the network to capture the mapping between the relative information of the vehicle and the obstacles and the optimal control actions. The trained network is then used for online trajectory planning to achieve optimal feedback obstacle avoidance control for AGVs facing sudden obstacles. Furthermore, to address situations involving sudden obstacles in different directions and moving obstacles, a rotation coordinate system method is proposed, significantly expanding the application scenarios of the proposed approach. The effectiveness and real-time performance of the designed method are comprehensively validated through extensive simulation and physical experiments.

This article proposes a resilient distributed model predictive control (DMPC) algorithm for a class of constrained dynamically coupled multiple cyber-physical systems (CPSs) subject to bounded additive disturbances. The algorithm is designed to address severe attacks on the forward controller-actuator (C-A) channel, the feedback sensor-controller (S-C) channel, and the channels between subsystems, without any prior information about the intruder available to the defender. To mitigate the negative effects of intruders, we consider a one-step time delay strategy in the local model predictive controller design. This strategy allows the generated controller data to be checked for acceptability before use. To ensure constraint satisfaction for an infinite-horizon MPC problem while accounting for the unknown duration of attacks, we develop a set of minimally conservative constraints in the open-loop control mode using a constraint tightening technique. Moreover, we obtain an equivalent finite number of constraints for the infinite-horizon problem to ensure recursive feasibility. To prevent tampered data from affecting control performance, a detector module is designed to decide whether data is used by its receiver. It is shown that the closed-loop system is uniformly ultimate boundedness (UUB) under any admissible attack scenario and disturbance realization. Finally, the effectiveness of the proposed algorithm is validated by a case study.

This article investigates the attack detection for multisensor cyber-physical systems with both secure and insecure sensors as well as unknown-but-bounded noises based on a zonotopic approach. Based on the intersection of the predicted state set and the secure measurement state set, we construct the security state set to detect attacks on insecure sensors. Then the attacks can be detected by judging whether the intersection of the security state set and insecure measurement state set is empty. Then, we provide a sufficient condition to guarantee that the detection performance of our proposed method is better than that of the predicted state set-based method. Furthermore, we propose a watermarking-based detection method to detect attacks whose amplitude is within the zonotope of the measurement noise, and randomly select the watermarking within a center-constrained zonotope. The watermarking is utilized to ensure that the measurement output with watermarking is not equal to zero, which can enhance the detection performance of our proposed method. Finally, a numerical simulation is provided to demonstrate the effectiveness of theoretical results.

This paper presents a hierarchical economic model predictive control (EMPC) framework for electric vehicles (EVs) to address traffic congestion, energy consumption, and driving safety in tracking control. The framework comprises a higher-level planner and a lower-level controller that act in tandem to achieve driving safety and economic efficiency while ensuring control performance. The higher-level planner employs an event-triggered logic by taking traffic density and economic consumption into account. The resulting optimization problem is only solved to update the reference control input of lower-level controller when the traffic density violates a pre-specified threshold. The lower-level controller ensures driving safety and control performance of EVs by designing adaptive inter-vehicle distance constraints. In addition, the robustness of the EMPC framework is ensured by adopting a robust constraint tightening policy. Recursive feasibility analyses of the optimization problems in both levels of the framework are also conducted. Rigorous proofs of asymptotic average performance and stability analysis are guaranteed for the closed-loop system. The proposed hierarchical EMPC algorithm is demonstrated to be effective and superior in a case study.

This paper addresses the longitudinal control problem of an on-ramp vehicle merging into a platoon on the main road. To tackle this challenge, a finite-time model predictive control (MPC) algorithm with a specialized feedback control law is proposed. A constraint set of the state error is designed and based on this, a decision-making scheme is established to allow the on-ramp vehicle to assess the feasibility of the merging operation at the beginning under the designed MPC strategy. If the merging is feasible, the proposed MPC strategy will be applied to drive the on-ramp vehicle towards a small neighborhood around the desired state on the basis of platoon’s velocity and position within a finite time step before joining the platoon. Furthermore, asymptotic convergence towards the desired state is achieved by a co-designed feedback control law. Otherwise, the MPC strategy will not be triggered, instead an alternative method such as slowing down the on-ramp vehicle to create space and allow the vehicles on the main road to proceed ahead. Under the proposed method, the recursive feasibility of the MPC optimization problem is achieved at all time steps and the finite time convergence to the small neighborhood of the desired state can be proved under the MPC algorithm. An upper bound on the convergence time step is also derived, which is used to prove the effectiveness of the decision-making mechanism. In addition, the closed-loop constraints satisfaction and asymptotic stability of the on-ramp vehicle are also guaranteed. The effectiveness of the proposed MPC method is demonstrated through simulation examples.

Human-machine mixed traffic (HMMT) where autonomous ground vehicles (AGVs) coexist with human-driven vehicles (HDVs), is expected to be the predominant transportation mode in the foreseeable future. To address the multi-objective real-time lane-change trajectory planning problem for AGVs in HMMT, this study proposes a self-attention guided convolutional neural network-long short-term memory network (CNN-LSTM) based framework. This framework introduces an interval type-2 fuzzy physical programming (IT2FPP) method to iteratively solve the trajectory planning problem under varying HDV driving intentions and constructs a dataset using the corresponding HMMT system states and AGV control actions. IT2FPP can handle uncertainties in all boundaries of the preference function and accommodate fuzzy preferences from multiple decision-makers, overcoming the limitations of type-1 fuzzy physical programming. Additionally, a fitting function-based obstacle avoidance method is proposed to model obstacles in the trajectory planning problem with fitting functions, ensuring collision-free trajectories while improving computational efficiency. Then, a self-attention guided CNN-LSTM network is designed to learn the mapping function between the HMMT system states and AGV control actions, enabling real-time trajectory planning with very low computational burden. The network effectively extracts intrinsic features from time series data, demonstrating high accuracy. As HDV driving intentions are incorporated in the dataset, there is no need for inference when generating AGV trajectories in real-time, further enhancing planning efficiency. The framework effectively addresses the multi-objective real-time lane-change trajectory planning problem for AGVs in HMMT, demonstrating high accuracy, real-time performance, and practical application potential, as validated by simulation and physical experiments.

In the realm of the cooperative control of multiagent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound. Online learning, which involves incorporating newly acquired training data into GP models, promises to improve control performance by enhancing predictions during the operation. Therefore, this article investigates the online cooperative learning algorithm for MAS control. Moreover, an event-triggered data selection mechanism, inspired by the analysis of a centralized event-trigger (CET), is introduced to reduce the model update frequency and enhance the data efficiency. With the proposed learning-based control, the practical convergence of the MAS is validated with guaranteed tracking performance via the Lyapunov theory. Furthermore, the exclusion of the Zeno behavior for individual agents is shown. Finally, the effectiveness of the proposed event-triggered online learning method is demonstrated in simulations.

Designing an efficient learning-based model predictive control (MPC) framework for ducted-fan unmanned aerial vehicles (DFUAVs) is a difficult task due to several factors involving uncertain dynamics, coupled motion, and unorthodox aerodynamic configuration. Existing control techniques are either developed from largely known physics-informed models or are made for specific goals. In this regard, this article proposes a compound learning-based MPC approach for DFUAVs to construct a suitable framework that exhibits efficient dynamics learning capability with adequate disturbance rejection characteristics. At the start, a nominal model from a largely unknown DFUAV model is achieved offline through sparse identification. Afterward, a reinforcement learning (RL) mechanism is deployed online to learn a policy to facilitate the initial guesses for the control input sequence. Thereafter, an MPC-driven optimization problem is developed, where the obtained nominal (learned) system is updated by the real system, yielding improved computational efficiency for the overall control framework. Under appropriate assumptions, stability and recursive feasibility are compactly ensured. Finally, a comparative study is conducted to illustrate the efficacy of the designed scheme.

Category-level 6-D object pose estimation plays a crucial role in achieving reliable robotic grasp detection. However, the disparity between synthetic and real datasets hinders the direct transfer of models trained on synthetic data to real-world scenarios, leading to ineffective results. Additionally, creating large-scale real datasets is a time-consuming and labor-intensive task. To overcome these challenges, we propose CatDeform, a novel category-level object pose estimation network trained on synthetic data but capable of delivering good performance on real datasets. In our approach, we introduce a transformer-based fusion module that enables the network to leverage multiple sources of information and enhance prediction accuracy through feature fusion. To ensure proper deformation of the prior point cloud to align with scene objects, we propose a transformer-based attention module that deforms the prior point cloud from both geometric and feature perspectives. Building upon CatDeform, we design a two-branch network for supervised learning, bridging the gap between synthetic and real datasets and achieving high-precision pose estimation in real-world scenes using predominantly synthetic data supplemented with a small amount of real data. To minimize reliance on large-scale real datasets, we train the network in a self-supervised manner by estimating object poses in real scenes based on the synthetic dataset without manual annotation. We conduct training and testing on CAMERA25 and REAL275 datasets, and our experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) techniques in both self-supervised and supervised training paradigms. Finally, we apply CatDeform to object pose estimation and robotic grasp experiments in real-world scenarios, showcasing a higher grasp success rate.

The 6-D pose estimation is a critical work essential to achieve reliable robotic grasping. Currently, the prevalent method is reliant on keypoint correspondence. However, this approach hinges on the determination of object keypoint locations, alongside their detection and localization in real scenes. It also employs the random sample consensus (RANSAC)-based perspective-n-point (PnP) algorithm to solve the pose. Yet, it is nondifferentiable and incapable of backpropagation with loss during the training phase. Alternatively, the direct regression method, while speedy and differentiable, falls short in terms of pose estimation performance, and thus needs enhancement. In view of these gaps, we investigate PPM6D, a new method for 6-D object pose estimation based on regression and point pair matching. Our methodology begins with a proposed cross-fusion module, designed to achieve the fusion and complementation of RGB features and point cloud features. Subsequently, an attention module adjusts the features of the object's 3-D model. Finally, we design a point pair matching module for effective matching of points and characteristics, resulting in an integral matching and fusion. PPM6D is extensively trained and tested utilizing benchmark datasets like LINEMOD, occlusion LINEMOD (LINEMOD-occ), YCB-Video, and T-LESS dataset. Experimental results prove that PPM6D can outperform many keypoint-based pose estimation methods, given its relatively rapid speed, thereby offering novel regression-based pose estimation ideas. When applied to real-world scenarios of object pose estimation tasks and grasp tasks of an actual Baxter robot, PPM6D demonstrates superior performance as compared to most alternatives.

Systems of different types of agents coupled with high-order interactions can emerge complex synchronization patterns. This article presents an analysis framework to study the group synchronization of heterogeneous multiagent systems with hierarchical structure and directed interactions of arbitrary order. First, directed hypergraph is introduced to describe the arbitrary-order interactions among the agents. Then, to explore the possible group synchronization patterns, the agent partition methods commonly used in dyadic graphs are extended to the directed hypergraph. Meanwhile, to verify the admissibility of the group synchronization patterns, two equitable partition types of agent partition are defined from the perspective of hyperedge partition. Next, using the simultaneous block diagonalization method, the stability problem of the group synchronous solution is decoupled into several independent subblocks. These subblocks are associated with the perturbations along and transverse to the synchronous manifold, with transverse subblocks related to loss of synchronization. Therefore, the stability of the group synchronous solution can be investigated by evaluating the stability of each transverse subblock via the master stability function method, thus significantly reducing the stability calculation complexity. Finally, the effectiveness of the analysis framework is illustrated via the simulation.

This article introduces a resilient model predictive control (MPC) approach for constrained cyber-physical systems (CPSs) in the presence of bounded disturbances and denial-of-service (DoS) attacks. An attacker aims to disrupt the communication channel between the controller and actuator (C-A) by deploying adversarial jamming signals. A resilient MPC algorithm is designed, where switching between different cost functions is considered and the control input sequences optimized are used to compensate for information loss caused by DoS attacks. We demonstrate that under certain conditions on the duration of DoS attacks and system parameters, the closed-loop system can be guaranteed to be uniformly ultimately bounded (UUB) in the attack scenario. Moreover, in the nonattack scenario, it exhibits robust asymptotic stability. By properly setting tightened constraints and cost functions, the recursive feasibility of the optimization problem can be ensured for any admissible attack scenario and disturbances realization. The maximum duration of DoS attacks that can be tolerated in the C-A channel is derived by feasibility analysis. Finally, the effectiveness of the designed algorithm is validated through a simulation example, which includes comparisons with two other algorithms.

This article studies the tracking problem for a class of strict-feedback uncertain multi-input–multi-output (MIMO) nonlinear systems, considering both the output constraints and multiple sensor/actuator faults. A novel control approach, named adaptive-neural-backstepping fault-tolerant constrained (ANBFTC) algorithm, is proposed, which incorporates the dynamic surface analysis into the iterative design. A filter-based adaptation coordinate transformation (FBACT) is introduced to define new backstepping iteration variables, eliminating the need for fault amplitudes and bias information. To further address the nonlinear uncertainties inherent in the system, we employ a learning approach, specifically utilizing radial basis function neural networks (RBFNNs), to approximate the uncertainty dynamics. This methodology not only mitigates the computational challenges typically associated with high-order derivatives in iterative designs but also ensures the convergence of tracking errors while adhering to output constraints, even in the presence of multiple sensor/actuator faults. Finally, numerical simulation results are presented to demonstrate the feasibility of the ANBFTC approach.

Although there has been significant progress in category-level object pose estimation in recent years, there is still considerable room for improvement. In this paper, we propose a novel transformer-based category-level 6D pose estimation method called CatFormer to enhance the accuracy pose estimation. CatFormer comprises three main parts: a coarse deformation part, a fine deformation part, and a recurrent refinement part. In the coarse and fine deformation sections, we introduce a transformer-based deformation module that performs point cloud deformation and completion in the feature space. Additionally, after each deformation, we incorporate a transformer-based graph module to adjust fused features and establish geometric and topological relationships between points based on these features. Furthermore, we present an end-to-end recurrent refinement module that enables the prior point cloud to deform multiple times according to real scene features. We evaluate CatFormer's performance by training and testing it on CAMERA25 and REAL275 datasets. Experimental results demonstrate that CatFormer surpasses state-of-the-art methods. Moreover, we extend the usage of CatFormer to instance-level object pose estimation on the LINEMOD dataset, as well as object pose estimation in real-world scenarios. The experimental results validate the effectiveness and generalization capabilities of CatFormer. Our code and the supplemental materials are avaliable at https://github.com/BIT-robot-group/CatFormer.

Unmanned equipment is more and more widely used in complex dangerous scenarios such as emergency rescue. The cooperative operation of manned/unmanned hybrid formation will be the main method at present. This paper proposes a topology analysis and evaluation method of manned/unmanned cooperative equipment systems and constructs a topology model from the logic layer to the physical layer. According to the concepts of degree and betweenness centrality, the key nodes analysis and invulnerability evaluation method of manned/unmanned equipment system are proposed. Finally, the method's effectiveness is verified by simulation, which provides system and data support for dynamic reconstruction and real-time decision-making of manned/unmanned equipment systems.

Cloud-edge collaborative computing paradigm is a promising solution to high-resolution video analytics systems. The key lies in reducing redundant data and managing fluctuating inference workloads effectively. Previous work has focused on extracting regions of interest (RoIs) from videos and transmitting them to the cloud for processing. However, a naive Infrastructure as a Service (IaaS) resource configuration falls short in handling highly fluctuating workloads, leading to violations of Service Level Objectives (SLOs) and inefficient resource utilization. Besides, these methods neglect the potential benefits of RoIs batching to leverage parallel processing. In this work, we introduce Tangram, an efficient serverless cloud-edge video analytics system fully optimized for both communication and computation. Tangram adaptively aligns the RoIs into patches and transmits them to the scheduler in the cloud. The system employs a unique “stitching” method to batch the patches with various sizes from the edge cameras. Additionally, we develop an online SLO-aware batching algorithm that judiciously determines the optimal invoking time of the serverless function. Experiments on our prototype reveal that Tangram can reduce bandwidth consumption and computation cost up to 74.30 % and 66.35 %, respectively, while maintaining SLO violations within 5 % and the accuracy loss negligible.

Probabilistic time series modeling based on generative models has attracted lots of attention because of its wide applications and excellent performance. However, existing state-of-the-art models, based on stochastic differential equation, not only struggle to determine the drift and diffusion coefficients during the design process but also have slow generation speed. To tackle this challenge, we firstly propose decomposable denoising diffusion model ($\text{D}^3\text{M}$) and prove it is a general framework unifying denoising diffusion models and continuous flow models. Based on the new framework, we propose some simple but efficient probability paths with high generation speed. Furthermore, we design a module that combines a special state space model with linear gated attention modules for sequence modeling. It preserves inductive bias and simultaneously models both local and global dependencies. Experimental results on 8 real-world datasets show that $\text{D}^3\text{M}$ reduces RMSE and CRPS by up to 4.6% and 4.3% compared with state-of-the-arts on imputation tasks, and achieves comparable results with state-of-the-arts on forecasting tasks with only 10 steps.

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, Radam, Adabound, and AdaBelief.

With the quick evolution of giant models, the paradigm of pre-training models and then fine-tuning them for downstream tasks has become increasingly popular. The adapter has been recognized as an efficient fine-tuning technique and attracts much research attention. However, adapter-based fine-tuning still faces the challenge of lacking sufficient data. Federated fine-tuning has been recently proposed to fill this gap, but existing solutions suffer from a serious scalability issue, and they are inflexible in handling dynamic edge environments. In this paper, we propose Tomtit, a hierarchical federated fine-tuning system that can significantly accelerate fine-tuning and improve the energy efficiency of devices. Via extensive empirical study, we find that model synchronization schemes (i.e., when edge servers and devices should synchronize their models) play a critical role in federated fine-tuning. The core of Tomtit is a distributed design that allows each edge and device to have a unique synchronization scheme with respect to their heterogeneity in model structure, data distribution and computing capability. Furthermore, we provide a theoretical guarantee about the convergence of Tomtit. Finally, we develop a prototype of Tomtit and evaluate it on a testbed. Experimental results show that it can significantly outperform the state-of-the-art.

Deep reinforcement learning (DRL)-based moving target defense (MTD) emerges as an outstanding method to enhance the security of highly hostile Internet of Things (IoT) environments. However, due to the gap between certain stationary learning environment and real-world, even a well-trained DRL model may not adapt to unknown attacks in the real-world network environments. Therefore, we present a DRL-based self-evolving MTD approach against unknown attacks. First, we formulate the defense in a dynamic network environment as a Markov decision process (MDP), and utilize a DRL model based on actor-critic framework to obtain the optimal sequential defense strategies. Second, we deploy honeypots within the network environments to capture the traffic features of unknown attacks. These features are then specifically labeled to enable the DRL model to learn the characteristics of unknown attacks and identify them. Third, we design an actor network based on ResNet architecture to perceive the network states and make defense decisions in response to these unknown attacks. Finally, through extensive experiments on a platform based on software defined networks, we show that our proposed approach can enhance the defense performance compared with state-of-the-art solutions when dealing with unknown attacks.

The industrial Internet of Things (IoT) landscape is enriched with a diverse array of sensors, which are configured for the real-time monitoring and data collection to improve the production efficiency and optimizing industrial processes. The continual monitoring of IoT systems allows for promptly detection of anomalies in these time series data, thereby minimizing economic losses and ensuring the safe operation of the overall system. Existing deep learning-based methods for time series anomaly detection often rely on the generative models to learn the normal behavior of the data. However, these methods face challenges related to the speed and quality of data generation, ultimately impacting the overall detection performance of the models. In response to these challenges, this article proposes a new unsupervised anomaly detection method named SGFM. It combines state space models and graph neural networks to extract complex spatiotemporal dependencies in time series, which then serve as guidance to facilitate the learning process of a flow matching model, aiming for more refined predictions and consequently enhancing the effectiveness of anomaly detection. The effectiveness of SGFM is validated through the experiments on the three classic time series data sets. The results exhibits a notable improvement of up to 4% compared to the existing methods.

In this article, an indoor positioning system is investigated for an unmanned ground vehicle based on inertial measurement unit (IMU) and ultrawide band (UWB) integration. Measurement outliers of UWB usually exist due to multipath and non-line-of-sight (NLOS) effects. To describe characteristics of stochastic noises well, heavy-tailed noises modeled by Student’s t distributions are considered. By a NLOS identification method, a local modified fusion estimator is designed based on the Kalman filter framework to fuse the measurements of IMU and UWB. A modified matrix-weighted fusion estimator is proposed for high-accuracy estimation and robustness. Sufficient conditions are given for the fusion estimation error to be exponentially bounded in mean square. Finally, effectiveness of the proposed fusion estimator is indicated by experimental results.

Federated learning (FL) is widely used in the Internet of Things (IoT) systems. However, FL is susceptible to backdoor attacks due to its inherently distributed and privacy-preserving nature. Existing studies assume that backdoor triggers on different malicious clients are universal, and most defense algorithms are designed to counter backdoor attacks based on this assumption. Recently, dynamic backdoor attacks have been proposed to undermine robust algorithms in centralized machine learning. We introduce dynamic backdoor attacks into the FL system and develop three types of dynamic backdoors named Aggregation, Single, and Continuous to target the FL system. To defend against such attacks, we propose a novel robust algorithm called MITDBA, which utilizes gramian information to capture high-order representations, then employs spectral signatures to detect and remove malicious clients, and finally utilizes clipping operations to filter the selected local models during the aggregation process. We conduct attack and defense experiments on MNIST, CIFAR-10, and GTSRB data sets. The experimental results demonstrate that our designed attack strategies can successfully insert dynamic backdoors into the global model, bypassing the existing state-of-the-art defenses, but these attacks can be effectively mitigated by MITDBA.

Cloud computing is a potent platform for delivering high-quality computational services to intricate IoT applications. However, effective scheduling approaches are essential to meet application demands while maximizing cloud computing’s potential. In this study, we propose an innovative workflow scheduling method for addressing the cost-effective, deadline-constrained scheduling challenge of IoT applications in cloud computing systems. Our solution, the F-ACO algorithm, leverages a hybrid intelligence approach that combines Ant Colony Optimization (ACO) with a cost-driven heuristic strategy. The primary goal is to minimize workflow scheduling costs while ensuring that workflow deadlines are met. F-ACO introduces a deadline distribution method to derive task sub-deadlines, enabling dynamic adjustments for unscheduled tasks to meet workflow deadlines. Furthermore, we introduce an adaptive ACO-based task ordering mechanism with self-adaptive heuristic information to optimize task scheduling sequences, reducing search space redundancy and enhancing convergence speed. The approach includes a cost-driven task scheduling method designed to allocate each task to a virtual machine with minimal execution cost and idle time, further optimizing the overall workflow scheduling cost. To validate our F-ACO algorithm, we conducted numerous simulations using real-world workflows and compared its performance against state-of-the-art algorithms. Our experimental results affirm F-ACO’s competitive edge in effectively scheduling IoT applications in cloud computing environments.

Data-driven predictive control (DPC) has been studied and applied in various scenarios. However, the challenge of computational efficiency remains. With the development of cloud computing, it provides potential solutions to the computational problem. Hence, this paper proposes a workflow-based fast DPC method in cloud-edge collaborative architecture. First, a workflow construction method of DPC is designed to make full use of the distributed computing ability of cloud computing. Next, to tackle the uncertainty in the cloud workflow processing, we design a cloud-edge collaborative scheme. In this scheme, a edge data-driven disturbance observer is proposed to estimate and compensate the uncertain event with guaranteed UUB stability. Then, An autonomous cloud control experimental system based on container technology is designed and implemented to execute the workflow-based DPC controller. Evaluations demonstrate that computation times are reduced by 45.19$\%$and 74.35$\%$for two real-time control examples, and by a maximum of 85.10$\%$for a high-dimensional control example.Note to Practitioners—This work is motivated by the challenge of the further combination of cloud computing and control system such as DPC. In the existing cloud-based control system, the computation mission of native control algorithm is deployed in a single cloud server directly. However, the structure of cloud environment is distributed, and the existing computation mode could not make full use of the parallel computing of cloud computing. Thus, the computation time could not be reduced significantly, and would still have serious effects on the control quality. In this work, a novel workflow-based DPC system in cloud-edge collaborative architecture is proposed, which decompose the native control mission into distributed cloud workflow with multiple smaller computation tasks. Then, an edge disturbance observer is designed to compensate the uncertainty occurring in the cloud workflow processing. As the evaluations show, the proposed workflow-based control system in cloud-edge collaborative architecture could be applied in the control mission with real-time requirement and the high-dimension control mission with intensive data.

Subspace identification (SID) has been widely used in system identification and control fields, since it can estimate system models while only relying on the input and output data using reliable numerical operations. However, the high-dimension Hankel matrices are involved to store these data and used to obtain the system models, which increases the computation amount of SID and makes SID unsuitable for the large-scale or real-time identification tasks. In this paper, a novel fast SID method based on cloud workflow processing approach and container technology is proposed to accelerate the traditional algorithm. First, a workflow establishment method of SID is designed to match the distributed cloud environment, based on the computational feature of each calculation stage. Second, a containerised cloud workflow processing system is established to execute the logic-and data-dependent SID workflow mission based on the Kubernetes system. Finally, the experiments show that the computation time is reduced by at most$91.6\%$for the large-scale SID mission and decreased to within 20 ms for the real-time mission parameter.Note to Practitioners—Subspace identification has became a widely used method in various fields, including power grids, chemical processing, data-driven control, and fault detection. However, as systems become larger and more complex, the computational challenges increase. To address this issue, this paper proposes a workflow-based method for subspace identification that can be executed in a cloud environment to accelerate the process. This note outlines the steps that practitioners can take to apply this method. The first step is to design a workflow structure as proposed method in this paper. This structure should be customized to fit the specific needs of the practitioner’s application. The second step is to build a containerized cloud workflow processing system that can execute the workflow. This system should be based on the Kubernetes system and designed to handle the specific computational requirements of the workflow. Practitioners who work in fields where computational efficiency is crucial for system identification operations can benefit from the proposed method. By following the steps outlined above, practitioners can streamline the process of subspace identification and achieve improvements in computational efficiency.

With the development of cloud computing, a growing number of workflows are deployed in cloud platform that can dynamically provide cloud resources on demand for users. In clouds, one basic problem is how to schedule workflow under the deadline constraint and minimize the execution cost. As the capability of cloud resources getting higher, the required cost is also rising. Capability of some resources exceeds the need of users, which leads to higher cost, and the budget of users should be considered. In this paper, a novel scheduling algorithm, named DB-ACO, is proposed to minimize the execution cost for the workflow with deadline and budget constraints. DB-ACO is verified on four typical scientific workflows, and the experiments results show it outperforms four state-of-the-art methods, especially for CyberShake.Note to Practitioners—Budget and deadline are important requirements for users in cloud computing, which are used as constraints. Extensive works have been devoted to minimize the cost of workflows execution with different scheduling strategies. However, most of them only consider one single constraint and assume the constraint is simple and loose, which is impractical in actual scenarios due to higher requirement of users. This paper investigates a novel scheduling algorithm DB-ACO to optimize cost under budget and deadline. DB-ACO combines heuristic and meta-heuristic, it uses ant colony optimization to optimize the execution cost under the deadline and budget constraints: each ant sorts tasks on the basis of the combination of the pheromone trail and heuristic information, the deadline and budget are distributed fairly to each task by a novel distribution method, then the service selection rules are introduced to build solution.

Cloud workflow scheduling is a typical combinatorial optimization problem and becomes more challenging due to the increasing diversity of workflows. However, current research employs the same scheduling strategy on diverse workflows. In fact, a scheduling strategy may perform well on one workflow but poorly on other workflows owning to the unique characteristics of each workflow. Therefore, in practical applications, selecting suitable scheduling strategies for diverse workflows is a critical issue. To solve it, this paper investigates a diverse workflows scheduling problem and presents a classification-based workflow scheduling framework, which includes workflow parser, workflow classifier, workflow scheduler, resource manager and workflow status tracker, to manage and schedule diverse workflows using suitable strategies. Based on the framework, we propose a classification-based workflow scheduling algorithm (CWSA) to optimize the economic cost of workflow execution under deadline constraints. We conduct the experiments using diverse workflow instances randomly generated from five types of real-world workflows to evaluate the proposed CWSA approach. The results demonstrate the superiority of CWSA compared with the state-of-the-art approaches. Note to Practitioners—Diverse workflows (i.e., many workflows with various types, such as Montage, LIGO and Cybernetics) in clouds are widespread. How to efficiently schedule them in cloud is very important. This paper formulates the diverse workflows scheduling problem and proposes a CWSA to solve it. The basic idea of CWSA is to select a suitable scheduling strategy for each workflow. Specifically, in CWSA, we design a classification neural network architecture that consists of a graph neural network and a fully connected neural network to classify each workflow to its suitable deadline distribute strategy by its characteristics and deadline constraint. Then CWSA obtains the sub-deadlines of tasks and assigns tasks to appropriate VMs (Virtual Machines). Furthermore, as an important factor in workflow scheduling, the transmission time between dependent tasks is introduced into the graph neural network, which improves the classification accuracy.

In practice, the real-time and accuracy of robotic grasp detection are two very important metrics. In the past, researchers had to sacrifice the real-time nature of the detection network in order to obtain higher detection accuracy. How to make the real-time and accuracy of the network co-exist is a problem worth studying. In order to solve this problem, this paper proposes a network, FANet, based on grasp keypoints, which improves the accuracy of grasp detection while ensuring the real-time performance. The key of this paper is how to quickly and accurately detect grasped keypoints. To this end, this paper proposes a local refinement module that optimizes and de-duplicates each feature of the multi-scale feature map, enabling the network to make full use of the multi-scale features. We also propose a global feature refinement module that allows the network to make better use of global features. We also propose a grasp keypoint optimization module that predicts the offset between the actual keypoints and the predicted keypoints, enabling the network to predict the keypoints more accurately. Moreover, we develop two FANets specifically for grasp detection on CPU and GPU, both of which can accomplish real-time grasp detection in real-world scenes. We complete the training and testing of FANet on the Cornell dataset and the Jacquard dataset, achieving SOTA results on the Jacquard dataset. We also test FANet on a dataset of unknown objects, all with good results. Finally, we use the FANet in grasping experiments with an actual Baxter robot and achieve an average grasping success rate of 96%.Note to Practitioners—Real-time and accuracy are two very important metrics in robotic grasping detection. To achieve high accuracy, more time is often consumed for feature extraction. Similarly, in order to improve the real time performance, we need to reduce the time consumed in the feature extraction process, which may result in a drop in detection accuracy. How to coordinate the relationship between them, so as to have both, is a problem worth investigating. Current methods tend to focus on obtaining higher accuracy and they are willing to spend more time to achieve higher accuracy. But in some practical scenarios, such as on factory assembly lines, objects move fast, and the network needs to be able to detect the grasping position quickly, the real-time performance is more important, which makes some methods difficult to use. In addition, most of the current methods tend to focus on GPU-based robotic grasp detection methods, and in real-world scenarios we may not have such a powerful processing GPU available. In contrast, the CPU is an indispensable unit of the computer that we can use to process images without a high-performance GPU. However, compared to GPUs, the CPUs’ image processing capability is poor, making it difficult to achieve real-time processing. Faced with this situation, the problem of how to achieve real-time and high accuracy in a CPU-only robotic grasp detection network is worth studying, but most of the existing methods ignore this problem. To address these problems, we propose a Fast and Accurate robotic grasp detection Network (FANet), which not only enables the network to combine real-time and accuracy, but also enables real-time detection on CPU or GPU.

Human motion forecasting is an important and challenging task in many computer vision application domains. Recent work concentrates on utilizing the timing processing ability of recurrent neural networks (RNNs) to achieve smooth and reliable results in short-term prediction. However, as evidenced by previous works, RNNs suffer from error accumulation, leading to unreliable results. In this paper, we propose a simple feed-forward deep neural network for motion prediction, which takes into account temporal smoothness between frames and spatial dependencies between human body joints. We design Lightweight Multiscale Spatiotemporal Locally Connected Graph Convolutional Networks (MST-LCGCN) for Single Human Motion Forecasting to implicitly establish the spatiotemporal dependence in the process of human movement, where different scales fuse dynamically during training. The entire model is action-agnostic and follows a framework of encoder-decoder. The encoder consists of temporal GCNs (TGCNs) to capture motion features between frames and locally connected spatial GCNs (SGCNs) to extract spatial structure among joints. The decoder uses temporal convolution networks (TCNs) to maintain its extensibility for long-term prediction. Considerable experiments show that our approach outperforms previous methods on the Human3.6M and CMU Mocap datasets while only requiring much fewer parameters.Note to Practitioners—Accuracy and real-time performance are the two most significant evaluation factors for the challenge of human motion forecasting. Existing methods tend to use models with a huge amount of parameters, sacrificing operation speed to obtain a small increase in accuracy. However, in practical scenarios, the slowdown in speed makes predictions meaningless. Therefore, we propose a lightweight MST-LCGCN network to learn human action patterns over time. To obtain higher accuracy, we extract features from the spatial and temporal dimensions to contain more information; to obtain faster operation speed, we design our network while reducing unnecessary depth as much as possible. We demonstrate the advantages of our model in terms of efficiency and accuracy through extensive quantitative and qualitative experiments on two datasets. Our network will be helpful for robots to avoid obstacles in advance and compensate for network delays, and we will apply them to real life in the future.

As a critical security threat, Byzantine attacks in nodes sending tampered data to their neighbors in networks, significantly undermining the robustness of networked systems. This paper focuses on the leader-follower resilient consensus of continuous-time heterogeneous multiagent systems against Byzantine attacks on nodes. Considering the excessive communication burden in practice, a distributed dynamic event-triggered mechanism is devised to enable intermittent communication among agents. Building upon this mechanism, two protocols are applied to achieve the output consensus when the leader converges to dynamic and static states, respectively. The latter protocol can decrease both communication data and the frequency of event triggers when the leader converges to a static state. In addition, the output regulation control for uncertain system dynamics is achieved by utilizing the technique of internal model principle. Moreover, the designed event-triggered mechanism can be employed to the networks with bounded communication delays. Finally, the effectiveness of the designed protocols is demonstrated by some numerical simulations.

Medical image segmentation is widely used in clinical diagnosis, and methods based on convolutional neural networks have been able to achieve high accuracy. However, it is still difficult to extract global context features, and the parameters are too large to be clinically applied. In this regard, we propose a novel network structure to improve the traditional encoder-decoder network model, which saves parameters while maintaining segmentation accuracy. We improve the feature extraction efficiency by constructing an encoder module that can simultaneously extract local features and global continuity information. A novel attention module is designed to optimize segmentation boundary regions while improving training efficiency. The feature transfer structure of the decoding part is also improved, which fully integrates the features of different levels to restore the spatial resolution more finely. We evaluate our model on seven different medical segmentation datasets, the 2018 Data Science Bowl Challenge (DSBC2018), the 2018 Lesion Boundary Segmentation Challenge (ISIC2018), the Gland Segmentation in Colon Histology Images Challenge (GlaS), Kvasir-SEG, CVC-ClinicDB, Kvasir-Instrument and Polypgen. Extensive experimental results show that our model can achieve good segmentation performance while maintaining a small number of parameters and computational load, which can further facilitate the generalization of the theoretical approach to clinical practice. Our code will be released athttps://github.com/caijilia/ERDUnet.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

This work studies the attitude coordination tracking problem for multiple spacecraft with consideration of unintended faults (communication link faults and actuator faults), inertial uncertainties, and external disturbances under a directed communication graph. A resilient neuroadaptive distributed fixed-time control scheme is investigated to solve this challenging problem. First, an improved adaptive distributed observer is established for followers to estimate the states of the leader when considering communication link faults. The proposed observer improves the resilience against communication link faults. Subsequently, to further cope with the problem of actuator faults, inertial uncertainties, and external disturbances, based on the proposed observer and the technique of adding a power integrator, a neuroadaptive distributed fixed-time attitude coordination controller is developed. Unlike the existing controllers, the proposed one requires less information when dealing with faults and lumped uncertainties, and has a lower-computational cost. Moreover, the fixed-time stability of the closed-loop system is ensured under the designed resilient neuroadaptive distributed control scheme. Finally, comparative simulations are carried out to manifest the effectiveness of the investigated coordination control method.

In this article, switched model predictive control (MPC) is proposed for nonholonomic mobile robots with adaptive dwell time and a dual-terminal set. The dual-terminal set is used to reduce on-line complexity of the switched MPC for the nonholonomic mobile robots with multiple constraints. By a switched signal with the adaptive dwell time, cost functions are switched to improve control performance under multiple constraints. The switched MPC with feasibility and stability can adjust a tradeoff between control performance and computational complexity for the closed-loop system. Simulation results are given to illustrate superiority of the switched MPC for nonholonomic mobile robots.

In this article, the event-triggered fixed-time tracking control is investigated for uncertain strict-feedback nonlinear systems involving state constraints. By employing the universal transformed function (UTF) and coordinate transformation techniques into backstepping design procedure, the proposed control scheme ensures that all states are constrained within the time-varying asymmetric boundaries, and meanwhile, the undesired feasibility condition existing in other constrained controllers can be removed elegantly. Different from the existing static event-triggered mechanism, a dynamic event-triggered mechanism (DETM) is devised via constructing a novel dynamic function, so that the communication burden from the controller to actuator is further alleviated. Furthermore, with the aid of adaptive neural network (NN) technique and generalized first-order filter, together with Lyapunov theory, it is proved that the states of closed-loop system converge to small regions around zero with fixed-time convergence rate. The simulation results confirm the benefits of developed scheme.

Pushing and grasping (PG) are crucial skills for intelligent robots. These skills enable robots to perform complex grasping tasks in various scenarios. These PG methods can be categorized into single-stage and multistage approaches. Single-stage methods are faster but less accurate, while multistage methods offer high accuracy at the expense of time efficiency. To address this issue, a novel end-to-end PG method called efficient PG network (EPGNet) is proposed in this article. EPGNet achieves both high accuracy and efficiency simultaneously. To optimize performance with fewer parameters, EfficientNet-B0 is used as the backbone of EPGNet. Additionally, a novel cross-fusion module is introduced to enhance network performance in robotic PG tasks. This module fuses and utilizes local and global features, aiding the network in handling objects of varying sizes in different scenes. EPGNet consists of two branches dedicated to predicting PG actions, respectively. Both branches are trained simultaneously within a Q-learning framework. Training data is collected through trial and error, involving the robot performing PG actions. To bridge the gap between simulation and reality, a unique PG dataset is proposed. Additionally, a YOLACT network is trained on the PG dataset to facilitate object detection and segmentation. A comprehensive set of experiments is conducted in simulated environments and real-world scenarios. The results demonstrate that EPGNet outperforms single-stage methods and offers competitive performance compared to multistage methods, all while utilizing fewer parameters. A video is available at https://youtu.be/HNKJjQH0MPc.

In this article, we consider a discrete-time Nash equilibrium (NE) seeking problem for graphic game subject to disturbances. For the first-order dynamics, the discrete-time outlier-resistant extended state observer (ESO)-based game strategy is proposed to enable the players to estimate the disturbances under effect of anomaly measurements and then compensate them. An event-triggered mechanism is applied between adjacent players to reduce the frequency of communication. The convergence of the outlier-resistant ESO and control strategy is presented. Moreover, the upper bound of ϵ -NE solution deviating from the unique point of nominal system is given analytically. Then, the addressed issues are extended to high-order game systems. The NE seeking-based control strategy for each player is designed such that the equilibrium point converges to the ϵ -NE which is also analytically calculated. Finally, in order to verify the effectiveness of the proposed game strategy, an example of satellite system is given.

In this article, we consider the "nearest distance" from a given uncontrollable dynamical network to the set of controllable ones. We consider networks whose behaviors are represented via linear dynamical systems. The problem of interest is then finding the smallest number of entries/parameters in the system matrices, corresponding to the smallest number of edges of the networks, that need to be perturbed to achieve controllability. Such a value is called the zero-norm distance to controllability (ZNDC). We show genericity exists in this problem, so that other matrix norms (such as the 2-norm or the Frobenius norm) adopted in this notion are nonsense. For ZNDC, we show it is NP-hard to compute, even when only the state matrices can be perturbed. We then provide some nontrivial lower and upper bounds for it. For its computation, we provide two heuristic algorithms. The first one is by transforming the ZNDC into a problem of structural controllability of linearly parameterized systems, and then greedily selecting the candidate links according to a suitable objective function. The second one is based on the weighted -norm relaxation and the convex-concave procedure, which is tailored for ZNDC when additional structural constraints are involved in the perturbed parameters. Finally, we examine the performance of our proposed algorithms on several typical uncontrollable networks arising in multiagent systems.

This paper aims to address the issue of fixed-time consensus control for uncertain nonlinear multiagent systems (MASs), in which only a group of followers can directly access the leader's information. To ensure the minimum utilization of wireless channel without sacrificing system performance, a fixedtime event-triggered consensus scheme is developed and employed. Firstly, the fixed-time observer with the event-triggered mechanism is proposed to estimate the states of the leader for each follower, which eliminates the unexpected Zeno behavior. Secondly, based on the reconstructed leader's information, a fuzzy fixed-time controller via memory based learning is proposed, in which the fuzzy logic system (FLS) technology is utilized to handle the uncertainty in the MASs. Unlike most FLS based results, the historical memory instead of the single data point is utilized to update the FLS, which improves the learning ability of FLS. Moreover, in the proposed distributed controller, only one FLS parameter is required to be updated for each norder uncertain follower, effectively improving the computational efficiency. With the help of graph theory and Lyapunov stability theory, the fixed-time stability for the entire system is derived. Finally, the validity of the proposed control scheme is illustrated through simulation examples.

In large-scale networks, communication links between nodes are easily injected with false data by adversaries. This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security of the network. Based on the proposed strategy, each sensor can directly exclude suspicious sensors from its neighboring set. First, the problem of selecting suspicious sensors is formulated as a combinatorial optimization problem, which is non-deterministic polynomial-time hard (NP-hard). To solve this problem, the original function is transformed into a submodular function. Then, we propose an attack detection scheduling algorithm based on the sequential submodular optimization theory, which incorporates expert problem to better utilize historical information to guide the sensor selection task at the current moment. For different attack strategies, theoretical results show that the average optimization rate of the proposed algorithm has a lower bound, and the error expectation is bounded. In addition, under two kinds of insecurity conditions, the proposed algorithm can guarantee the security of the entire network from the perspective of the augmented estimation error. Finally, the effectiveness of the developed method is verified by the numerical simulation and practical experiment.

This article proposes a reliable and secured polar coding for the network-oriented cloud control system (NOCCS) in the context of security, reliability, and scalability. A low-complexity variant of the 3-D chaos system is proposed to accomplish internal chaos-encryption-based polar coding for orthogonal frequency division multiplexing over an active power network (OFDM-AON) to ensure the reliability and security of the NOCCS. Internal encryption is achieved by disrupting the correlation of the information bits index and frozen bits index along with the subcarrier by the three chaotic behavioral sequences obtained by Chua's system while encoding polar code. The method is tested on the fiber optic channel between the cloud computing and a local controller placed at a significant distance, thoroughly connected through the fiber optic cable network, and utilizing a 16 quadrature amplitude modulation-OFDM (QAM-OFDM) signal transmission. Our experimental results and performance analysis demonstrate that the proposed encryption scheme produces a 2.5-dB gain in sensitivity on the local controller end at a bit error rate (BER) of 10$^{-3}$compared to the conventional QAM-OFDM. Furthermore, the spacing window for the key distribution is 10$^{75}$, which can be regarded as a protection degree against any brute-force attack from unauthorized optical network units that act as local controllers. Considering the BER enhancement, reliability, and security, the proposed QAM-OFDM-based chaos encryption polar encoding can be ranked as a suitable choice for the NOCCSs data transmission and protection against any brute-force or differential attacks.

Robustness and attacks have become prominent concerns in federated learning (FL)-based Internet of Things (IoT). Our focus primarily lies on robustness, as existing robust algorithms are limited by the data distribution and attacker quantity. Personalized FL has emerged as a paradigm to address data heterogeneity, providing personalized local models for participating clients. In this work, we aim to produce personalized models for clients and defend against backdoor attacks on IoT applications by harnessing personalized FL. We proposePerVK, a personalized FL framework that utilizes virtual learning, personalized learning, and knowledge distillation.PerVKeffectively reduces data heterogeneity and overcomes the limitations imposed by the number of malicious clients and data distributions. Empirical experiments are conducted on CIFAR-10 and GTSRB datasets, considering various attack scenarios, as well as compared the performance ofPerVKwith state-of-the-art baselines. The experimental results demonstrate thatPerVKsuccessfully defends against backdoor attacks and outperforms existing baselines.

This paper focuses on the trajectory planning problem in highly complex and irregular parking lot scenarios. We formulate this problem as an optimal control problem (OCP) according to the requirements of the task and propose a planner to solve it numerically. To ensure optimal planning results, the proposed planner consists of three procedures, which are responsible for generating the guiding route, constructing the collision-free tunnel, and optimizing the trajectory, respectively. Firstly, we use the generalized Voronoi graph (GVG) to build an equidistant roadmap and propose an improved adaptive A* algorithm (IAA*) to improve the time efficiency of the guiding route generation process. Secondly, we employ a coarse trajectory to guide a homotopic route and replace the intractably scaled collision-avoidance constraints with within-tunnel constraints, which are small-scale and independent of the environment’s complexity. Our tunnel construction method ensures the integrity of the free spaces, so that repeated construction is not necessary. Finally, in the process of trajectory optimization, we propose a lightweight OCP iterative solution framework to search for the optimal solution with high computational efficiency, in which the customized OCP with only box constraints is quickly solved in each iteration. Besides, the theoretical analysis, numerical simulations, and real-world experiments had been carried out to demonstrate the effectiveness and efficiency of the method.

In the current research, many researchers have focused on instance-level pose tracking, which requires a 3D model of the object in advance, making it challenging to apply in practice. To address this limitation, some researchers have proposed the category-level object pose tracking method. Achieving accurate and speedy monocular category-level pose tracking is an essential research goal. In this article, we propose CatTrack, a new single-stage keypoints-based monocular category-level multi-object pose tracking network. A significant issue in object pose tracking tasks is utilizing the information from the previous frame to guide pose estimation for the next frame. However, as the object poses and camera information in each frame are different, we need to remove irrelevant information and emphasize useful features. To this end, we propose a transformer-based temporal information capture module to leverage the position information of keypoints from the previous frame. Furthermore, we propose a new keypoint matching module to enable the grouping and matching of object keypoints in complex scenes. We have successfully applied CatTrack to the Objectron dataset and achieved superior results in comparison to existing methods. Furthermore, we have also evaluated the generalization of CatTrack and successfully applied it to track the 6D pose of unseen real-world objects.

This article proposes a novel reinforcement learning-based model predictive control (RLMPC) scheme for discrete-time systems. The scheme integrates model predictive control (MPC) and reinforcement learning (RL) through policy iteration (PI), where MPC is a policy generator and the RL technique is employed to evaluate the policy. Then the obtained value function is taken as the terminal cost of MPC, thus improving the generated policy. The advantage of doing so is that it rules out the need for the offline design paradigm of the terminal cost, the auxiliary controller, and the terminal constraint in traditional MPC. Moreover, RLMPC proposed in this article enables a more flexible choice of prediction horizon due to the elimination of the terminal constraint, which has great potential in reducing the computational burden. We provide a rigorous analysis of the convergence, feasibility, and stability properties of RLMPC. Simulation results show that RLMPC achieves nearly the same performance as traditional MPC in the control of linear systems and exhibits superiority over traditional MPC for nonlinear ones.

This article addresses the event-based fully distributed consensus problem for linear heterogeneous multiagent systems (MASs) subject to input saturation. A leader with unknown but bounded control input is also considered. Based on an adaptive dynamic event-triggered protocol, all the agents can reach output consensus without knowing any global knowledge. Moreover, by applying a multiple-level saturation technique, the input-constrained leader-following consensus control is achieved. The given event-triggered algorithm can be utilized for the directed graph containing a spanning tree with the leader as the root. One distinct feature compared with previous works is that the proposed protocol can achieve saturated control without any a priori condition, instead, the local information is needed. Finally, the numerical simulations are illustrated to verify the performance of the proposed protocol.

Robotic grasping techniques have been widely studied in recent years. However, it is always a challenging problem for robots to grasp in cluttered scenes. In this issue, objects are placed close to each other, and there is no space around for the robot to place the gripper, making it difficult to find a suitable grasping position. To solve this problem, this article proposes to use the combination of pushing and grasping (PG) actions to help grasp pose detection and robot grasping. We propose a pushing-grasping combined grasping network (GN), PG method based on transformer and convolution (PGTC). For the pushing action, we propose a vision transformer (ViT)-based object position prediction network pushing transformer network (PTNet), which can well capture the global and temporal features and can better predict the position of objects after pushing. To perform the grasping detection, we propose a cross dense fusion network (CDFNet), which can make full use of the RGB image and depth image, and fuse and refine them several times. Compared with previous networks, CDFNet is able to detect the optimal grasping position more accurately. Finally, we use the network for both simulation and actual UR3 robot grasping experiments and achieve SOTA performance. Video and dataset are available at https://youtu.be/Q58YE-Cc250.

Enabling path programmability is an essential feature of Software-Defined Networking (SDN). During controller failures in Software-Defined Wide Area Networks (SD-WANs), a resilient design should maintain path programmability for offline flows, which were controlled by the failed controllers. Existing solutions can only partially recover the path programmability rooted in two problems: 1) the implicit preferable recovering flows with long paths and 2) the sub-optimal remapping strategy in the coarse-grained switch level. In this paper, we propose ProgrammabilityGuardian to recover the path programmability of offline flows while maintaining low communication overhead. These goals are achieved through the fine-grained flow-level mappings enabled by existing SDN techniques. ProgrammabilityGuardian configures the flow-controller mappings to recover offline flows with a similar path programmability, maximize the total programmability of the offline flows, and minimize the total communication overhead for controlling these recovered flows. Simulation results of different controller failure scenarios under two different topologies show that ProgrammabilityGuardian recovers offline flows with a balanced path programmability, improves the total programmability of the recovered flows up to 68% and 70%, and reduces the communication overhead by 96% and 99%, compared with the baseline algorithm.

Software-Defined Networking (SDN) is introduced to Wide Area Networks (WANs) to facilitate network operations and management. One main advantage of SDN is path programmability, i.e., the ability to change forwarding path of flows by controlling underlying SDN switches to accommodate traffic variations. However, the SDN controllers may experience unexpected failure and thus lose its path programmability. The typical solution is to let active controllers control offline switches, which are controlled by failed controllers, by establishing the remapping between the offline switches and active controllers. However, existing remapping solutions do not consider the impact of controller failure on network performance and cannot exhibit predictable network performance under controller failure. In this paper, we take Traffic Engineering (TE) as a typical network scenario and propose Traffic Engineering-Aware Controller-switcH remapping rEcoveRy named TEACHER. We introduce Traffic-aware Path Programmability (TPP) as a new metric to describe the impact of controller failure on TE and design TEACHER based on this metric to smartly recover offline switches. Simulation results show that TEACHER can increase the overall TPP by up to 83.0% and improves the load balancing performance by up to 58.2% under Sprintlink topology with relatively low computation time, compared with baselines.

A novel method for positioning accuracy analysis based on non-probabilistic time-dependent reliability is proposed for industrial robots with multisource uncertainties. To overcome the limitation of the probabilistic method in analyzing fewer samples, in this article, we consider uncertain parameters as unknown-but-bounded (UBB). The interval transformation matrix of robots and the uncertain position of the end-effectors are accurately estimated using the proposed method based on the Denavit–Hartenberg (D–H) method for forward kinematics of industrial robots. According to the Delaunay triangulation method combined with the interval method, the positioning accuracy analysis method is investigated to accurately estimate the trajectory bounds, which is more suitable for the complex trajectory of the robotic end-effector. A novel reliability method for positioning accuracy based on a non-probabilistic time-dependent model is proposed to analyze the reliability of positional accuracy in industrial robots. The interval process model and the first-passage theory are applied to constitute the non-probabilistic time-dependent reliability with different dimensions and various thresholds, which are more appropriate for the positioning accuracy analysis of industrial robots with lines, curves, and spiral trajectories. The proposed method is verified using two numerical examples compared with the Monte Carlo simulations (MCS).

As an emerging computing paradigm, edge computing offers computational resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: clients are static and are required to disclose their preferences, which is impractical. To address this issue, we propose a novel sequential computation offloading mechanism, where the ECSP posts prices of computational resources with different configurations to clients in turn. Clients independently choose which computational resources to rent and how to offload based on their prices. Then Egret, a deep reinforcement learning-based approach that achieves maximum revenue, is proposed. Egret determines the optimal price and visiting orders online without infringing on clients’ privacy. Experimental results show that the revenue of ECSP in Egret is only 1.29% lower than Oracle and 23.43% better than the state-of-the-art when the client arrives dynamically.

This study proposes a convex model-based reduced-order model (CMBROM) method for uncertain linear control systems, which ensures the accuracy of the reduced-order model with small samplings. Once the convex bounds of uncertainties are known, the coefficient matrices, input, and output in the uncertain state-space equation are quantified as convex model-based (CMB) parameters. A novel CMB state-space equation is constituted based on an order-extended matrix, which can solve the deterministic and uncertain parts of the state and output using the proposed solution method. The controllability and stability of the CMB state-space system have been proved. The conventional balance system is extended into a convex model format as an important fundamental process for order reduction, and the nominal and uncertain balanced coefficient matrices can be obtained using the transformation matrix. By solving the uncertain controllable and observability matrices using the CMB Lyapunov equation, a convex model perturbation-based singular value decomposition (SVD) is proposed to estimate the uncertain Hankel singular values. Finally, the CMB order-truncated criterion is set using the CMB possibility.

Six-dimensional (6-D) object pose estimation plays a critical role in robotic grasp, which performs extensive usage in manufacturing. The current state-of-the-art pose estimation techniques primarily depend on matching keypoints. Typically, these methods establish a correspondence between 2-D keypoints in an image and the corresponding ones in a 3-D object model. And then they use the PnP-RANSAC algorithm to determine the 6-D pose of the object. However, this approach is not end-to-end trainable and may encounter difficulties when applied to scenarios necessitating differentiable poses. When employing a direct end-to-end regression method, the outcomes are often inferior. To tackle the mentioned problems, we present GR6D, which is a keypoint-and graph-convolution-based neural network for differentiable pose estimation based on RGB-D data. First, we propose a multiscale fusion method that utilizes convolution and graph convolution to exploit information contained in RGB and depth images. Additionally, we propose a transformer-based pose refinement module to further adjust features from RGB images and point clouds. We evaluate GR6D on three datasets: 1) LINEMOD; 2) occlusion LINEMOD; and 3) YCB-Video dataset, and it outperforms most state-of-the-art methods. Finally, we apply GR6D to pose estimation and the robotic grasping task in the real world, manifesting superior performance.

This article investigates the adaptive sliding mode control (ASMC) for a class of nonlinear impulsive hybrid systems with time-varying delay, adopting a creative approach that emphasizes the switching perspective. When both impulse and sliding mode control are involved in the time-delay system, ensuring the continuity of the sliding mode function (SMF) and achieving the reachability of system states become crucial challenges that need to be addressed. In view of this, a novel impulse-based SMF is developed such that the impulsive effect can be avoided on sliding surface, and the difficulty of its continuity is also settled at impulsive instants. By using the state augmented approach, the delay-dependent Lyapunov function with switched systems is formulated to guarantee robust stability of the given sliding mode dynamics. Meanwhile, the designed ASMC law is derived to achieve the finite reachability of switching surface for system states. It is shown that the proposed ASMC law offers a high degree of freedom for adjusting the constants in nonlinear assumptions. Finally, comparative studies are conducted to validate the theoretical results and demonstrate their practical applicability.

Federated learning (FL) enables collaborative model training among distributed devices without data sharing, but existing FL suffers from poor scalability because of global model synchronization. To address this issue, hierarchical federated learning (HFL) has been recently proposed to let edge servers aggregate models of devices in proximity, while synchronizing via the cloud periodically. However, a critical open challenge about how to design a good synchronization scheme (when devices and edges should be synchronized) is still unsolved. Devices are heterogeneous in computing and communication capability, and their data could be non-IID. No existing work can well synchronize various roles (e.g., devices and edge) in HFL to guarantee high learning efficiency and accuracy. In this paper, we propose a learning-based synchronization scheme for HFL systems. By collecting data such as edge models, CPU usage, communication time, etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. We build an HFL testbed and conduct experiments using real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experimental results have confirmed the effectiveness of Hwamei.

Heavy computational load for solving nonconvex problems for large-scale systems or systems with real-time demands at each sample step has been recognized as one of the reasons for preventing a wider application of nonlinear model predictive control (NMPC). To improve the real-time feasibility of NMPC with input nonlinearity, we devise an innovative scheme called cloud-based computational model predictive control (MPC) by using an elaborately designed parallel multiblock alternating direction method of multipliers (ADMMs) algorithm. This novel parallel multiblock ADMM algorithm is tailored to tackle the computational issue of solving a nonconvex problem with nonlinear constraints. It is ensured that the designed algorithm converges to a locally optimal solution of the optimization problem under reasonable assumptions by using the Kurdyka–Łojasiewicz property. With the help of this distributed optimization algorithm, a computational MPC scheme is developed, which can transform the NMPC optimization problem into a set of subproblems only associated with the decision variables at one prediction step. Through the parallel computing algorithm, the computational MPC can deal with large computational loads caused by high-dimensional optimization problems, and improve computational efficiency. Furthermore, to allow for a more efficient implementation of the developed computational MPC and alleviate local calculation loads, a cloud-based computational MPC architecture is devised, which makes significantly better use of computational resources provided by a cloud server. An important advantage of this architecture with Docker container to implement parallelization is that it does not lead to large increases in the solution time regardless of how long the prediction horizon is set. Finally, the developed cloud-based computational MPC architecture is trialed on a group of plug-in hybrid electric vehicles (PHEVs).

Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views into a single kernel. However, existing approaches compute the kernels independently for each view. This ignores complementary information across views and thus may result in a bad kernel choice. In contrast, we propose the Contrastive Multi-view Kernel - a novel kernel function based on the emerging contrastive learning framework. The Contrastive Multi-view Kernel implicitly embeds the views into a joint semantic space where all of them resemble each other while promoting to learn diverse views. We validate the method's effectiveness in a large empirical study. It is worth noting that the proposed kernel functions share the types and parameters with traditional ones, making them fully compatible with existing kernel theory and application. On this basis, we also propose a contrastive multi-view clustering framework and instantiate it with multiple kernel k-means, achieving a promising performance. To the best of our knowledge, this is the first attempt to explore kernel generation in multi-view setting and the first approach to use contrastive learning for a multi-view kernel learning.

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance.Note to Practitioners—This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

Nowadays, more and more workflow applications with different computing requirements are migrated to clouds and executed with cloud resources. Workflow scheduling becomes a critical problem in the cloud environment, which focuses on meeting various quality of service (QoS) constraints. Workflow reliability and energy consumption are two essential parts in clouds and minimizing energy consumption for scheduling workflow with the reliability constraint is a challenging issue. In response to the challenge, we propose a workflow scheduling algorithm named REWS to reduce energy consumption and satisfy workflow reliability constraints. In REWS, a new sub-reliability constraint prediction strategy is adopted to break down the workflow reliability constraint to task sub-reliability constraints and the effectiveness of this strategy is proved. Moreover, an update method is adopted to adjust the task sub-reliability constraint for reducing energy consumption. In addition, a brief system framework which consists of five parts: workflow analyzer, reliability decomposer, resource manager, workflow scheduler and feedback processer is built to support the algorithm implementation of REWS. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed REWS approach. The results demonstrate the superiority of REWS as compared with the state-of-the-art algorithms.Note to Practitioners—Workflow scheduling is a challenging issue in emerging trends of the cloud environment that focuses on satisfying various QoS constraints. In this paper, we investigate a reliability-aware and energy-efficient workflow scheduling problem in cloud computing. A novel workflow scheduling algorithm called REWS, is designed to reduce the energy consumption and meet the workfolw reliability constraint. The basic idea of REWS is to divide the workflow reliability constraint into task sub-reliability constraints and schedule tasks with an energy-efficient scheduling strategy. We conduct the experiments to evaluate the proposed REWS and the results demonstrate that REWS outperforms the state-of-the-art algorithms.

Nowadays, more and more workflows with different computing requirements are migrated to clouds and executed with cloud resources. In this work, we study the problem of stochastic multi-workflows scheduling in clouds and formalize this problem as an optimization problem that is NP-hard. To solve this problem, an efficient stochastic multi-workflows dynamic scheduling algorithm called SMWDSA is designed to schedule multi-workflows with deadline constraints for optimizing multi-workflows scheduling cost. The proposed SMWDSA consists of three stages including multi-workflows preprocessing, multi-workflow scheduling and scheduling feedback. In SMWDSA, a novel task sub-deadlines assignment stretagy is design to assign the task sub-deadlines to each task of multi-workflows for meeting workflow deadline constraints. Then, we propose a task scheduling method based on the minimal time slot availability to execution task for minimizing workflow scheduling cost while meetingt workflow deadlines. Finally, a scheduling feedback strategy is adopted to update the priorities and sub-deadlines of unscheduled tasks, for further minimizing workflow scheduling cost. We conduct the experiments using both synthetic data and real-world data to evaluate SMWDSA. The results demonstrate the superiority of SMWDSA as compared with the state-of-the-art algorithms. Note to Practitioners—Workflow scheduling in clouds is significantly challenging due to not only the large scale of workflows but also the elasticity and heterogeneity of cloud resources. Moreover, minimizing workflow scheduling cost and satisfying workflow deadlines are two critical issues in scheduling with cloud resources, especially the uncertainty of workflow arrive time and task execution time are considered. To meet workflow deadlines, it is an effective strategy to decompose workflow deadline constraints into task sub-deadline constraints. To minimize the workflow scheduling cost, each task in a workflow needs to be assigned to their most suitable VMs for execution. This article presents a novel workflow scheduling algorithm to schedule stochastic multi-workflows in clouds for optimizing multi-workflows scheduling cost and meeting workflows deadlines. This algorithm obtains the task sub-deadline constraints based on the characteristics of workflows for meeting the worklfow deadline constraint. Under the premise of meeting task deadlines, it schedules tasks to a VM with minimum the slot time, for minimizing the cost. Case studies based on well-known real-world workflows data sets suggest that it outperforms traditional ones in terms of success and cost of multi-workflows scheduling. It can thus aid the design and optimization of multi-workflows scheduling in a cloud environment. It can help practitioners better manage the scheduling cost and performance of real-world applications built upon cloud services.

Real-time and accuracy are important evaluation metrics of robotic grasp detection algorithms. To further improve the accuracy on the premise of ensuring real-time performance, in this paper, a new Selective Kernel convolution Grasp detection Network (SKGNet) is proposed. Compared with previous methods, the attention mechanism and multi-scale fusion features are integrated into the SKGNet, which makes the network not only pay full attention to the grasp area but also flexibly adjust the grasp area according to the scale of the object, thus effectively distinguishing the object from the background. The SKGNet is trained and tested on the Cornell dataset and the Jacquard dataset, with the accuracy of 99.1% and 95.9% respectively, which is superior to SOTA methods. Moreover, SKGNet’s detection speed has reached 28fps. To demonstrate the performance of SKGNet, comparison studies and ablation experiments are performed in this paper. Finally, the grasp experiments of Baxter robot are also performed to verify the generalization of SKGNet in the actual scene, which achieves an average grasping success rate of 96.5%. Video is available athttps://youtu.be/j07sb_ChzWQ. Note to Practitioners—Autonomous grasping is an very important skill for the robotic systems in the real world. However, due to the low grasp detection accuracy, robotic grasp is still a challenging problem. Although some methods have been developed to improve the grasp detection accuracy, the time efficiency is poor. Grasp detection with good accuracy and efficiency is worthy of further study. In this view, a novel deep learning-based grasp detection network SKGNet is proposed in this paper. It takes RGB-D images as input, trains and tests on public datasets, and finally outputs a series of grasp rectangles. Compared with the existing works, it not only achieves state-of-the-art detection accuracy, but also has high efficiency. To demonstrate the generalization performance and effectiveness, the SKGNet is also tested in the real world, and applied to perform the actual grasp task of Baxter robot. The results show that the SKGNet has good robustness and can detect the unknown objects of different sizes and shapes in the real world well.

This paper addresses the motion planning of the manipulator in task space. To improve the overall trajectory performance, a special motion planning method based on DMPs (Dynamic Movement Primitives) and the modified obstacle-avoiding algorithm is proposed. The proposed method solves the problems of trajectory jitter and inability to avoid obstacles in some scenarios, which are faced by the scheme of steering angle. Besides, it improves the retention of teaching intentions, reduces the loss of free space, and helps the system adapt to the dynamic environment. At the theoretical level, the convergence of the target state has been proved using Lyapunov stability theory-based analysis. The availability of the proposed method is validated and analyzed by performing a series of numerical simulations and Baxter robot experiments. The results indicate that the proposed method can provide reliable solutions for motion planning. Note to Practitioners—From the perspective of demonstration learning, this paper aims to elevate the motion planning of the manipulator in task space, especially in improving the obstacle avoidance performance. The existing DMPs-based motion planning algorithm uses the scheme of steering angle to achieve obstacle avoidance, and the obstacle is regarded as a mesh of points on the boundary. The obstacle-avoiding performance is limited. How to combine the obstacle-avoiding algorithm with the DMPs-based motion planning algorithm more effectively, so as to simultaneously achieve retaining teaching intentions as much as possible, still faces challenges. This paper designs a modified obstacle-avoiding algorithm, which solves the problems of trajectory jitter and inability to avoid obstacles in some circumstances, and improves the retention of teaching intentions. The modified obstacle-avoiding algorithm is also applied to the dynamic environment. The proposed method satisfies Lyapunov stability theory-based analysis, and the experiments on Baxter robot verify the feasibility.

This paper presents a distributed stochastic economic model predictive control (DSEMPC) algorithm for network interconnected systems with dynamic couplings and economic considerations. Each individual subsystem is subject to stochastic disturbances and state chance constraints. Unlike many existing methods, the proposed approach relies only on the expectation information of the disturbance and real-time estimated covariance. To transform chance constraints into the deterministic form, the Cantelli and Hoeffding inequalities are employed. Moreover, a novel robust adaptive tightened term for constraints is designed based on neighbors’ reference nominal states and the estimated time-varying covariance for each subsystem. This term reduces conservativeness and decouples the dynamics, reformulating the chance constraints and ensuring recursive feasibility. The distributed control algorithm is also aimed at optimizing the economic performance whose cost function is not necessarily positive definite or quadratic in the presence of stochastic disturbance. The input-to-state stability in probability (ISSiP) of each subsystem is guaranteed through a tailored Lyapunov function in expectation form. The paper closes with a simulation of data center temperature control, highlighting the proposed results’ effectiveness and the advantage of economic performance optimization.

With the migration of more and more workflows to clouds, the workflow scheduling in clouds (WSC) becomes a critical problem. Although many algorithms have been presented for WSC, there is still room and need for improvement. This paper formulates WSC as a constrained optimization problem that optimizes workflow execution cost within a workflow deadline constraint and proposes a fully hybrid workflow scheduling algorithm, called HPCP-PSO to solve it. Unlike previous works, HPCP-PSO is based on the repeated and alternated execution of two different methods, namely, the heuristic IaaS Cloud Partial Critical Paths (IC-PCP) and meta-heuristic Particle Swarm Optimization (PSO). Moreover, HPCP-PSO incorporates with two novel designs: 1) a new solution encoding strategy not only to sufficiently embody the elasticity of cloud resources, but also to reflect the scheduling relationship between assigned and unassigned tasks; 2) a solution repair strategy on each infeasible lease process to utilize a user-defined deadline more effectively and enhance the solution efficiency of the algorithm. Extensive experiments are conducted on four real-world scientific workflows and the results show that compared with IC-PCP, PSO, and HGSA, the proposed algorithm outperforms them on average by 35.83%, 70.53%, and 87.71% in terms of workflow execution cost.

Recent researches made a breakthrough in visual tracking accuracy. Many trackers benefit from the object state representations and network loss functions, which mine the output space and improve the power of supervision, respectively. Probabilistic regression method models the noises and uncertainties in the annotations. However, advanced trackers with probabilistic regression are not studied sufficiently in the aspect of supervision and the aspect of robustness of evaluation maximization. In this paper, an overlap maximization network in the manner of probabilistic regression is proposed to improve the learning ability of the network and the discriminative ability in the evaluation maximization. Firstly, the probabilistic regression is extended with the intersection over union (IoU) evaluation, which is normalized as a probability density in the regression space. Secondly, the classification probability is added as a branch of the iterative evaluation module to improve the ability of distinguishing objects in the evaluation maximization. Moreover, the two branches are constructed into a joint probabilistic regression task of IoU evaluation, which makes the network learn from two types of ground truth and provide a consistent result with multi-branch outputs. For feature interpretation, the strip pooling network and the space-time memory network are introduced to encode long-range context and provide dynamic features, respectively. Compared to the state-of-the-art probabilistic regression trackers and other advanced trackers, the experiments show that the proposed tracker achieves outstanding performance across the six datasets, including GOT-10k, LaSOT, TrackingNet, UAV123, OTB-100 and VOT2018.

This article proposes the novel concepts of the high-order discrete-time control barrier function (CBF) and adaptive discrete-time CBF. The high-order discrete-time CBF is used to guarantee forward invariance of a safe set for discrete-time systems of high relative degree. An optimization problem is then established unifying high-order discrete-time CBFs with discrete-time control Lyapunov functions to yield a safe controller. To improve the feasibility of such optimization problems, the adaptive discrete-time CBF is designed, which can relax constraints on system control input through time-varying penalty functions. The effectiveness of the proposed methods in dealing with high relative degree constraints and improving feasibility is verified on the discrete-time system of a three-link manipulator.

This article studies the problem of trajectory optimization for autonomous ground vehicles with the consideration of irregularly placed on-road obstacles and multiple maneuver phases. By introducing a series of event sequences, a new multiphase constrained optimal control formulation is constructed to describe the automatic overtaking process. Although existing trajectory optimization techniques can be applied to address the constructed problem, they may suffer from poor or premature convergence issues due to the complexity of the mission formulation. Thus, to offer an effective alternative, a novel desensitized trajectory optimization method is designed and implemented to explore the optimal overtaking maneuver for the AGVs. The proposed method applies a double layer structure, where an enhanced intelligent optimization method is used in the outer layer such that the main inner optimization routine can be boosted by starting at a better reference solution. The algorithm convergence as well as the solution optimality conditions are theoretically analyzed. Numerical results are provided to illustrate the validity of the established formulation. Comparative case studies were executed to demonstrate the quality of the obtained solution and the enhanced performance of the proposed trajectory optimization method.

This paper proposes a distributed control scheme for a platoon of heterogeneous vehicles based on the mechanism of model predictive control (MPC). The platoon composes of a group of vehicles interacting with each other via inter-vehicular spacing constraints, to avoid collision and reduce communication latency, and aims to make multiple vehicles driving on the same lane safely with a close range and the same velocity. Each vehicle is subject to both state constraints and input constraints, communicates only with neighboring vehicles, and may not know a priori desired setpoint. We divide the computation of control inputs into several local optimization problems based on each vehicle’s local information. To compute the control input of each vehicle based on local information, a distributed computing method must be adopted and thus the coupled constraint is required to be decoupled. This is achieved by introducing the reference state trajectories from neighboring vehicles for each vehicle and by employing the interactive structure of computing local problems of vehicles with odd indices and even indices. It is shown that the feasibility of MPC optimization problems is achieved at all time steps based on tailored terminal inequality constraints, and the asymptotic stability of each vehicle to the desired trajectory is guaranteed even under a single iteration between vehicles at each time. Finally, a comparison simulation is conducted to demonstrate the effectiveness of the proposed distributed MPC method for heterogeneous vehicle control with respect to normal and extreme scenarios.

Software-Defined Networking (SDN) promises good network performance in Wide Area Networks (WANs) with the logically centralized control using physically distributed controllers. In Software-Defined WANs (SD-WANs), maintaining path programmability, which enables flexible path change on flows, is crucial for maintaining network performance under traffic variation. However, when controllers fail, existing solutions are essentially coarse-grained switch-controller mapping solutions and only recover the path programmability of a limited number of offline flows, which traverse offline switches controlled by failed controllers. In this paper, we propose FlexibleProgrammabilityMedic (FlexPM) to provide predictable path programmability recovery under multiple controller failures in SD-WANs. The key idea of FlexPM is to approximately realize flow-controller mappings using hybrid SDN/legacy routing supported by high-end commercial SDN switches. Using the hybrid routing, we can recover programmability by selecting a routing mode for each offline flow at each offline switch in a fine-grained way to fit the given control resource from active controllers and release a few control resource of active controllers by reasonably configuring some normal flows under legacy routing mode. Thus, FlexPM can promise ample control resource to improve the recovery efficiency and further effectively map offline switches to active controllers. Simulation results show that FlexPM outperforms existing switch-level solutions by maintaining balanced programmability and increasing the total programmability of recovered offline flows up to 660% under AT&T topology and 590% under Belnet topology.

This article develops a distributed economic model predictive control (EMPC) method which is applied in a group of interconnected linear subsystems subject to unknown bounded disturbances. Multiple subsystems are coupled through the dynamics, and the control objective is to optimize some general performance criteria of the whole system which may take economic considerations into account. First, a two operation modes EMPC optimization problem is formulated, which incorporates the constraints derived from the Lyapunov technique. In the first mode, each subsystem focuses on the optimization of the economic performance while maintaining the state in a certain region. In the second mode, the system states are steered to a neighborhood of a steady state by making use of the Lyapunov-based constraints. Furthermore, a consensus alternating direction method of multipliers (ADMM) is adopted to solve the model predictive control optimization problems with a coupled predicted model constraint in a distributed way. By introducing consensus constraints, the resulting local optimization problem does not depend on real-time optimal solutions from neighboring subsystems and allows subsystems to solve it in parallel. Moreover, the closed-loop system is ensured to be input-to-state stable (ISS) with respect to the disturbances. To demonstrate the effectiveness of the algorithm, we conduct numerical simulations on a thermal power interconnected system.

This article considers the data-driven optimal control problem for unknown linear systems subject to system constraints from a computation efficiency improvement perspective. We propose a novel Computational Data-enabled Predictive Control (CDeePC) algorithm in a cloud environment, built on a recent work DeePC[1]. First, massive input–output data samples are precollected to describe the system input/output behavior through a behavioral systems theory approach, which might result in a large-scale online optimization problem with high dimensional decision variables. To solve it, in CDeePC, a multiblock alternating direction method of multipliers (ADMMs) algorithm is then employed, in which a solution to the original large problem can be calculated by solving iteratively a set of small subproblems. Next, to further improve the robustness of the algorithm, an online feedback CDeePC (OCDeePC) algorithm is proposed by utilizing real-time data samples to better capture the system characterization. Two analytic inversion formulas are derived for fast computation of time-varying controller parameters based on the result at the previous time. After that, we discuss the computational complexity and provide the convergence proof of proposed algorithms. Finally, after designing a cloud-based control architecture and formulating the iterative scheme to compute control actions as a workflow, we construct and deploy the proposed controllers as a service in the cloud. A case study of tracking control of wheeled mobile robots is provided to illustrate the efficacy of the proposed algorithms.

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN’s updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time.Note to Practitioners—Most scientific applications are computation and/or data-intensive and need large-scale or high-performance resources for their execution. More and more scientists use workflows to manage their applications, but how to efficiently run them in the cloud is a big challenge due to their large scale as well as the dynamic characteristics of the elastic and heterogeneous cloud resources. In this article, we develop a novel multiobjective optimization technique for workflow scheduling such that the makespan and cost can be minimized simultaneously. A scoring criterion, dynamic hierarchical structure and its evolutionary rules, and adaptive adjustment strategies are designed to cooperate with each other and increase the search ability and efficiency of the original and widely used NSGA-II. Adequate experiments are conducted to verify the proposed method’s performance, and the experimental results show that it can provide more near-optimal solutions than the existing methods. It can be readily applied for implementing more efficient and effective cloud data centers to execute large-scale scientific workflows.

Cloud container services as the new norm of cloud resource provisioning are more flexible and widely used in workflows scheduling. However, it is challenging to minimize the cost for workflows scheduling in cloud container services, especially when workflows arrive time and tasks execution time are uncertain. In this article, a stochastic hybrid workflows [i.e., off-line batch workflows (DIWs) and online stream workflows (DSWs)] scheduling problem in cloud container services is solved. A stochastic hybrid workflows scheduling system (SHWS), which consists of a workflow analyzer, workflow classifier, runtime estimator, workflow scheduler, and resource manager, is designed to manage and schedule DIWs and DSWs. Based on the SHWS, a stochastic hybrid workflows scheduling algorithm (SHWSA) is proposed to minimize the cost and improve resource utilization. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed SHWSA approach. The results demonstrate the superiority of SHWSA compared with the state-of-the-art algorithms.Note to Practitioners—This article investigates a stochastic hybrid workflows scheduling problem in cloud container services. We propose a stochastic hybrid workflows scheduling algorithm, which is named SHWSA. The SHWSA is designed to jointly schedule off-line batch workflows (DIWs) and online stream workflows (DSWs) for minimizing the cost and improving resource utilization in cloud container services. The basic idea is to assign tasks subdeadlines and prioritize tasks for guaranteeing workflows deadlines constraints and the processing dependence requirements of tasks. Experiments show that SHWSA outperforms some state-of-the-art algorithms.

This article addresses the finite-time attitude formation-containment control problem for networked uncertain rigid spacecraft under directed topology. A unified distributed finite-time attitude control framework, based on the sliding-mode control (SMC) principle, is developed. Different from the current state of the art, the proposed attitude control method is suitable for not only the leader spacecraft but also the follower spacecraft, and only the neighbor state information among spacecraft is required, allowing the resulting control scheme to be truly distributed. Furthermore, the proposed method is inherently continuous, which eliminates the undesired chattering problem. Such features are deemed favorable in practical spacecraft applications. In addition, upon using the proposed neuro-adaptive control technique, the attitude formation-containment deployment can be achieved in finite time with sufficient accuracy, despite the involvement of both the uncertain inertia matrices and external disturbances. The effectiveness of the developed control scheme is confirmed by numerical simulations.

In this article, we propose a novel economic model-predictive control (MPC) algorithm for a group of disturbed linear systems and implement it in a distributed manner. The system consists of multiple subsystems interacting with each other via dynamics and aims to optimize an economic objective. Each subsystem is subject to constraints both on states and inputs as well as unknown but bounded disturbances. First, we divide the computation of control inputs into several local optimization problems based on each subsystem's local information. This is done by introducing compatibility constraints to confine the difference between the actual information and the previously published reference information of each subsystem, which is the key feature of the proposed distributed algorithm. Then, to ensure the satisfaction of both state and input constraints under disturbances, constraints are tightened on the state and the input of nominal systems by considering explicitly the effect of uncertainties. Moreover, based on an overall optimal steady state, a dissipativity constraint and a terminal constraint are designed and incorporated in the local optimization problems to establish recursive feasibility and guarantee stability for the resulting closed-loop system. Finally, the efficiency of the distributed economic MPC algorithm is demonstrated in a building temperature control case study.

This article is concerned with a distributed filtering problem for Markov jump systems subject to the measurement loss with unknown probabilities. A centralized robust Kalman filter is designed by using variational Bayesian methods and a modified interacting multiple model method based on information theory (IT-IMM). Then, a distributed robust Kalman filter based on the centralized filter and a hybrid consensus method called hybrid consensus on measurement and information (HCMCI) is designed. Moreover, boundedness of the estimation errors and the estimation error covariances are studied for the distributed robust Kalman filter.

This article develops a dynamic version of event-triggered model predictive control (MPC) without utilizing any terminal constraint. Such a dynamic event-triggering mechanism takes the advantages of both event- and self-triggering approaches by dealing explicitly with conservatism in the triggering rate and measurement frequency. The prediction horizon shrinks as the system states converge; we prove that the proposed strategy is able to stabilize the system even without any stability-related terminal constraint. Recursive feasibility of the optimization control problem (OCP) is also guaranteed. The simulation results illustrate the effectiveness of the scheme.

Strong nonlinearities, uncertainties, and disturbances present great challenges to the control of the dissolved oxygen (DO) in a wastewater treatment process (WWTP). To deal with those undesired issues, in this article, a scalable-bandwidth extended state observer (SESO) is proposed, and the SESO-based adaptive sliding-mode control (ASMC) is designed. By the SESO, the time-varying total disturbance can be estimated more accurately and compensated more effectively. For the disturbances that are not addressed completely, an ASMC is employed to suppress them. Due to the advantages of both SESO and ASMC, the DO can be regulated more desirably. The benchmark simulation model Number 1 is taken to verify the proposed SESO-based ASMC. Comparative simulation results highlight the advantages of the proposed approach.

In this article, an event-driven output feedback control approach is proposed for discrete-time systems with unknown mismatched disturbances. To estimate the unavailable states and disturbances, a reduced-order extended state functional observer is proposed, and by introducing an event-driven scheduler, the ZOH-based event-driven output feedback disturbance rejection controller is designed, and the stability and disturbance rejection analyses are performed. To further save the network resources, the predictive event-driven output feedback disturbance rejection control approach is proposed, and the stability and disturbance rejection analyses of the systems with predictive control are also conducted. It can be shown that the disturbances are compensated completely in output channels of the systems, and compared with the time-driven control schemes. And event-triggering frequency is greatly reduced with the proposed event-driven control methods. Finally, the effectiveness of the provided control approaches is demonstrated by numerical simulations.

This article studies the reachable set of cyber-physical systems subject to stealthy attacks with the Kullback-Leibler divergence adopted to describe the stealthiness. The reachable set is defined as the set in which both the system state and the estimation error of the Kalman filter reside with a certain probability. The necessary and sufficient conditions of the reachable set being unbounded are given for the finite and infinite time cases, respectively. When the reachable set is bounded, an ellipsoidal outer approximation is obtained by solving a convex optimization problem. An application of this approximation to the safety evaluation is also given. A numerical simulation of an unmanned ground vehicle is presented to demonstrate the effectiveness of the proposed approach.

In this article, we investigate the fixed-time behavioral control problem for a team of second-order nonlinear agents, aiming to achieve a desired formation with collision/obstacle avoidance. In the proposed approach, the two behaviors(tasks) for each agent are prioritized and integrated via the framework of the null-space-based behavioral projection, leading to a desired merged velocity that guarantees the fixed-time convergence of task errors. To track this desired velocity, we design a fixed-time sliding-mode controller for each agent with state-independent adaptive gains, which provides a fixed-time convergence of the tracking error. The control scheme is implemented in a distributed manner, where each agent only acquires information from its neighbors in the network. Moreover, we adopt an online learning algorithm to improve the robustness of the closed system with respect to uncertainties/disturbances. Finally, simulation results are provided to show the effectiveness of the proposed approach.

This article investigates the attitude stabilization problem of a rigid spacecraft with actuator saturation and failures. Two neural network-based control schemes are proposed using anti-saturation adaptive strategies. To satisfy the input constraint, we design two controllers in a saturation function structure. Taking into account the modeling uncertainties, external disturbances, and adverse effects from actuator faults and failures, the first anti-saturation adaptive controller is implemented based on radial basis function neural networks (RBFNNs) with a fixed-time terminal sliding mode (FTTSM) containing a tunable parameter. Then, we upgrade the proposed controller to a fully adaptive-gain anti-saturation version, in order to strengthen the robustness and adaptivity with respect to actuator faults and failures, unknown mass properties, and external disturbances. In the two schemes, all of the designed adaptive parameters are scalars, thus they only require light computational load and can avoid the redesign process of the controller during spacecraft operation. Finally, the feasibility of the proposed methods is illustrated via two numerical examples.

For taking out the adjustment process of sparse auto-encoder for broad learning system, Fenget al.proposed fuzzy broad learning system by replacing the feature nodes of broad learning system with Takagi–Sugeno fuzzy systems. In fuzzy broad learning system, artificial parameters selection of ridge regression might result in the decrease in testing accuracy. To overcome this shortcoming of fuzzy broad learning system, this article builds a novel model of fuzzy broad learning system based on accelerating amount by introducing the accelerating amount into fuzzy broad learning system. The theoretical result on the universal approximation property of fuzzy broad learning system based on accelerating amount is presented. Three experiment studies on the regression problems of UCI, fashion MNIST, and medical MNIST datasets are performed to show the improvement in testing accuracy.

This article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for autonomous ground vehicles (AGVs) during the parking maneuver process. The proposed design utilizes a multilayer structure. In the first layer, a desensitized trajectory optimization method is iteratively performed to establish a set of time-optimal parking trajectories with the consideration of noise-perturbed initial configurations. Subsequently, by using the preplanned optimal parking trajectory data set, several DNNs are trained in order to learn the functional relationship between the system state-control actions in the second layer. To obtain further improvements regarding the DNN performances, a simple yet effective data aggregation approach is designed and applied. These trained DNNs are then utilized as the motion controllers to generate feedback actions in real time. Numerical results were executed to demonstrate the effectiveness and the real-time applicability of using the proposed control scheme to plan and steer the AGV parking maneuver. Experimental results were also provided to justify the algorithm performance in real-world implementations.

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.

Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while meeting the deadline constraint of each workflow. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improve global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.

The sequential fusion estimation for multirate multisensor dynamic systems with heavy-tailed noises and unreliable measurements is an important problem in dynamic system control. This work proposes a sequential fusion algorithm and a detection technique based on Student’s$t$-distribution and the approximate$t$-filter. The performance of the proposed algorithm is analyzed and compared with the Gaussian Kalman filter-based sequential fusion and the$t$-filter-based sequential fusion without detection technique. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective and robust to unreliable measurements. The$t$-filter-based sequential fusion algorithm is shown to be the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm.

This article addresses the problem of stabilizing a continuous-time switched system affected by a completely unknown disturbance, data quantization, and time-varying delay. In the sense of combined dwell-time and average dwell-time, it is assumed that the switching is slow enough. Suppose that the bound of delay is known but the one of disturbance is unknown. An estimation for the bound of disturbance is used to counteract the unknown disturbance. By extending the approach of the delay-free case, a communication and control strategy is developed by introducing a virtual system. On this basis, the exponential decay and practical stability of the closed-loop system are guaranteed by using a Lyapunov function. Two examples are illustrated to show the usefulness of the proposed framework for stability analysis of some classes of switched systems.

In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.

Software-Defined Networking (SDN) promises good network performance in Wide Area Networks (WANs) with the logically centralized control using physically distributed controllers. In Software-Defined WANs (SD-WANs), maintaining path programmability, which enables flexible path change on flows, is crucial for maintaining network performance under traffic variation. However, when controllers fail, existing solutions are essentially coarse-grained switch-controller mapping solutions and only recover the path programmability of a limited number of offline flows, which traverse offline switches controlled by failed controllers. In this paper, we propose ProgrammabilityMedic (PM) to provide predictable path programmability recovery under controller failures in SD-WANs. The key idea of PM is to approximately realize flow-controller mappings using hybrid SDN/legacy routing supported by high-end commercial SDN switches. Using the hybrid routing, we can recover programmability by fine-grainedly selecting a routing mode for each offline flow at each offline switch to fit the given control resource from active controllers. Thus, PM can effectively map offline switches to active controllers to improve recovery efficiency. Simulation results show that PM outperforms existing switch-level solutions by maintaining balanced programmability and increasing the total programmability of recovered offline flows up to 315% under two controller failures and 340% under three controller failures.

Content Delivery Network (CDN) is a popular service to accelerate object transmission by dynamically caching popular objects at cache points near users. Existing video caching schemes for CDN do not consider some important components of Quality of Experience (QoE). In this paper, we jointly consider video quality and popularity to design a new QoE metric called Video Hit Experience (VHE) and propose an efficient video caching algorithm named Hit ExpeRience-based videO caching (HERO) to improve VHE. Preliminary results show that HERO outperforms existing solutions.

Recently, the correlation filter (CF) has been catching significant attention in visual tracking for its high efficiency in most state-of-the-art algorithms. However, the tracker easily fails when facing the distractions caused by background clutter, occlusion, and other challenging situations. These distractions commonly exist in the visual object tracking of real applications. Keep tracking under these circumstances is the bottleneck in the field. To improve tracking performance under complex interference, a combination of least absolute shrinkage and selection operator (LASSO) regression and contextual information is introduced to the CF framework through the learning stage in this article to ignore these distractions. Moreover, an elastic net regression is proposed to regroup the features, and an adaptive scale method is implemented to deal with the scale changes during tracking. Theoretical analysis and exhaustive experimental analysis show that the proposed peak strength context-aware (PSCA) CF significantly improves the kernelized CF (KCF) and achieves better performance than other state-of-the-art trackers.

Constrained autonomous vehicle overtaking trajectories are usually difficult to generate due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularly placed. In this article, a novel swarm intelligence-based algorithm is proposed for producing the multiobjective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process to dynamically balance the local exploitation and global exploration, thereby exploring the tradeoff between objectives more effectively. The performance of using the designed fuzzy adaptive multiobjective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multiobjective optimal overtaking trajectories for autonomous ground vehicles. Moreover, the comparison to other state-of-the-art multiobjective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality Pareto-optimal solutions.

This article is concerned with a distributed state estimation problem over sensor networks. The communication links of the sensor networks are subject to bounded time-varying transmission delays. A distributed Kalman filtering algorithm is designed to estimate the state based on a Kalman consensus filtering algorithm. Moreover, sufficient conditions are derived for convergence on estimation errors and the boundedness of error covariances, respectively. Finally, the effectiveness of the designed algorithm is validated by a simulation example.

This paper investigates an H∞static output feedback for a low-frequency (LF) networked control system (NCS). A decentralized event-triggered scheme (DETS) is proposed to reduce the network communication loads. The LF NCS with the DETS is modeled as a time-delay system with an LF constraint. With this model, H∞performance analysis is developed via an LF integral quadratic constraint method and a generalized Kalman-Yakubovich-Popov lemma. Furthermore, a detailed controller design algorithm is presented to obtain an H∞static output feedback controller for the LF NCS with the DETS via a two-stage approach. The numerical results are provided to demonstrate the effectiveness and superiority of the proposed techniques.

In this paper, event-triggered active model predictive control is investigated for a nonlinear multiagent system (MAS) with packet losses. By designing event-triggered mechanisms which reduce sensing cost, event-triggered conditions are detected at certain sampling instants. The prediction horizons of all agents are selected actively through the event-triggered mechanisms. Selecting a maximal predictive horizon, a common predictive horizon of the nonlinear MAS is obtained to ensure synchronous updating. The Bernoulli distributions are applied to describe the packet losses which are dealt with by model predictive control. Finally, the effectiveness of the proposed algorithm design is verified by a numerical simulation.

In this article, the problem of disturbance rejection control is studied for discrete-time systems within an event-driven control framework. Through a predefined event-driven scheduler, both full- and reduced-order extended state observer (ESO)-based output feedback controllers are designed. With the proposed ESOs, both the disturbance and the system states are estimated, and the controllers are constructed with the estimated disturbance and states. Then, the stability and disturbance rejection analyses are conducted. It can be found that with the established event-driven control approaches, the updating frequency of the controller can be prominently reduced, and the disturbance can also be compensated in the output channels of the systems. Finally, the validity of the established control approaches is illustrated by the numerical simulations.

Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches may not be able to timely install/delete/update flow entries to properly route/reroute flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency and QoS improvement using three techniques: Flow-set Routing, Lazy Rerouting, and Adaptive Rerouting. Flow-set Routing achieves load balancing with a small number of flow entry operations by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry operation on switches. Adaptive Rerouting selectively reroutes flow-sets to maintain load balancing. We built an NS3 based fat-tree network simulation platform to evaluate AggreFlow's performance. The simulation results show that AggreFlow reduces power consumption by about 18%, yet achieving load balancing and improved QoS (low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes.

With the development of the Internet of Things, the data that needs to be processed is increasing rapidly. Therefore, the collaboration of cloud and edge emerges as the times require. Edge nodes are mainly responsible for collecting data, and decide to process the data locally or offload to cloud data centers. Cloud data centers are suitable for data analysis, model training, and managing edge nodes. In this article, we focus on the task assignment problems in collaborative edge and cloud environments and study it in a distributed, non-cooperative environment. An M/M/1 queueing model is established to characterize the task transmission. Because of the multi-core processors, we set an M/M/C queueing model to characterize the task computation. We consider the problem from the perspective of game theory and formulate it into a non-cooperative game among multi-agents (multiple edge data centers) in which each agent is informed with incomplete information (allocation strategies) of others. For each agent, we define a function of the expected cost of tasks as the disutility function, and minimize it subject to the QoS constraint. We analyze the existence of Nash equilibrium and develop a Greedy Energy-aware Algorithm (GEA) to choose active servers using the Limit Searching Algorithm (LSA) to find the ceiling utilization. Then we propose the Best Response Algorithm (BRA) to optimize the utility function. The convergence of the BRA algorithm has been discussed. Finally, the results demonstrate that the BRA algorithm can get a solution close to Nash equilibrium and reach it quickly.

This paper considers the co-design technique of quantization and event-driven control for reducing the negative influences of network on the system performance of networked control systems (NCSs). Event-driven strategy aims at reducing the frequency of transmission, while signal quantization reduces the amounts of data for each transmission. By combining these two techniques, this paper establishes the quantized event-driven control approaches for both the fixed and relative event threshold cases. The system stability and the existence of the minimum interevent interval (MIEI) are analyzed. Finally, the validity of the proposed approaches is illustrated by the numerical simulations.

In this paper, both a trajectory tracking controller and a position controller are investigated for leader-follower trajectory control of quadrotors. A tracking differentiator is designed to generate speed signals for the quadrotors which are composed of a leader and some followers. Based on state errors between the leader and followers, a nonlinear disturbance observer is used in the trajectory tracking controller. A saturation law with anti-trigonometric functions is adopted in the position controller for the leader and followers. Experiment results are given to show the applicability of the proposed method for quadrotors.

In this paper, a predictive cloud control scheme is investigated for a networked multiagent system (NMAS) with quantized signals under denial-of-service (DoS) attacks. An arbitrary region quantizer is applied to take effective values of control signals. A Stackelberg game is adopted to model the DoS attacks which result in attack-induced packet dropouts. Based on cloud computing and predictive control, the predictive cloud control scheme is designed to compensate for network-induced delays and the attack-induced packet dropouts actively. By a “zoom” strategy, sufficient conditions are given to achieve consensus for the NMAS. Effectiveness and computing superiority of the proposed scheme are demonstrated by simulation results.

This article is concerned with the distributed covariance intersection (CI) fusion estimation for cyber-physical systems (CPSs) with delayed measurements and unknown inputs. The measurement transmission is subject to random delays described by a set of independent Bernoulli processes. Based on the provided finite-length buffers, the delayed measurements are retrieved within the corresponding buffer length. By modeling the unknown inputs with a noninformative prior distribution, a local minimum mean square error (MMSE) estimator is derived in the Bayesian framework. Then this result is extended to the multiple sensor scenario, where the sequential CI fusion approach is applied to design a recursively distributed fusion estimator. It is proved that the distributed sequential CI fusion estimator is consistent and performs better than each local estimator in state estimation. An illustrative example is provided to demonstrate the effectiveness of the proposed technique.

This article considers a cyber-physical system with multiple remote state estimation subsystems under denial-of-service (DoS) attack. Suppose that there are multiple distributed estimation systems, in each of which, a sensor monitors the system and sends its local estimation to a remote estimator over one of multiple wireless channels. A DoS attacker emits noise power to jam the wireless channels. A scheduler is installed to dispatch each sensor to transmit information through the specific channel to minimize the total estimation error covariance on account of energy-saving. Whereas, the DoS attacker attempts to jam a channel to realize an opposite objective. With considering interference from other individuals, a multisensor multichannel remote state estimation model is constructed. A myopic policy and a long-term online interaction strategy under varying network environment are investigated and compared by solving a two-player zero-sum game and a Markov game, respectively. Simulations are provided to demonstrate our results.

This article proposes two kinds of terminal sliding mode control (TSMC) strategies for implementing the finite-time attitude tracking of spacecraft under environmental disturbances and model uncertainties. First, the integral disturbance observer (IDO) is designed to estimate the disturbances and uncertainties. Second, the IDO-based continuous TSMC (CTSMC) method is developed to achieve active disturbance rejection and better tracking performance. Third, to further mitigate the chattering, a modified TSMC (MTSMC) method is constructed by employing an adaptive method and incorporating a piecewise smooth function. Finally, simulations are performed to show the feasibility of the proposed TSMC laws.

Deep learning suffers from many notorious issues such as low convergence rate, over-fitting, and time-consuming. To alleviate these problems, an alternative learning framework with a non-iterative training mechanism named Broad Learning System (BLS) was proposed, which randomly assigns the parameters of hidden nodes and frozen them throughout the training process and then obtains its output weights using the ridge regression theory. This training method makes BLS have very high training efficiency. However, using ridge regression to solve the output weights cannot guarantee the stability of the solution in many cases, especially when the number of training samples is large, which may cause over-fitting and instability of BLS models. To solve this problem, we propose an improved BLS with a dense architecture and use the Proportional-Integral-Differential (PID) and Adaptive moment estimation (Adam) to replace the ridge regression operation. The new algorithm is called PID-A-DBLS, and its advantages include: 1) dense architecture can improve the feature extraction ability of the model; 2) using PID and Adam to solve the output weights can avoid the disadvantages of ridge regression. Extensive experimental results on four benchmark data sets show that PID-A-DBLS can achieve much better generalization ability and stability than BLS and its variants.

This paper proposes Adaptive Batch Sizing (ABS) for online federated learning. ABS is an iteration process-efficient solution that adaptively adjusts batch size of the training process at edge nodes. Preliminary results show that ABS maintains training efficiency and accuracy, compared with existing iteration round-efficient solutions.

Cloud computing is emerging as a deployment promising environment for hosting exponentially increasing scientific and social media applications, but how to manage and execute these applications efficiently depends mainly on workflow scheduling. However, scheduling workflows in the cloud is an NP-hard problem and its existing solutions have certain limitations when applied to real-world scenarios. In this paper, a Temporal Fusion Pointer network-based Reinforcement Learning algorithm for multi-objective workflow scheduling (TFP-RL) is proposed. Through adopting reinforcement learning, our algorithm can discover its heuristics over time by continuous learning according to the rewards resulting from good scheduling solutions. To make more comprehensive scheduling decisions as the influence of historical actions, a novel temporal fusion pointer network (TFP) is designed for the reinforcement learning agent, which can improve the quality of our resulting solutions and the ability of our algorithm in dealing with versatile workflow applications. To decrease convergence time, we train the proposed TFP-RL model independently by the Asynchronous Advantage Actor-Critic method and use its resulting model for scheduling workflows. Finally, under a multi-agent reinforcement learning framework, a Pareto dominance-oriented criterion for reasonable action selection is established for a multi-objective optimization scenario. We first train our TFP-RL model by taking randomly generated workflows as inputs to validate its effectiveness in scheduling, then compare our trained model with other existing scheduling approaches through practical compute- and data-intensive workflows. Experimental results demonstrate that our proposed algorithm outperforms the benchmarking ones in terms of different metrics.

Conventional training mechanism for deep learning, which is based on gradient descent, suffers from many notorious issues such as low convergence rate, over-fitting, and time-consuming. To alleviate these problems, a novel deep learning algorithm with a different learning mechanism named Broad Learning System (BLS) was proposed by Prof. C. L. Philip Chen in 2017. BLS randomly selects the parameters of the feature nodes and enhancement nodes during its training process and uses the ridge regression theory to solve its output weights. BLS has been widely used in many fields because of its high efficiency. However, there is a fundamental problem that has not yet been solved, that is, the appropriate value of the parameter λ for the ridge regression operation of BLS is difficult to be set properly, which often leads to the problem of over-fitting and seriously limits the development of BLS. To solve this problem, we proposed a novel Dense BLS based on Conjugate Gradient (CG-DBLS) in this paper, in which each feature node is connected to other feature nodes and each enhancement node is connected to other enhancement nodes in a feed-forward fashion. The recursive least square method and conjugate gradient method are used to calculate the output weights of the feature nodes and enhancement nodes respectively. Experiment studies on four benchmark regression problems from UCI repository show that CG-DBLS can achieve much lower error and much higher stability than BLS and its variants.

Summary Class imbalance makes traditional intrusion detection system have low detection rate (DR) and high false positive rate (FR) for minority class, which is unsuitable for practical needs. In order to improve the DRs and reduce FRs of minority classes, we propose a novel intrusion detection method, which combines convolutional neural networks (CNNs) algorithm with threshold modification method based on receiver operating characteristic (ROC) curve. In this method, we use CNNs as a classifier and modify threshold of the classifier through ROC curve. In addition, NSLKDD dataset and UNSW‐NB15 dataset have been carried out to evaluate the performance of this method. The experimental results illustrate that the proposed method has a better performance no matter in improving DRs or reducing FRs of minority classes.

The objective of this paper is to present an approximation-based strategy for solving the problem of nonlinear trajectory optimization with the consideration of probabilistic constraints. The proposed method defines a smooth and differentiable function to replace probabilistic constraints by the deterministic ones, thereby converting the chance-constrained trajectory optimization model into a parametric nonlinear programming model. In addition, it is proved that the approximation function and the corresponding approximation set will converge to that of the original problem. Furthermore, the optimal solution of the approximated model is ensured to converge to the optimal solution of the original problem. Numerical results, obtained from a new chance-constrained space vehicle trajectory optimization model and a 3-D unmanned vehicle trajectory smoothing problem, verify the feasibility and effectiveness of the proposed approach. Comparative studies were also carried out to show the proposed design can yield good performance and outperform other typical chance-constrained optimization techniques investigated in this paper.

Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multiobjective trajectory planning problem, this paper applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multiobjective continuous-time trajectory optimization problems.

This paper addresses the problem of designing a distributed filter for linear discrete-time networked control systems that suffer from randomly occurring deception attacks and bounded disturbances. A Round-Robin-type protocol is applied to schedule communication between filters due to the communication resource constraint. The discrete-time Wirtinger's inequality is used to derive a sufficient condition that ensures H∞performance of the closed-loop system. A corresponding filter that guarantees the security is designed. Finally, a numerical example of an inverted pendulum system is provided to demonstrate the feasibility of the proposed filter.

This paper studies the problem of designing the optimal deception attack to maximize a utility function with the Kullback-Leibler divergence adopted as a detection constraint. The utility function reflects the goal of pulling the state away from the origin, increasing the cost of the controller and decreasing the cost of the attacker. To analyze the stealthiness of the attack, the attack signal is decomposed into two parts, one of which is strict stealthy. The necessary and sufficient condition is derived for the case that the strict stealthy attack cannot lead to an unbounded benefit. In this case, the linear transformation of the optimal attack is proved to be a Gaussian distribution. With the mean value and covariance of the Gaussian distribution as variables, the original problem is transformed into a new problem which may not be convex. A suboptimal attack policy is provided and the upper bound for the loss of benefit when using the suboptimal attack is also given. A numerical example of unmanned ground vehicle is illustrated to verify the effectiveness of the proposed attack policy.

This article addresses the distributed finite-time attitude tracking control problem for a group of uncertain rigid spacecraft in the presence of unavailable angular velocity under the directed topology condition. First, a finite-time adaptive neural network observer is proposed for each follower to estimate its own unavailable angular velocity. Unlike existing velocity-observer-based design methods, the proposed one does not need the exact knowledge of the system model, and works well for the systems with both vanishing and nonvanishing uncertainties. Further, another finite-time observer is provided to obtain the precise angular velocity information of the dynamic leader in a distributed manner. Based on these two observers and adding a power integrator technique, a continuous distributed finite-time control scheme with only attitude measurements is finally established. A rigorous theoretical proof shows that the entire finite-time stability of the combined observer-controller closed-loop system is ensured. Simulation results illustrate the benefits and effectiveness of the developed control scheme.

In this paper, the security problem for a cloud control system (CCS) is studied. In the CCS, so-called advanced persistent threats (APTs) can be launched by a malicious attacker to reduce the quality of service of cloud and deteriorate the system performance further. To defend against APTs and create a security as a service scheme, a defender needs to allocate defense resource to different units serving to different plants for improving the overall system performance when the CCS accommodates multiple physical plants simultaneously. After observing the defender's action, the attacker decides which serve units to comprise. Considering that both defender and attacker are subject to resource constraints, the interaction of two sides is modeled by a Stackelberg game. The optimal solutions for two players under different types of budget constraints are investigated. Simulation examples and comparison results are provided to verify the main results of this paper.

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time.

This article is concerned with the finite-time tracking control problem for a class of strict-feedback nonlinear systems involving state constraints, unknown nonlinearities, and nonvanishing disturbances. Unlike the literature that mainly focuses on a C0finite-time controller, in this article, a novel C1smooth finite-time adaptive neural network (NN) controller is proposed by employing a smooth switch between the fractional and cubic form state feedback. The proposed controller not only avoids the singularity but also makes it possible to implement the dynamic surface control (DSC) technique. By applying the adaptive NN control technique, together with barrier Lyapunov functions (BLFs) and a generalized first-order filter including both linear and fractional terms, the desired fast finite-time control performance of the closed-loop nonlinear systems can be guaranteed, and meanwhile, the state constraints are never violated. Under the proposed control scheme, the tracking control problems of nonlinear systems with output constraint and full-state constraints are, respectively, discussed. It is explicitly shown that all the internal error signals are driven to converge into small regions in a finite time. Finally, the effectiveness of the control scheme is also confirmed by the applications to the control of a second-order nonlinear system and an uncertain ship autopilot.

In this article, resilient strategy design is investigated for a cloud control system (CCS) subject to jamming attack. In the presence of jammer, signals, including measurements and control inputs between cloud server and physical plant may be interfered, which degrades the signal-to-interferencelus-noise ratio and further leads to packet dropout of signals. A Stackelberg game is applied to depict the interaction of transmitter and jammer. On account of game equilibria of two players, controller is devised for CCS with time-varying delay. A novel cross-layer dynamic pricing mechanism is devised to drive the disturbance attention performance of CCS to a desired region under constrained information and time-varying networks. The simulation results are provided to verify the effectiveness of the proposed methodology.

In this paper, a constrained space maneuver vehicles trajectory optimization problem is formulated and solved using a new three-layer-hybrid optimal control solver. To decrease the sensitivity of the initial guess and enhance the stability of the algorithm, an initial guess generator based on a specific stochastic algorithm is applied. In addition, an improved gradient-based algorithm is used as the inner solver, which can offer the user more flexibility to control the optimization process. Furthermore, in order to analyze the quality of the solution, the optimality verification conditions are derived. Numerical simulations were carried out by using the proposed hybrid solver and the results indicate that the proposed strategy can have better performance in terms of convergence speed and convergence ability when compared with other typical optimal control solvers. A Monte-Carlo simulation was performed and the results show a robust performance of the proposed algorithm in dispersed conditions.

This paper concentrates on the remote state estimation problem for nonlinear systems over a communication-limited wireless sensor network. Because of the non-Gaussian property caused by nonlinear transformation, the unscented transformation technique is exploited to obtain approximate Gaussian probability distributions of state and measurement. To reduce excessive data transmission, uncontrollable and controllable stochastic event-triggered scheduling schemes are developed to decide whether the current measurement should be transmitted. Compared with some existing deterministic event-triggered scheduling schemes, the newly developed ones possess a potential superiority in maintaining Gaussian property of innovation process. Under the proposed schemes, two nonlinear state estimators are designed based on the unscented Kalman filter. Stability and convergence conditions of these two estimators are established by analyzing behaviors of estimation error and error covariance. It is shown that an expected compromise between communication rate and estimation quality can be achieved by properly tuning event-triggered parameter matrix. Numerical examples are provided to testify the validity of the proposed results.

This paper generalizes stability for a high frequency networked control system (NCS) subject to actuator saturation via integral quadratic constraints. A delta operator system with a high frequency constraint is used to model the high frequency NCS. Double-loop stability is treated via outer-loop and interloop feedback configurations for the high frequency NCS. Stability criteria are derived with the high frequency constraint and actuator saturation by a generalized Kalman-Yakubovich-Popov lemma. Numerical results are provided to demonstrate the effectiveness of the proposed techniques in this paper.

In this paper, a countermeasure for wireless networked control systems suffering from jamming attacks is studied by a variable sampling approach. A Stackelberg game framework is utilized to analyze interactions between a smart jammer and a legitimate user. The variable sampling approach is exploited to deal with data packets dropout between a sensor and a controller. Moreover, a resilient variable sampling controller is designed by a delta operator method. Besides, stability conditions are provided for systems with proposed controller. Finally, a numerical simulation is provided to validate the effectiveness of the proposed method.

This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multilayer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework.

In this paper, we study the exponential stabilization problem for continuous-time Takagi-Sugeno fuzzy systems subject to aperiodic sampling. By aiming to transmission reduction, an appropriate aperiodic event-triggered communication scheme with adaptive mechanism is put forward, which covers the existing periodic mechanisms as special cases. For the sake of reduction in design conservativeness, both the available information of sampling behavior and threshold error are fully acquired by constructing a novel time-dependent Lyapunov functional. Then, a new exponential stability criterion is presented to establish the quantitative relationship among the adaptive adjusted event threshold, the decay rate, the upper bound, and the lower bound of variable sampling period, simultaneously. By resorting to a matrix transformation, the corresponding stabilization criterion is further derived by which the sampled-data controller can be obtained. Finally, two illustrative examples are provided to demonstrate the virtue and applicability of proposed design method.

A novel adaptive finite-time control method is investigated for the gyros systems in the presence of external disturbance. The fast nonsingular terminal sliding mode surface (NTSMS) is designed which has all advantages of NTSMS and general sliding mode surface together. Based on adaptive law and Lyapunov stability method, the novel proposed theorems are given and proven, and we can obtain that the synchronization errors converge to a small region including zero in finite time. Furthermore, it can be seen that the new adaptive control algorithm can guarantee that the chaotic gyros systems have some excellent properties, and all the signals are finite-time convergent. Finally, the new control scheme is verified by using a numerical example, and can achieve beneficial synchronization under the effectiveness of external disturbance of the gyros systems.

Great progresses have been achieved on object detection in the wild. However, it still remains a challenging problem due to tiny objects. In this paper, we present a Three-category Classification Neural Network to find tiny faces under complex environments by leveraging contextual information around faces. Tiny faces (within 20×20 pixels) are so fuzzy that the facial patterns are not clear or even ambiguous for detection. To solve this problem, instead of formulating the face detection as a two-category classification task, a novel face detection network is proposed for three-category classification, i.e., normal face, tiny face and background. Moreover, we take full advantage of contextual information around faces and pick good prior anchors to predict good detection on tiny faces. Extensive experiments on two challenging face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of our method.

An optimal distributed fusion estimation problem is concerned in this study for a kind of linear dynamic multirate sensors systems with correlated noise and stochastic unreliable measurements. The system is formulated at the finest scale with multiple sensors at different scales observing a common target independently with different sampling rates. The noise between different sensors is relevant, moreover, is also correlated with the system noise. The authors derive the local state estimation algorithms under the circumstance of total reliable measurements and stochastic unreliable measurements occur occasions, and the optimal distributed Kalman filter fusion algorithm, respectively. The authors provide a simulation example to illustrate the effectiveness and feasibility of the proposed algorithm.

Mobile crowdsensing systems aim at providing various novel sensing applications by recruiting pervasive users with mobile devices, which are now equipped with enriched built-in sensors (e.g., GPS, microphone, camera, gyroscope, accelerometer, etc.). A key factor to enable such systems is substantial participation of large amount of mobile users. In this paper, we focus on the data collection in mobile opportunistic crowdsensing, where the data can be transferred between mobile users via opportunistic device-to-device communications. The goal is to deliver the sensed data from the collector to the corresponding requester, which can maximize the collector's rewards. Here, we assume that the data collection has time-sensitive characteristics, i.e., the reward is time-sensitive. We consider selfish mobile users with rational behaviors, and propose a credit-based incentive-aware mechanism to stimulate mobile users to participate in data collection for mobile opportunistic crowdsensing. Particularly, we propose an effective mechanism to define the expected rewards for the sensed data, and formulate the sensed data trading as a two-person cooperative game, whose solution is obtained through the Nash bargaining theory. Extensive simulations based on both synthetic and real-world mobility traces are conducted to validate the efficiency of our incentive-aware mechanisms.

Cooperative consensus tracking and its -gain performance is investigated in this paper for a class of multiple agent systems (MASs) in the presence of a single high-dimensional leader. Compared with the traditional models for MASs, the inherent dynamics of the leader are allowed to be different with those of the followers in the present framework, which is thus much more favorable in various practical applications. A new kind of distributed controllers associated with a reduced-order state observer are designed for each follower to track the high-dimensional leader under directed switching topology. With the help of -matrix theory and stability analysis methods of switched systems, some efficient criteria are derived for cooperative consensus tracking of MASs without any external disturbance under directed switching topology. Theoretical analysis is further extended to the case of consensus tracking for MASs subject to unknown external disturbances by showing that, a finite -gain performance for tracking errors against external disturbances can be ensured if some suitable conditions are satisfied. At last, the synthesis issue of designing an observer-based controller to achieve a prescribed -gain performance for consensus tracking is studied by using tools from control theory, where the underlying topology is assumed to be undirected and fixed. The effectiveness of theoretical results is verified by performing numerical simulations.

This paper addresses the adaptive control for task-space teleoperation systems with constrained predefined synchronization error, where a novel switched control framework is investigated. Based on multiple Lyapunov-Krasovskii functionals method, the stability of the resulting closed-loop system is established in the sense of state-independent input-to-output stability. Compared with previous work, the developed method can simultaneously handle the unknown kinematics/dynamics, asymmetric varying time delays, and prescribed performance control in a unified framework. It is shown that the developed controller can guarantee the prescribed transient-state and steady-state synchronization performances between the master and slave robots, which is demonstrated by the simulation study.

Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.

This paper addresses the telecoordinated control of multiple robots in the simultaneous presence of asymmetric time-varying delays, nonpassive external forces, and uncertain kinematics/dynamics. To achieve the control objective, a neuroadaptive controller with utilizing prescribed performance control and switching control technique is developed, where the basic idea is to employ the concept of motion synchronization in each pair of master-slave robots and among all slave robots. By using the multiple Lyapunov-Krasovskii functionals method, the state-independent input-to-output practical stability of the closed-loop system is established. Compared with the previous approaches, the new design is straightforward and easier to implement and is applicable to a wider area. Simulation results on three pairs of three degrees-of-freedom robots confirm the theoretical findings.

This paper is concerned with the problem of distributed weighted Kalman filter fusion (DWKFF) for a class of multisensor unreliable networked systems (MUNSs) with correlated noises. The process noise and the measurement noises are assumed to be one-step, two-step cross-correlated, and one-step autocorrelated, and the measurement noises of each sensor are one-step cross-correlated. The stochastic uncertainties in the state and measurements are described by correlated multiplicative noises. The MUNSs suffer measurement delay or loss due to their unreliability. Buffers of finite length are proposed to deal with measurement delay or loss, and an optimal local Kalman filter estimator with a buffer of finite length is derived for each subsystem. Based on the new optimal local Kalman filter estimator, the DWKFF algorithm with finite length buffers has been developed which has stronger fault-tolerance ability. Simulation results illustrate the effectiveness of the proposed approaches.

As communication bandwidth and resources are limited in network-based control systems, in order to reduce superfluous waste, it is necessary to design an event-triggered communication mechanism. In this paper, the problem of event-triggered state estimation is studied for fusion of multiple sensors with correlated noise. The noise of different sensors are cross-correlated and coupled with the system noise of the previous step and the same time step. An optimal state estimation algorithm based on iterative estimation of white noise estimator is presented, which makes full use of the observation information effectively. A numerical example is used to illustrate the effectiveness of the presented algorithm.

The problem of spacecraft attitude tracking control for actuator faults/failures and external disturbances is addressed in this paper. Assuming that information on external disturbances is unknown, a finite time sliding mode (FTSM) control law is developed to handle actuator faults/failures. Under the constraint conditions including the actuator faults and external disturbance, stability analysis shows that the designed controller not only ensures the attitude tracking convergence in finite time, but also has superior to actuator fault tolerance. Finally, in order to illustrate the efficiency and robustness of the proposed control law, the numerical simulations are carried out for spacecraft under actuator faults and disturbances.

Summary The problem of distributed cooperative control for networked multiagent systems is investigated in this paper. Each agent is modeled as an uncertain nonlinear high‐order system incorporating with model uncertainty, unknown external disturbance, and actuator fault. The communication network between followers can be an undirected or a directed graph, and only some of the follower agents can obtain the commands from the leader. To develop the distributed cooperative control algorithm, a prefilter is designed, which can derive the state‐space representation to a newly constructed plant. Then, a set of distributed adaptive neural network controllers are designed by making certain modifications on traditional backstepping techniques with the aid of adaptive control, neural network control, and a second‐order sliding mode estimator. Rigorous proving procedures are provided, which show that uniform ultimate boundedness of all the tracking errors can be achieved in a networked multiagent system. Finally, a numerical simulation is carried out to evaluate the theoretical results.

In this paper, networked predictive control is investigated for planar nonlinear systems with quantization by an extended state observer (ESO). The ESO is used not only to deal with nonlinear terms but also to generate predictive states for dealing with network-induced delays. Two arbitrary region quantizers are applied to take effective values of signals in forward channel and feedback channel, respectively. Based on a "zoom" strategy, sufficient conditions are given to guarantee stabilization of the closed-loop networked control system with quantization. A simulation example is proposed to exhibit advantages and availability of the results.

This paper addresses the adaptive task-space bilateral teleoperation for heterogeneous master and slave robots to guarantee stability and tracking performance, where a novel semi-autonomous teleoperation framework is developed to ensure the safety and enhance the efficiency of the robot in remote site. The basic idea is to stabilize the tracking error in task space while enhancing the efficiency of complex teleoperation by using redundant slave robot with subtask control. To unify the study of the asymmetric time-varying delays, passive/nonpassive exogenous forces, dynamic parameter uncertainties and dead-zone input in the same framework, a novel switching technique-based adaptive control scheme is investigated, where a special switched error filter is developed. By replacing the derivatives of position errors with their filtered outputs in the coordinate torque design, and employing the multiple Lyapunov-Krasovskii functionals method, the complete closed-loop master (slave) system is proven to be state-independent input-to-output stable. It is shown that both the position tracking errors in task space and the adaptive parameter estimation errors remain bounded for any bounded exogenous forces. Moreover, by using the redundancy of the slave robot, the proposed teleoperation framework can autonomously achieve additional subtasks in the remote environment. Finally, the obtained results are demonstrated by the simulation.

This paper addresses the sampled-data stabilization problem for chaotic systems represented by Takagi-Sugeno (T-S) fuzzy models. If the upper bounds for the time derivative of membership functions are available, combining the fuzzy blending for some quadratic functions together with the introduction of some new useful terms, a novel fuzzy time-dependent Lyapunov-Krasovskii functional (LKF) is proposed to fully capture the available characteristics of the actual sampling pattern and membership functions simultaneously. Based on the proposed LKF, a new criterion dependent on the upper bounds for the time derivative of membership functions is presented to guarantee the asymptotic stability of the whole closed-loop system. Moreover, a stability criterion independent of the upper bounds is also provided based on the corresponding common time-dependent LKF. Then, the designed fuzzy sampled-data controller can be synthesized by analyzing the corresponding stabilization conditions. Moreover, a search algorithm is provided to find the optimal tuning parameters. Finally, one practical example of the Lorenz system is given to illustrate that much less conservativeness can be achieved compared with the earlier results by using the corresponding common LKF, and the results can be further improved when adopting the fuzzy time-dependent LKF within large upper bounds.

This brief presents a new guaranteed performance state estimation criterion for delayed static neural networks. To facilitate the use of the slope information about activation function, the estimation error of activation function is separated into two parts for the first time. Then, a novel Lyapunov-Krasovskii functional (LKF) is constructed, which has fully captured the slope information of the activation. Based on the new LKF, a less conservative design criterion of estimator is derived to ensure the asymptotic stability of estimation error system with performance. The desired estimator gain matrices and the performance index are obtained by solving a convex optimization problem. The simulation results show that the proposed method has much better performance than the most recent results.

This paper proposes a stable adaptive fuzzy fault-tolerant attitude-tracking controller for a rigid spacecraft in the presence of unavailable velocities, external disturbance, actuator faults, and actuator saturation. Fuzzy logic systems are applied to approximate the unknown nonlinear function vector, and a fuzzy adaptive observer is designed to estimate the unmeasured velocity of the rigid body. By using the backstepping technique, a novel adaptive fuzzy attitude-tracking fault-tolerant control scheme is developed. It has been testified that this control approach guarantees that all signals of the rigid spacecraft are bounded and the tracking error between the system output and the reference signal converges to a small neighborhood of zero. Simulation examples with constant faults and time-variant faults are provided to show the fault-tolerant effectiveness of the control method.

This paper deals with a consensus tracking problem for multiagent systems (MASs) with Lipschitz-type nonlinear dynamics and directed switching topology. Unlike most existing works where the relative full state measurements of neighboring agents are utilized, it is assumed that only the relative output measurements of neighboring agents are available for coordination. To achieve consensus tracking in the considered MASs, a new class of observer-based protocols is proposed. By appropriately constructing some topology-dependent multiple Lyapunov functions, it is theoretically shown that distributed consensus tracking in the closed-loop MASs equipped with the designed protocols can be ensured if each possible topology contains a directed spanning tree rooted at the leader and the dwell time for the switchings among different topology is less than a derived positive quantity. Interestingly, it is found that the communication topology for observers' states may be independent with that of the feedback signals. The derived results are further extended to the case of directed switching topology with only average dwell time constraints. Finally, the effectiveness of the analytical results is demonstrated via numerical simulations.

In the above paper[1], there are errors regarding the description of(4), and misquotes in Algorithm 1 and 2. In Algorithm 1, the first equation referenced should be (5) and not (40). In Algorithm 2, the first equation referenced should be (40) and not (5). The correction for(4)is as follows:\begin{equation*} \mathcal {L}^{(\sigma (t))}=\left [{\begin{array}{cc} \widetilde {\mathcal {L}}^{(\sigma (t))}& \mathrm {a}^{(\sigma (t))}\\ \mathrm {0}_{N}^{T}& 0 \end{array}}\right ] \tag{4}\end{equation*}where$\widetilde {\mathcal {L}}^{(\sigma (t))}\in \mathbb {R}^{N\times N}$,$\mathrm {a}^{(\sigma (t))}=-[a_{1(N+1)}^{(\sigma (t))},a_{2(N+1)}^{(\sigma (t))},\cdots ,~a_{N(N+1)}^{(\sigma (t))}]^{T}\in \mathbb {R}^{N}$, and$\mathcal {A}^{(\sigma (t))} = [a_{ij}^{(\sigma (t))}]_{(N+1) \times (N+1)}$is the adjacency matrix of$\mathcal {G}^{(\sigma (t))}$.

This paper develops a finite-time control approach for nonlinear teleoperation systems, which is capable of unifying the study of model uncertainties, actuator saturation, and asymmetric time-varying delays in the same framework. First, a novel anti-windup compensator is designed to analyze the effect of actuator saturation. To achieve the finite-time tracking, a nonsmooth generalized switched filter is also investigated. By introducing the anti-windup compensator and the generalized switched filter into the adaptive fuzzy control torque design, a novel finite-time controller is developed. By using the multiple Lypaunov-Krasovskii functionals method, the resulting closed-loop system is state-independent input-to-output practical stable (SIIOpS) and based on this, it is proved to be finite-time SIIOpS. It is shown that the asymptotic convergence of the adaptive estimation errors and the finite-time convergence of the position tracking errors are obtained, whether the robots contact with the human operator/environment or not. Finally, the effectiveness is demonstrated by the simulation results.

In this paper, the finite-time trajectory tracking control problem for Mars landing with disturbances is addressed. To improve the accuracy of Mars landing, a new nonlinear entry guidance law for low lift landers has been developed. The High-Order Sliding Mode Control(HOSMC) for a tracking a reference trajectory is proposed. The guidance method is based on HOSMC theory adapted to the problem of entry guidance. The HOSMC has been adapted to account for the specific 2-sliding modes exhibited by the longitudinal motion of the entry vehicle, using bank angle control for following a reference trajectory. The global stability nature of the HOSMC law is proven by using a Lyapunov-based approach. Extended state observer are applied to estimate the disturbance, by which sliding mode controllers are designed combing the two approaches respectively to force the state variables of the closed loop system to converge to the reference trajectory. Also, simulation results are presented to illustrate the effectiveness of the control strategy.

In this paper, we aim to develop a set of distributed adaptive fuzzy controllers for a group of uncertain nonlinear high-order multi-agent systems in the presence of actuator faults. Firstly, a pre-filter is designed which can derive the state space representation to a newly constructed plant. Design of the control variable will be provided by making certain modifications on traditional backstepping technique with the aid of adaptive control, fuzzy control and a second-order sliding mode estimator. It shows that the effects due to actuator faults, external disturbances and the uncertainties of the model can be compensated with the proposed scheme, and the global boundedness of the tracking errors can also be ensured.

In this work, we propose a time-sensitive incentive-aware mechanism for mobile video offloading by using the idea of crowdsourcing, where video packet holder cooperates with mobile users to deliver video packets to destination. The objective is to maximize video provider and mobile relay users' payoffs. We formulate the interaction among video packet provider and mobile relay users as a two-person cooperative game, where the video packets are treated as commodities. We apply the Nash bargain solution to obtain the optimal cooperation decision and payment. We carry out extensive simulation based on the real-world traces to validate the superiority of our proposed scheme.

This study presents three non‐linear centralised scaled unscented Kalman filter (SUKF) for multisensor data fusion algorithms, which are augmented measurements, measurements weighted and sequential filtering fusion. First, the accuracy analysis of extended Kalman filter (EKF) and SUKF is investigated in detail. Second, through comparing the error covariance traces and the absolute mean estimation errors of X and Y directions of centralised SUKF for multisensor data fusion algorithms with that of centralised EKF for multisensor data fusion algorithms, it can be remarked that the performance of centralised augmented measurements SUKF for multisensor data fusion algorithm is the best one among the six algorithms, which is to say that Algorithm (Iu) shows the best performance in accuracy. Finally, combining and synthetically analysing the running time of six algorithms, it illustrates that Algorithm (Iu) is optimal in comprehensive aspects among six algorithms.

In this paper, an adaptive fast terminal sliding mode control control law (AFTSMCL) is presented to resolve attitude tracking control problem for rigid spacecraft, which can provide finite-time convergence, strong robustness, and fault-tolerant control. Rigorous proof is achieved first. Simulation results are presented to illustrate the effectiveness of presented control law.

Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.

This paper addresses adaptive fuzzy control for multimaster-multislave teleoperation for multiple mobile manipulators carrying a common object in a cooperative manner that subjected to asymmetric time-varying delays and model parameter uncertainties. In the proposed control framework, a novel switched error filtering is designed. By introducing the filtering output in the control torque design, the complete closed-loop master/slave systems are modeled as a special class of switched system that are composed of two subsystems, i.e., the local master (slave) dynamics with well-defined auxiliary variable and the switched error filter subsystem, which is fairly different from the existing subsystem decomposition method. Utilizing the Lyapunov-Krasovskii method, the complete closed-loop master (slave) system is proved to be state-independent input-to-output stable. The proposed scheme has overcome some application limitations existing in the literature. It is shown that the position tracking errors and the parameter estimation errors can remain bounded under the proposed control laws, which are validated by simulation studies.

In this paper, we propose a technique for making IEEE 802.15.4 standard suitable for low-latency deterministic networks for wireless control applications where the cyclic update time of sensor's information is not more than 10 ms. The IEEE 802.15.4 has shown good characteristics for deterministic networks in beacon-enabled mode, but with minimum cyclic update time for personal area network devices not less than 15.36 ms. Moreover, it is unsuitable for hard real-time systems when used in nonbeacon-enabled mode due to the random nature of channel access protocol. The proposed technique employs time-division multiple access (TDMA)-based protocol that works on slightly modified IEEE 802.15.4 in star topology. Each end device in the network transmits its data frame after certain time delay in response to periodic requests from the coordinator. This time delay is optimized for better channel bandwidth utilization and reliable data exchange. While this TDMA-based protocol eliminates the risk of frame collisions to a great extent, medium-access control sublayer modifications reduce the nondeterminism of the network and increase the bandwidth efficiency. A mathematical model is proposed to design and develop a practical communication network with commercial off-the-shelf radios that can predict worst case arrival time of data frames. Experiments were conducted to evaluate the model for the proposed protocol.

This study presents a solution to the interference management scheme and resource allocation strategy for the two‐tier femtocell networks, where the femtocell users (FUEs) share the same frequency band with the existing macrocell users. It is assumed that the FUEs compete for the available spectrum to fulfil their own communication. And the macrocell base station protects itself by pricing the interference from the FUEs, which formulates the Stackelberg game. In this study, two effective pricing schemes, uniform pricing scheme and non‐uniform pricing scheme, combining with admission control are proposed to maximise the revenues and protect the quality of service requirements. The Stackelberg equilibriums for the proposed games are investigated. Besides, a novel distributed interference pricing algorithm is provided for the uniform pricing case. Numerical results show that, in two‐tier femtocell networks with shared spectrum, the proposed pricing schemes are effective in resource allocation and performance protection.

The problem of energy‐efficient state estimation for a discrete‐time linear system, the states of which are measured by multiple sensors and transmitted over wireless channels, is dealt with in this study. The effect of packet loss on estimation performance is considered here, and the packet loss probability depends on time‐varying channel gains, packet lengths and transmitted power of sensors. The data fusion strategy is based on covariance intersection fusion of local state estimates of the observable subsystem of each sensor. The sufficient conditions, imposing constraint on the packet loss probability of each channel, are established by taking into account each observable subsystem structure to guarantee the expectation of the trace of estimation error covariance matrices is exponentially bounded, and the upper bound is given. The conditions obtained in this study can also be utilised to minimise the energy used by each sensor. Simulation examples are provided to demonstrate the effectiveness of the results.

This paper is concerned with the stabilization problem for a class of discrete-time networked control systems (NCSs) with bounded time delays and packet losses. The controlled plant is represented by a Takagi–Sugeno fuzzy model, and both the state feedback control and output feedback control cases are considered. By guaranteeing the decrement of Lyapunov functional at each control signal updating step, a less conservative stability condition for the state feedback NCSs is derived, and the corresponding stabilizing controller design method is also presented. Under an observer-based framework, the output feedback stabilization problem is further studied, where the main contribution is the development of the separation principle for NCSs. Illustrative examples are provided to show the advantage and effectiveness of the developed results.

Mars entry phase is the most challenging part among Mars entry, descent, and landing (EDL). One of the main reasons is that the lander suffers tough tests from the uncertainties, which include atmospheric density, lift over drag ratio, ballistic coefficient, and initial conditions. In this paper, the desensitized extended Kalman filter (DEKF) is introduced. State estimations from DEKF are obtained by minimizing a cost function that is composed of the trace of posterior covariance matrix and the weighted norm of the posterior state estimation error sensitivities (PSEES). By applying inertial measurement unit (IMU), Mars orbiters and Mars surface beacons (MSBs) integrated navigation simulations demonstrate that the introduced DEKF is far less sensitive to uncertain parameters than standard EKF during Mars entry. With the increase of uncertainties one by one, the root-mean-square errors (RMSE) of the states are still convergent in case of DEKF as compared to standard EKF where they become larger. At last, the consistency test is carried on to further validate the proposed DEKF.

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

In this paper, an adaptive fuzzy control is investigated for multilateral teleoperation of two cooperating robotic manipulators that manipulate an object with constrained trajectory/force in the presence of dynamics uncertainties and random network-induced delays. First, the interconnected dynamics that consist of two master robots and cooperating slave robots are formulated. To consider multiple stochastic delays in communication channels, Markov processes are used to model these random network-induced delays. The interconnected dynamics of the teleoperation are divided into a local master/slave position/force subsystem and a stochastic-delayed motion synchronization subsystem. Then, an adaptive fuzzy control strategy, which is based on linear matrix inequalities (LMIs) that combine adaptive update techniques, is proposed to suppress the dynamics uncertainties, the external disturbances, and the multiple stochastic delays in communication channels. The control approach ensures that the defined synchronization errors converge to zero. The stochastic stability in mean square of the closed-loop system is proved using LMIs based on Lyapunov-Krasovskii functional synthesis. The proposed controls are validated using extensive simulation studies.

In this study, the minimum transmission power of each femtocell user in a two‐tier network, in which femtocells and macrocell use the spectrum simultaneously, is investigated. That is, the authors are aiming at finding the power that characterises robustness and energy‐efficient when taking the uncertain gains into consideration. To solve the optimisation problem with uncertainty, an opportunistic power control strategy for the femtocells is introduced. Since the channel gain from a femtocell user to the femtocell base station is changeable with the environment, it is difficult to track channel gains instantaneously. For the reason that the mean channel gains, from the femtocell users to the femtocell base stations are usually achievable, the information of mean and probability density function are used to model the optimisation problem. Moreover, outage probability for each femtocell user's throughput is originated instead of considering the total throughput of the system, because high total throughput may not ensure that of each femtocell user. The signal‐to‐interference‐plus‐noise ratio of the macrocell is ensured by setting appreciate interference temperature. A distributed algorithm is designed to calculate the powers of femtocell users. Numerical results show the effectiveness of the proposed outage probabilistic method and the distributed algorithm.

This study considers a networked system in which the measurement suffers from one‐step delay and packet dropouts because of the unreliability of the network. A new model applied to describe the arrival conditions of the measurements is proposed. Based on the new model and using a state augmentation method, optimal linear filter, predictor and smoother are obtained. A sufficient condition for the convergence of the system is given. Finally, the simulation results show the effectiveness of the proposed algorithms.

This paper is concerned with the network delay compensation problem for nonlinear networked control systems (NCSs). By taking full advantage of the characteristics of the packet-based transmission in NCSs, new network delay compensation approaches are proposed to actively compensate the network communication delay under the fuzzy control framework. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the predictive control input packets are constructed based on parallel distributed compensation technique. Both state and output feedback fuzzy delay compensation controllers are designed. Finally, two examples are provided to illustrate the effectiveness and applicability of the developed techniques.

This paper considers a quantized system with finite-level quantized input computed from quantized measurements (QIQM). The problem of globally asymptotic stability of QIQM system is transferred to the one of an equivalent system depending on a multiplier which is nonnegative and bounded. It is the focus of this paper to discuss the nonnegativity and boundness of the multiplier. A sufficient condition is given for the globally asymptotic stability of QIQM system. Note the main method used here is similar to the one used by Richter and Misawa, in which the uniform quantizer is employed. This paper is the extension of that work to the logarithmic quantization which is more advantage than the uniform one. A numerical simulation is presented at last to show the effectiveness of the main results and the advantage of logarithmic quantization compared to uniform quantization.

This paper presents the design, analysis and performance of modified continuous extended Kalman filter based on complementary properties. The modified design departs from traditional solutions as better dynamic performance and steady-state performance for estimating the attitude angles on three axises, and effectively dominate the disturbance in practical system. In order to illustrate the better performance, the simulation results are given by comparing traditional extended Kalman filter with modified filter.

With the development of wireless communication technology, wireless network has been introduced into networked control system named WNCS. The wireless network brings some challenging issues. Here we consider three sources: ambient wireless traffic, block fading, and multipath fading. Based on the mathematical models of them, a wireless network is emulated. Networked predictive control (NPC) is then presented and used to make an inverted pendulum stable in the simulation. In the Simulink diagram the wireless network modules, the predictive controller, and the compensator are all implemented by C-MEX s-function. And the simulation result shows the effectiveness of the control scheme.

In “Comments on Constrained Infinite-Horizon Model Predictive Control for Fuzzy-Discrete-Time Systems” (IEEE Trans. Fuzzy Syst.), Ding criticized that the application of Lyapunov method, the handling of the output constraint, and the resultant linear-matrix-inequality-optimization problem are incorrect in IEEE Trans. Fuzzy Syst. , vol. 18, no. 2, pp. 429-436, Apr. 2010. This rebuttal answers some key points raised in the comments.

This paper investigates the problem of fault detection for Takagi-Sugeno (T-S) fuzzy discrete systems in finite-frequency domain. By means of the T-S fuzzy model, both a fuzzy fault detection filter system and the dynamics of filtering error generator are constructed. Two finite-frequency performance indices are introduced to measure fault sensitivity and disturbance robustness. Faults are considered in a middle frequency domain, while disturbances are considered in another certain finite-frequency domain interval. By using the generalized Kalman-Yakubovic-Popov Lemma in a local linear system model, the design methods are presented in terms of solutions to a set of linear matrix inequalities, which can be readily solved via standard numerical software. The design problem is formulated as a two-objective optimization algorithm. A numerical example is given to illustrate the effectiveness and potential of the developed techniques.

The problem of constrained infinite-horizon model-predictive control for fuzzy-discrete systems is considered in this paper. New sufficient conditions are proposed in terms of linear-matrix inequalities. Based on the optimal solutions of these sufficient conditions at each sampling instant, we design both parallel-distributed compensation and nonparallel-distributed compensation state-feedback controllers, which can guarantee that the resulting closed-loop fuzzy-discrete system is asymptotically stable. In addition, the fuzzy-feedback controllers meet the specifications for the fuzzy-discrete systems with both input and output constraints. Numerical examples are presented to demonstrate the effectiveness of the proposed techniques.

This paper is devoted to design adaptive sliding-mode controllers for the Takagi-Sugeno (T--S) fuzzy system with mismatched uncertainties and exogenous disturbances. The uncertainties in state matrices are mismatched and norm-bounded, while the exogenous disturbances are assumed to be bounded with an unknown bound, which is estimated by a simple and effective adaptive approach. Both state- and static-output-feedback sliding-mode-control problems are considered. In terms of linear-matrix inequalities (LMIs), both sliding surfaces and sliding-mode controllers can be easily obtained via a convex optimization technique. Finally, two simulation examples and a real experiment are utilized to illustrate the applicability and effectiveness of the design procedures proposed in this paper.

This paper is concerned with the stability analysis for a class of MIMO networked control systems (NCSs) with network constraints. In view of MIMO NCSs where network is of limited access channels, a discrete-time switched delay model is formulated. By constructing a novel piecewise Lyapunov-Krasovskii functional, a new stability criterion is developed in terms of linear matrix inequalities. A numerical example is given to show the effectiveness of the proposed method.

This paper is concerned with theHinfinfuzzy filtering problem for a class of continuous-time fuzzy systems with time-varying delays. The objective is to design a stable filter guaranteeing the asymptotic stability and a prescribedHinfinperformance of the filtering error system. Motivated by the parallel distributed compensation technique, we design a new filter model in this paper. Filter parameter matrices can be obtained from the solution of a convex optimization problem in terms of linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of a desiredHinfinfuzzy filter is given. Two numerical examples are provided to demonstrate the effectiveness and less conservativeness of the proposed design approach.

A novel networked multisensor data-fusion method is developed in this paper. A federated filter is employed to fuse the data transmitted over the network, which plays an important role in the data-processing center. The stability of filters under the network is considered; an algorithm to deal with the delayed data is introduced, and the principle for data fusion is presented. Finally, two numerical examples show the effectiveness of the proposed scheme.

This paper is concerned with the design of networked control systems (NCSs) with random network delay in the feedback channel and gives stability criteria of closed-loop networked predictive control systems. The principle of predictive control is adopted to overcome the effects of network time delay. The necessary and sufficient conditions on the stability of the closed-loop NCS are derived, which provides useful analytical stability criteria. The closed-loop networked predictive control system with bounded random network delay is stable if the corresponding switched system is stable. Simulation and real-time results give an illustration of the proposed control strategies