Showing papers in "IEEE/CAA Journal of Automatica Sinica in 2022"

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

Abstract: This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks (VANETs) subject to finite communication resource. First, a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input. Under such a platoon model, the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader, while attaining improved communication efficiency. Toward this aim, a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed. One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status. Then, a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived. Finally, simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

Abstract: This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.

SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

Abstract: This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction. More specifically, the proposed method involves an intra-domain fusion unit based on self-attention and an inter-domain fusion unit based on cross-attention, which mine and integrate long dependencies within the same domain and across domains. Through long-range dependency modeling, the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective. In particular, we introduce the shifted windows mechanism into the self-attention and cross-attention, which allows our model to receive images with arbitrary sizes. On the other hand, the multi-scene image fusion problems are generalized to a unified framework with structure maintenance, detail preservation, and proper intensity control. Moreover, an elaborate loss function, consisting of SSIM loss, texture loss, and intensity loss, drives the network to preserve abundant texture details and structural information, as well as presenting optimal apparent intensity. Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-the-art unified image fusion algorithms and task-specific alternatives. Implementation code and pre-trained weights can be accessed at https://github.com/Linfeng-Tang/SwinFusion.

SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

Abstract: This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction. More specifically, the proposed method involves an intra-domain fusion unit based on self-attention and an inter-domain fusion unit based on cross-attention, which mine and integrate long dependencies within the same domain and across domains. Through long-range dependency modeling, the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective. In particular, we introduce the shifted windows mechanism into the self-attention and cross-attention, which allows our model to receive images with arbitrary sizes. On the other hand, the multi-scene image fusion problems are generalized to a unified framework with structure maintenance, detail preservation, and proper intensity control. Moreover, an elaborate loss function, consisting of SSIM loss, texture loss, and intensity loss, drives the network to preserve abundant texture details and structural information, as well as presenting optimal apparent intensity. Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-the-art unified image fusion algorithms and task-specific alternatives. Implementation code and pre-trained weights can be accessed at https://github.com/Linfeng-Tang/SwinFusion.

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

Abstract: This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks (VANETs) subject to finite communication resource. First, a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input. Under such a platoon model, the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader, while attaining improved communication efficiency. Toward this aim, a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed. One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status. Then, a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived. Finally, simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey

Abstract: With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

Abstract: This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.

Consensus Control of Multi-Agent Systems Using Fault-Estimation-in-the-Loop: Dynamic Event-Triggered Case

Abstract: The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems (MASs) in the presence of faults. A dynamic event-triggered protocol (DETP) by adding an auxiliary variable is utilized to improve the utilization of communication resources. First, a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation (FC) is adopted to realize the demand of reliability and safety of addressed MASs. Subsequently, a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the $l_{2}-l_{\infty}$ constraint by employing the variance analysis and the Lyapunov stability approaches. Furthermore, the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way, respectively. Finally, a simulation result is employed to verify the usefulness of the proposed design framework.

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

Abstract: Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

Abstract: Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.

An Overview of Finite/Fixed-Time Control and its Application in Engineering Systems

Abstract: The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability, the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information. This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.

An Overview of Finite/Fixed-Time Control and Its Application in Engineering Systems

Abstract: The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability, the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information. This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.

A Survey of Underwater Multi-Robot Systems

Abstract: As a cross-cutting field between ocean development and multi-robot system (MRS), the underwater multi-robot system (UMRS) has gained increasing attention from researchers and engineers in recent decades. In this paper, we present a comprehensive survey of cooperation issues, one of the key components of UMRS, from the perspective of the emergence of new functions. More specifically, we categorize the cooperation in terms of task-space, motion-space, measurement-space, as well as their combination. Further, we analyze the architecture of UMRS from three aspects, i.e., the performance of the individual underwater robot, the new functions of underwater robots, and the technical approaches of MRS. To conclude, we have discussed related promising directions for future research. This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Abstract: Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition, the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks. This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named SuperFusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion, and semantic segmentation tasks demonstrate the superiority of our SuperFusion compared to the state-of-the-art alternatives. The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/SuperFusion.

Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

Abstract: Some recent research reports that a dendritic neuron model (DNM) can achieve better performance than traditional artificial neuron networks (ANNs) on classification, prediction, and other problems when its parameters are well-tuned by a learning algorithm. However, the back-propagation algorithm (BP), as a mostly used learning algorithm, intrinsically suffers from defects of slow convergence and easily dropping into local minima. Therefore, more and more research adopts non-BP learning algorithms to train ANNs. In this paper, a dynamic scale-free network-based differential evolution (DSNDE) is developed by considering the demands of convergent speed and the ability to jump out of local minima. The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem. Nine meta-heuristic algorithms are applied into comparison, including the champion of the 2017 IEEE Congress on Evolutionary Computation (CEC2017) benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR). The experimental results reveal that DSNDE achieves better performance than its peers.

The DAO to MetaControl for MetaSystems in Metaverses: The System of Parallel Control Systems for Knowledge Automation and Control Intelligence in CPSS

Abstract: Briefing: An investigation and outline of MetaControl and DeControl in Metaverses for control intelligence and knowledge automation are presented. Prescriptive control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology, especially for computational control of metasystems in cyber-physical-social systems. We argue that circular causality, the generalized feedback mechanism for complex and purposive systems, should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in metaverses. Particularly, an interdisciplinary approach is suggested for MetaControl and DeControl as a new form of intelligent control based on five control metaverses: Meta Verses, MultiVerses, InterVerses, TransVerse, and Deep Verses.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Abstract: Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technologies, massive protein-protein interaction (PPI) data have been generated, making it very difficult to analyze them efficiently. To address this problem, this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, using MapReduce. To do so, an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction. Respective solutions are then devised to overcome these limitations. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins. After that, its procedure is modified by following the MapReduce framework to take the prediction task distributively. A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy. Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Abstract: Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition, the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks. This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named SuperFusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion, and semantic segmentation tasks demonstrate the superiority of our SuperFusion compared to the state-of-the-art alternatives. The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/SuperFusion.

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

Abstract: A cyber physical system (CPS) is a complex system that integrates sensing, computation, control and networking into physical processes and objects over Internet. It plays a key role in modern industry since it connects physical and cyber worlds. In order to meet ever-changing industrial requirements, its structures and functions are constantly improved. Meanwhile, new security issues have arisen. A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems, and thus has gained increasing attention from researchers and practitioners. This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems. First, as typical system models are employed to study these systems, time-driven and event-driven systems are reviewed. Then, recent advances on three types of attacks, i.e., those on availability, integrity, and confidentiality are discussed. In particular, the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders. Namely, both attack and defense strategies are discussed based on different system models. Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

Abstract: A cyber physical system (CPS) is a complex system that integrates sensing, computation, control and networking into physical processes and objects over Internet. It plays a key role in modern industry since it connects physical and cyber worlds. In order to meet ever-changing industrial requirements, its structures and functions are constantly improved. Meanwhile, new security issues have arisen. A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems, and thus has gained increasing attention from researchers and practitioners. This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems. First, as typical system models are employed to study these systems, time-driven and event-driven systems are reviewed. Then, recent advances on three types of attacks, i.e., those on availability, integrity, and confidentiality are discussed. In particular, the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders. Namely, both attack and defense strategies are discussed based on different system models. Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

Dynamic Event-triggered State Estimation for Nonlinear Coupled Output Complex Networks Subject to Innovation Constraints

Abstract: This letter investigates the recursive state estimation (RSE) problem for a class of coupled output complex networks via the dynamic event-triggered communication mechanism (DETCM) and innovation constraints (ICs). Firstly, a DETCM is employed to regulate the transmission sequences. Then, in order to improve the reliability of network communication, a saturation function is introduced to constrain the measurement outliers. A new RSE method is provided such that, for all output coupling, DETCM and ICs, an upper bound of state estimation error covariance (SEEC) is presented in a recursive form, whose trace can be minimized via parameterizing the state estimator gain matrix (SEGM). Moreover, the theoretical analysis is given to guarantee that the error dynamic is uniformly bounded. Finally, a simulation example is illustrated to show the effectiveness of the proposed RSE method.

Adaptive Decentralized Asymptotic Tracking Control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

Abstract: An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections, unknown time-varying parameters, and disturbances. First, by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time, all extra signals in the framework of decentralized control are filtered out, thereby removing all additional assumptions imposed on the interconnections, such as upper bounding functions and matching conditions. Second, by introducing two integral bounded functions, asymptotic tracking control is realized. Moreover, the nonlinear filters with the compensation terms are introduced to circumvent the issue of “explosion of complexity”. It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero asymptotically. In the end, a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

Scribble-Supervised Video Object Segmentation

Abstract: Recently, video object segmentation has received great attention in the computer vision community. Most of the existing methods heavily rely on the pixel-wise human annotations, which are expensive and time-consuming to obtain. To tackle this problem, we make an early attempt to achieve video object segmentation with scribble-level supervision, which can alleviate large amounts of human labor for collecting the manual annotation. However, using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete. To address this issue, this paper introduces two novel elements to learn the video object segmentation model. The first one is the scribble attention module, which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background. The other one is the scribble-supervised loss, which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage. To evaluate the proposed method, we implement experiments on two video object segmentation benchmark datasets, YouTube-video object segmentation (VOS), and densely annotated video segmentation (DAVIS)-2017. We first generate the scribble annotations from the original per-pixel annotations. Then, we train our model and compare its test performance with the baseline models and other existing works. Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.

Adaptive Decentralized Asymptotic Tracking Control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

Abstract: An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections, unknown time-varying parameters, and disturbances. First, by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time, all extra signals in the framework of decentralized control are filtered out, thereby removing all additional assumptions imposed on the interconnections, such as upper bounding functions and matching conditions. Second, by introducing two integral bounded functions, asymptotic tracking control is realized. Moreover, the nonlinear filters with the compensation terms are introduced to circumvent the issue of “explosion of complexity”. It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero asymptotically. In the end, a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

Sliding Mode Control in Power Converters and Drives: A Review

Abstract: Sliding mode control (SMC) has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched disturbances. The aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic systems. The fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives, such chattering phenomenon and variable switching frequency, are discussed and analyzed. The recent developments in SMC systems, future challenges and perspectives of SMC for power converters are discussed.

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Abstract: Graph convolutional networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.

Unmanned Aerial Vehicles: Control Methods and Future Challenges

Abstract: With the rapid development of computer technology, automatic control technology and communication technology, research on unmanned aerial vehicles (UAVs) has attracted extensive attention from all over the world during the last decades. Particularly due to the demand of various civil applications, the conceptual design of UAV and autonomous flight control technology have been promoted and developed mutually. This paper is devoted to providing a brief review of the UAV control issues, including motion equations, various classical and advanced control approaches. The basic ideas, applicable conditions, advantages and disadvantages of these control approaches are illustrated and discussed. Some challenging topics and future research directions are raised.

Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning

Abstract: Deep reinforcement learning (DRL), combining the perception capability of deep learning (DL) and the decision-making capability of reinforcement learning (RL) [1], has been widely investigated for autonomous driving decision-making tasks. In this letter, we would like to discuss the impact of different types of state input on the performance of DRL-based lane change decision-making.

Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

Abstract: Some recent research reports that a dendritic neuron model (DNM) can achieve better performance than traditional artificial neuron networks (ANNs) on classification, prediction, and other problems when its parameters are well-tuned by a learning algorithm. However, the back-propagation algorithm (BP), as a mostly used learning algorithm, intrinsically suffers from defects of slow convergence and easily dropping into local minima. Therefore, more and more research adopts non-BP learning algorithms to train ANNs. In this paper, a dynamic scale-free network-based differential evolution (DSNDE) is developed by considering the demands of convergent speed and the ability to jump out of local minima. The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem. Nine meta-heuristic algorithms are applied into comparison, including the champion of the 2017 IEEE Congress on Evolutionary Computation (CEC2017) benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR). The experimental results reveal that DSNDE achieves better performance than its peers.

Unmanned Aerial Vehicles: Control Methods and Future Challenges

Abstract: With the rapid development of computer technology, automatic control technology and communication technology, research on unmanned aerial vehicles (UAVs) has attracted extensive attention from all over the world during the last decades. Particularly due to the demand of various civil applications, the conceptual design of UAV and autonomous flight control technology have been promoted and developed mutually. This paper is devoted to providing a brief review of the UAV control issues, including motion equations, various classical and advanced control approaches. The basic ideas, applicable conditions, advantages and disadvantages of these control approaches are illustrated and discussed. Some challenging topics and future research directions are raised.