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Multi-robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells
Meng Zhou, Zihao Wang, Jing Wang, Zhengcai Cao
, Available online  , doi: 10.1109/JAS.2023.124041
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This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine (SVM) based on the definition of buffered Voronoi cells (BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm. Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.
A Novel Scalable Fault-Tolerant Control Design for DC Microgrids WIth Nonuniform Faults
Aimin Wang, Minrui Fei, Dajun Du, Yang Song
, Available online  , doi: 10.1109/JAS.2023.123918
[Abstract](25) [FullText HTML] (0) [PDF 525KB](6)
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Adaptive Consensus of Uncertain Multi-Agent Systems With Unified Prescribed Performance
Kun Li, Kai Zhao, Yongduan Song
, Available online  , doi: 10.1109/JAS.2023.123723
[Abstract](45) [FullText HTML] (0) [PDF 713KB](30)
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Dendritic Deep Learning for Medical Segmentation
Zhipeng Liu, Zhiming Zhang, Zhenyu Lei, Masaaki Omura, Rong-Long Wang, Shangce Gao
, Available online  , doi: 10.1109/JAS.2023.123813
[Abstract](4) [FullText HTML] (0) [PDF 772KB](1)
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Synchronous Membership Function Dependent Event-Triggered H∞ Control of T-S Fuzzy Systems Under Network Communications
Bo-Lin Xu, Chen Peng, Wen-Bo Xie
, Available online  , doi: 10.1109/JAS.2023.123729
[Abstract](3) [FullText HTML] (0) [PDF 407KB](1)
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Exponential Synchronization of Delayed Stochastic Complex Dynamical Networks via Hybrid Impulsive Control
Yao Cui, Pei Cheng, Xiaohua Ge
, Available online  , doi: 10.1109/JAS.2023.123867
[Abstract](4) [FullText HTML] (0) [PDF 568KB](0)
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Fixed-Time Sliding Mode Control With Varying Exponent Coefficient for Modular Reconfigurable Flight Arrays
Jianquan Yang, Chunxi Yang, Xiufeng Zhang, Jing Na
, Available online  
[Abstract](62) [FullText HTML] (0) [PDF 2547KB](35)
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The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array (MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements, and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally, simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.
Dendritic Learning-Incorporated Vision Transformer for Image Recognition
Zhiming Zhang, Zhenyu Lei, Masaaki Omura, Hideyuki Hasegawa, Shangce Gao
, Available online  
[Abstract](47) [FullText HTML] (0) [PDF 383KB](11)
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Multi-UAVs Collaborative Path Planning in the Cramped Environment
Siyuan Feng, Linzhi Zeng, Jining Liu, Yi Yang, Wenjie Song
, Available online  
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Due to its flexibility and complementarity, the multi-UAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue (SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles (UAVs) in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search (ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes. The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.
Even Search in a Promising Region for Constrained Multi-Objective Optimization
Fei Ming, Wenyin Gong, Yaochu Jin
, Available online  
[Abstract](46) [FullText HTML] (0) [PDF 1792KB](24)
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In recent years, a large number of approaches to constrained multi-objective optimization problems (CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly fine-tuned strategy or technique might overfit some problem types, resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties. First, the constrained Pareto front (CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance (i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search, which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.
Online Consensus Control of Nonlinear Affine Systems From Disturbed Data
Yifei Li, Wenjie Liu, Jian Sun, Chen Chen, Jia Zhang, Gang Wang
, Available online  
[Abstract](10) [FullText HTML] (0) [PDF 464KB](5)
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Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes
Chenliang Liu, Yalin Wang, Chunhua Yang, Weihua Gui
, Available online  , doi: 10.1109/JAS.2023.123741
[Abstract](129) [FullText HTML] (0) [PDF 583KB](46)
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Object Helps U-Net Based Change Detectors
Lan Yan, Qiang Li, Kenli Li
, Available online  
[Abstract](7) [FullText HTML] (0) [PDF 305KB](2)
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Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach
Yanzheng Zhu, Nuo Xu, Fen Wu, Xinkai Chen, Donghua Zhou
, Available online  
[Abstract](9) [FullText HTML] (0) [PDF 2703KB](9)
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In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuous-time Markov jump piecewise-affine (PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed $ H_{\infty}$ performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation. Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.
A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection
Honghao Zhu, MengChu Zhou, Yu Xie, Aiiad Albeshri
, Available online  
[Abstract](21) [FullText HTML] (0) [PDF 1201KB](12)
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A dandelion algorithm (DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA, which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA’s parameters and simplify DA’s structure. Only the normal sowing operator is retained; while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection (CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.
End-to-End Paired Ambisonic-Binaural Audio Rendering
Yin Zhu, Qiuqiang Kong, Junjie Shi, Shilei Liu, Xuzhou Ye, Ju-Chiang Wang, Hongming Shan, Junping Zhang
, Available online  
[Abstract](9) [FullText HTML] (0) [PDF 3348KB](5)
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Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function (HRTF) datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.
Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach
Yi Yu, Guo-Ping Liu, Yi Huang, Peng Shi
, Available online  
[Abstract](8) [FullText HTML] (0) [PDF 2433KB](8)
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DC-DC converter-based multi-bus DC microgrids (MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.
A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme
Nianyin Zeng, Xinyu Li, Peishu Wu, Han Li, Xin Luo
, Available online  
[Abstract](25) [FullText HTML] (0) [PDF 1818KB](5)
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Unmanned aerial vehicles (UAVs) have gained significant attention in practical applications, especially the low-altitude aerial (LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network (TDKD-Net) is proposed, where the TT-format TD (tensor decomposition) and equal-weighted response-based KD (knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU (intersection of union) loss with optimal transport assignment (F-EIoU-OTA) mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.
A Finite-Time Convergent Analysis of Continuous Action Iterated Dilemma
Zhen Wang, Xiaoyue Jin, Tao Zhang, Dengxiu Yu
, Available online  
[Abstract](5) [FullText HTML] (0) [PDF 283KB](3)
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Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5
Yongming Han, Lei Wang, Youqing Wang, Zhiqiang Geng
, Available online  
[Abstract](14) [FullText HTML] (0) [PDF 415KB](3)
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Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information
Yun Zhang, Lulu Zhang, Yunze Cai
, Available online  , doi: 10.1109/JAS.2023.124125
[Abstract](11) [FullText HTML] (0) [PDF 1520KB](6)
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This paper presents a novel cooperative value iteration (VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof. The players are divided into two groups in the learning process and adapt their policies sequentially. Our method removes the dependence of admissible initial policies, which is one of the main drawbacks of the PI-based frameworks. Furthermore, this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws. The efficacy of our method is illustrated by three examples.
Sparse Reconstructive Evidential Clustering for Multi-View Data
Chaoyu Gong, Yang You
, Available online  , doi: 10.1109/JAS.2023.123579
[Abstract](12) [FullText HTML] (0) [PDF 1337KB](4)
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Although many multi-view clustering (MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects, which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm (SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional human-readable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides, SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
A Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking
Yiwei Tian, Meiqin Liu, Senlin Zhang, Ronghao Zheng, Shanling Dong
, Available online  
[Abstract](4) [FullText HTML] (0) [PDF 476KB](5)
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Stabilization With Prescribed Instant Via Lyapunov Method
Jiyuan Kuang, Yabin Gao, Yizhuo Sun, Aohua Liu, Jianxing Liu
, Available online  
[Abstract](6) [FullText HTML] (0) [PDF 493KB](4)
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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach
Jiawen Kang, Junlong Chen, Minrui Xu, Zehui Xiong, Yutao Jiao, Luchao Han, Dusit Niyato, Yongju Tong, Shengli Xie
, Available online  , doi: 10.1109/JAS.2023.123993
[Abstract](18) [FullText HTML] (0) [PDF 2853KB](7)
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Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation, which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units (RSU) or unmanned aerial vehicles (UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning (MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization (MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers (e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.
Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information
Na Wang, Xiao Liang, Hongdan Li, Xiao Lu
, Available online  , doi: 10.1109/JAS.2023.124044
[Abstract](22) [FullText HTML] (0) [PDF 981KB](16)
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The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information. Compared with previous work, a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time. The main challenge lies in three aspects: Firstly, the asymmetric information results in coupling between control and estimation and failure of the separation principle. Secondly, two extra unknown variables are generated by asymmetric information (different information filtration) when solving forward-backward stochastic difference equations. Thirdly, the existence of additive noise makes the study of mean-square boundedness an obstacle. The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise. Numerical examples are presented to demonstrate the validity of the proposed algorithm.
Equilibrium Strategy of the Pursuit-Evasion Game in Three-Dimensional Space
Nuo Chen, Linjing Li, Wenji Mao
, Available online  
[Abstract](44) [FullText HTML] (0) [PDF 1974KB](17)
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The pursuit-evasion game models the strategic interaction among players, attracting attention in many realistic scenarios, such as missile guidance, unmanned aerial vehicles, and target defense. Existing studies mainly concentrate on the cooperative pursuit of multiple players in two-dimensional pursuit-evasion games. However, these approaches can hardly be applied to practical situations where players usually move in three-dimensional space with a three-degree-of-freedom control. In this paper, we make the first attempt to investigate the equilibrium strategy of the realistic pursuit-evasion game, in which the pursuer follows a three-degree-of-freedom control, and the evader moves freely. First, we describe the pursuer’s three-degree-of-freedom control and the evader’s relative coordinate. We then rigorously derive the equilibrium strategy by solving the retrogressive path equation according to the Hamilton-Jacobi-Bellman-Isaacs (HJBI) method, which divides the pursuit-evasion process into the navigation and acceleration phases. Besides, we analyze the maximum allowable speed for the pursuer to capture the evader successfully and provide the strategy with which the evader can escape when the pursuer’s speed exceeds the threshold. We further conduct comparison tests with various unilateral deviations to verify that the proposed strategy forms a Nash equilibrium.
Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems
Yunfeng Hu, Chong Zhang, Bo Wang, Jing Zhao, Xun Gong, Jinwu Gao, Hong Chen
, Available online  , doi: 10.1109/JAS.2023.123603
[Abstract](37) [FullText HTML] (0) [PDF 3973KB](20)
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Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output (MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control (ILC) scheme based on the zeroing neural networks (ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer (IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise, an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example 0and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.
PAPS: Progressive Attention-Based Pan-sharpening
Yanan Jia, Qiming Hu, Renwei Dian, Jiayi Ma, Xiaojie Guo
, Available online  , doi: 10.1109/JAS.2023.123987
[Abstract](13) [FullText HTML] (0) [PDF 5654KB](5)
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Pan-sharpening aims to seek high-resolution multispectral (HRMS) images from paired multispectral images of low resolution (LRMS) and panchromatic (PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS images. Having a better MS base (enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of constructing the desired HRMS. Extensive experiments together with ablation studies on widely-used datasets are provided to verify the efficacy of our design, and demonstrate its superiority over other state-of-the-art methods both quantitatively and qualitatively. Our code has been released at https://github.com/JiaYN1/PAPS.
Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming
Zhongyang Wang, Youqing Wang, Zdzisław Kowalczuk
, Available online  
[Abstract](108) [FullText HTML] (0) [PDF 1160KB](48)
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In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming (ADP) technique based on the internal model principle (IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.
An Incentive Mechanism for Federated Learning: A Continuous Zero-Determinant Strategy Approach
Changbing Tang, Baosen Yang, Xiaodong Xie, Guanrong Chen, Mohammed A. A. Al-qaness, Yang Liu
, Available online  , doi: 10.1109/JAS.2023.123828
[Abstract](132) [FullText HTML] (5) [PDF 2200KB](42)
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As a representative emerging machine learning technique, federated learning (FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution. These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant (CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL. Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.
Feature Matching via Topology-Aware Graph Interaction Model
Yifan Lu, Jiayi Ma, Xiaoguang Mei, Jun Huang, Xiao-Ping Zhang
, Available online  
[Abstract](112) [FullText HTML] (0) [PDF 16826KB](38)
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Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers. This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model, is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous locality-based method without noticeable deterioration in processing time, adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching (TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.
Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks
Fan Yang, Hongli Dong, Yuxuan Shen, Xuerong Li, Dongyan Dai
, Available online  
[Abstract](28) [FullText HTML] (0) [PDF 461KB](12)
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Robust Distributed Model Predictive Control for Formation Tracking of Nonholonomic Vehicles
Zhigang Luo, Bing Zhu, Jianying Zheng, Zewei Zheng
, Available online  
[Abstract](39) [FullText HTML] (0) [PDF 527KB](22)
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Security and Privacy in Solar Insecticidal Lamps Internet of Things: Requirements and Challenges
Qingsong Zhao, Lei Shu, Kailiang Li, Mohamed Amine Ferrag, Ximeng Liu, Yanbin Li
, Available online  
[Abstract](114) [FullText HTML] (0) [PDF 1047KB](24)
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Solar insecticidal lamps (SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things (IoT) has formed a new type of agricultural IoT, known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues. These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL, etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability, and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.
Orientation and Decision-Making for Soccer Based on Sports Analytics and AI: A Systematic Review
Zhiqiang Pu, Yi Pan, Shijie Wang, Boyin Liu, Min Chen, Hao Ma, Yixiong Cui
, Available online  , doi: 10.1109/JAS.2023.123807
[Abstract](235) [FullText HTML] (0) [PDF 1478KB](57)
Abstract:
Due to ever-growing soccer data collection approaches and progressing artificial intelligence (AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings game-changing approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action (OODA) loop. In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decision-making models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.
Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties
Dahui Luo, Yujuan Wang, Yongduan Song
, Available online  
[Abstract](94) [FullText HTML] (0) [PDF 1697KB](55)
Abstract:
This paper investigates the prescribed-time control (PTC) problem for a class of strict-feedback systems subject to non-vanishing uncertainties. The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC synthesis nontrivial. In this work, a control method that does not involve infinite time-varying gain is proposed, leading to a practical and global prescribed time tracking control solution for the strict-feedback systems, in spite of both the mismatched and non-vanishing uncertainties. Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point, in our method a softening unit is exclusively included to ensure the continuity of the control action. Furthermore, in contrast to most existing prescribed-time control works where the control scheme is only valid on a finite time interval, in this work, the proposed control scheme is valid on the entire time interval. In addition, the prior information on the upper or lower bound of ${\boldsymbol{g_{i}}}$ is not in need, enlarging the applicability of the proposed method. Both the theoretical analysis and numerical simulation confirm the effectiveness of the proposed control algorithm.
Autonomous Recommendation of Fault Detection Algorithms for Spacecraft
Wenbo Li, Baoling Ning
, Available online  , doi: 10.1109/JAS.2023.123423
[Abstract](98) [FullText HTML] (0) [PDF 481KB](39)
Abstract:
Geometric Programming for Nonlinear Satellite Buffer Networks With Time Delays under L1-Gain Performance
Yukang Cui, Yihui Huang, Michael V. Basin, Zongze Wu
, Available online  , doi: 10.1109/JAS.2023.123726
[Abstract](30) [FullText HTML] (0) [PDF 617KB](8)
Abstract:
Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time
Meilin Li, Yue Long, Tieshan Li, Hongjing Liang, C. L. Philip Chen
, Available online  , doi: 10.1109/JAS.2023.123582
[Abstract](117) [FullText HTML] (0) [PDF 4543KB](65)
Abstract:
This paper investigates the consensus control of multi-agent systems (MASs) with constrained input using the dynamic event-triggered mechanism (ETM). Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed. Then, a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks. It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time. In addition, it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter. Furthermore, the results are extended to regional consensus of the MASs with the bounded control protocol. Numerical simulations show the effectiveness of the proposed approach.
Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults
Li-Ying Hao, Gege Dong, Tieshan Li, Zhouhua Peng
, Available online  , doi: 10.1109/JAS.2023.123675
[Abstract](111) [FullText HTML] (0) [PDF 3518KB](33)
Abstract:
This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults, uncertainty and external disturbance. Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments, which may cause existing obstacle avoidance strategies to fail. To reduce the influence of actuator faults, an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors. The nonlinear state observer, which only depends on measurable position information of the autonomous surface vehicle, is used to address uncertainties and external disturbances. By using a backstepping technique and adaptive mechanism, a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero. Compared with existing results, the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously. Finally, the comparison results through simulations are given to verify the effectiveness of the proposed method.
Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios
Xiaolin Tang, Yuyou Yang, Teng Liu, Xianke Lin, Kai Yang, Shen Li
, Available online  , doi: 10.1109/JAS.2023.123975
[Abstract](68) [FullText HTML] (0) [PDF 2518KB](25)
Abstract:
Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot (PDEVNTPL) on the automatic ego vehicle (AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework (SPTF) based on soft actor-critic (SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of Deep Reinforcement Learning (DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26% respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43 degrees.
Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control
Hao Zhang, Yan Li, Zhuping Wang, Yi Ding, Huaicheng Yan
, Available online  , doi: 10.1109/JAS.2023.123381
[Abstract](33) [FullText HTML] (0) [PDF 525KB](15)
Abstract:
Non-Deterministic Liveness-Enforcing Supervisor Tolerant to Sensor-Reading Modification Attacks
Dan You, Shouguang Wang
, Available online  
[Abstract](30) [FullText HTML] (0) [PDF 790KB](20)
Abstract:
In this paper, we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur. In particular, we focus on the problem of liveness enforcement and consider a sensor-reading modification attack (SM-attack) that may disguise the occurrence of an event as that of another event by intruding sensor communication channels. To solve the problem, we introduce non-deterministic supervisors in the paper, which associate to every observed sequence a set of possible control actions offline and choose a control action from the set randomly online to control the system. Specifically, given a bounded Petri net (PN) as the reference formalism and an SM-attack, an algorithm that synthesizes a liveness-enforcing non-deterministic supervisor tolerant to the SM-attack is proposed for the first time.
Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications
Ding Wang, Ning Gao, Derong Liu, Jinna Li, Frank L. Lewis
, Available online  , doi: 10.1109/JAS.2023.123843
[Abstract](311) [FullText HTML] (1) [PDF 1172KB](181)
Abstract:
Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively. Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks, showing how they promote ADP formulation significantly. Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has demonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.
Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space
Yahui Liu, Bin Tian, Yisheng Lv, Lingxi Li, Fei-Yue Wang
, Available online  , doi: 10.1109/JAS.2023.123432
[Abstract](333) [FullText HTML] (0) [PDF 1384KB](52)
Abstract:
Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention, but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space (content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectNN. Source code of this paper is available at https://github.com/yahuiliu99/PointConT.
Distributed Optimal Formation Control for Unmanned Surface Vessels by a Regularized Game-Based Approach
Jun Shi, Maojiao Ye
, Available online  
[Abstract](33) [FullText HTML] (0) [PDF 720KB](12)
Abstract:
Prescribed-Time Fully Distributed Nash Equilibrium Seeking Strategy in Networked Games
Cheng Qian, Lei Ding
, Available online  
[Abstract](28) [FullText HTML] (0) [PDF 676KB](9)
Abstract:
Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems
Bogang Qu, Zidong Wang, Bo Shen, Hongli Dong, Hongjian Liu
, Available online  
[Abstract](58) [FullText HTML] (0) [PDF 1581KB](29)
Abstract:
This paper investigates the anomaly-resistant decentralized state estimation (SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements (i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points (MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach; 2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement; and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.
Sequential Inverse Optimal Control of Discrete-Time Systems
Sheng Cao, Zhiwei Luo, Changqin Quan
, Available online  , doi: 10.1109/JAS.2023.123762
[Abstract](68) [FullText HTML] (0) [PDF 1414KB](11)
Abstract:
This paper presents a novel sequential inverse optimal control (SIOC) method for discrete-time systems, which calculates the unknown weight vectors of the cost function in real time using the input and output of an optimally controlled discrete-time system. The proposed method overcomes the limitations of previous approaches by eliminating the need for the invertible Jacobian assumption. It calculates the possible-solution spaces and their intersections sequentially until the dimension of the intersection space decreases to one. The remaining one-dimensional vector of the possible-solution space's intersection represents the SIOC solution. The paper presents clear conditions for convergence and addresses the issue of noisy data by clarifying the conditions for the singular values of the matrices that relate to the possible-solution space. The effectiveness of the proposed method is demonstrated through simulation results.
Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control
Xiongbo Wan, Chaoling Zhang, Fan Wei, Chuan-Ke Zhang, Min Wu
, Available online  , doi: 10.1109/JAS.2023.123957
[Abstract](57) [FullText HTML] (0) [PDF 1517KB](40)
Abstract:
This article focuses on dynamic event-triggered mechanism (DETM)-based model predictive control (MPC) for T-S fuzzy systems. A hybrid dynamic variables-dependent DETM is carefully devised, which includes a multiplicative dynamic variable and an additive dynamic variable. The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem (OP). To facilitate the co-design of the MPC controller and the weighting matrix of the DETM, an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant (RPI) set that contain the membership functions and the hybrid dynamic variables. A dynamic event-triggered fuzzy MPC algorithm is developed accordingly, whose recursive feasibility is analysed by employing the RPI set. With the designed controller, the involved fuzzy system is ensured to be asymptotically stable. Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.
An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection
Ziqian Wang, Shangce Gao, Zhenyu Lei, Masaaki Omura
, Available online  
[Abstract](48) [FullText HTML] (0) [PDF 353KB](4)
Abstract:
Reinforcement Learning-Based MAS Interception in Antagonistic Environments
Siqing Sun, Defu Cai, Hai-Tao Zhang, Ning Xing
, Available online  
[Abstract](48) [FullText HTML] (0) [PDF 804KB](19)
Abstract:
Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach
Luigi D’Alfonso, Francesco Giannini, Giuseppe Franzè, Giuseppe Fedele, Francesco Pupo, Giancarlo Fortino
, Available online  , doi: 10.1109/JAS.2023.123705
[Abstract](70) [FullText HTML] (0) [PDF 2099KB](20)
Abstract:
In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework: the deep reinforcement learning output (action) is translated into a set-point to be tracked by the model predictive controller; conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decision-making purposes; on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps (links, junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main features of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.
Intra-independent Distributed Resource Allocation Game
Jialing Zhou, Guanghui Wen, Yuezu Lv, Tao Yang, Guanrong Chen
, Available online  , doi: 10.1109/JAS.2023.123906
[Abstract](32) [FullText HTML] (0) [PDF 552KB](14)
Abstract:
Analysis and Design of Time-Delay Impulsive Systems Subject to Actuator Saturation
Chenhong Zhu, Xiuping Han, Xiaodi Li
, Available online  , doi: 10.1109/JAS.2023.123720
[Abstract](71) [FullText HTML] (0) [PDF 1081KB](44)
Abstract:
This paper investigates the exponential stability and performance analysis of nonlinear time-delay impulsive systems subject to actuator saturation. When continuous dynamics is unstable, under some conditions, it is shown that the system can be stabilized by a class of saturated delayed-impulses regardless of the length of input delays. Conversely, when the system is originally stable, it is shown that under some conditions, the system is robust with respect to sufficient small delayed-impulses. Moreover, the design problem of the controller with the goal of obtaining a maximized estimate of the domain of attraction is formulated via a convex optimization problem. Three examples are provided to demonstrate the validity of the main results.
Data-Driven Learning Control Algorithms for Unachievable Tracking Problems
Zeyi Zhang, Hao Jiang, Dong Shen, Samer S. Saab
, Available online  , doi: 10.1109/JAS.2023.123756
[Abstract](44) [FullText HTML] (0) [PDF 1238KB](36)
Abstract:
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the P-type learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation. Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information. To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates low-memory footprints and offers flexibility in learning gain design. The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.
Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication
Maojiao Ye, Qing-Long Han, Lei Ding, Shengyuan Xu, Guobiao Jia
, Available online  , doi: 10.1109/JAS.2022.105857
[Abstract](210) [FullText HTML] (0) [PDF 1756KB](55)
Abstract:
This paper is concerned with distributed Nash equilibrium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization of players’ objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized information flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respectively. Through Lyapunov stability analysis, it is shown that players’ actions can be steered to a neighborhood of the Nash equilibrium with logarithmic and uniform quantizers, and the quantified convergence error depends on the parameter of the quantizer for both undirected and directed cases. A numerical example is given to verify the theoretical results.
Relaxed Stability Criteria for Time-delay Systems: A Novel Quadratic Function Convex Approximation Approach
Shenquan Wang, Wenchengyu Ji, Yulian Jiang, Yanzheng Zhu, Jian Sun
, Available online  , doi: 10.1109/JAS.2023.123735
[Abstract](67) [FullText HTML] (0) [PDF 1782KB](44)
Abstract:
This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays. By introducing two adjustable parameters and two free variables, a novel convex function greater than or equal to the quadratic function is constructed, regardless of the sign of the coefficient in the quadratic term. The developed lemma can also be degenerated into the existing quadratic function negative-determination (QFND) lemma and relaxed QFND lemma respectively, by setting two adjustable parameters and two free variables as some particular values. Moreover, for a linear system with time-varying delays, a relaxed stability criterion is established via our developed lemma, together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality. As a result, the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay systems. Finally, the superiority of our results is illustrated through three numerical examples.
Heterogeneous Image Knowledge Driven Visual Perception
Lan Yan, Wenbo Zheng, Fei-Yue Wang
, Available online  
[Abstract](49) [FullText HTML] (0) [PDF 922KB](16)
Abstract:
Parallel Light Fields: A Perspective and A Framework
Fei-Yue Wang, Yu Shen
, Available online  , doi: 10.1109/JAS.2023.123174
[Abstract](178) [FullText HTML] (0) [PDF 735KB](26)
Abstract:
A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network
Yishuai Lin, Gang Hu, Liang Wang, Qingshan Li, Jiawei Zhu
, Available online  , doi: 10.1109/JAS.2023.123300
[Abstract](134) [FullText HTML] (0) [PDF 1544KB](28)
Abstract:
Parameter-Free Shifted Laplacian Reconstruction for Multiple Kernel Clustering
Xi Wu, Zhenwen Ren, F. Richard Yu
, Available online  , doi: 10.1109/JAS.2023.123600
[Abstract](97) [FullText HTML] (0) [PDF 472KB](10)
Abstract:
Integrating Inventory Monitoring and Capacity Changes in Dynamic Supply Chains with Bi-Directional Cascading Propagation Effects
En-Zhi Cao, Chen Peng, Qing-Kui Li
, Available online  , doi: 10.1109/JAS.2023.123309
[Abstract](40) [FullText HTML] (0) [PDF 1122KB](15)
Abstract:
3D Localization for Multiple AUVs in Anchor-Free Environments by Exploring the Use of Depth Information
Yichen Li, Wenbin Yu, Xinping Guan
, Available online  , doi: 10.1109/JAS.2023.123261
[Abstract](39) [FullText HTML] (0) [PDF 322KB](5)
Abstract:
Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer
Chi Ma, Dianbiao Dong
, Available online  , doi: 10.1109/JAS.2023.123615
[Abstract](140) [FullText HTML] (0) [PDF 3029KB](55)
Abstract:
This paper studies the problem of time-varying formation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dynamics of the system. In addition, due to the limitations of the actual working conditions, each follower agent can only obtain the locally measurable partial state information of the leader agent. To address this problem, a neural network state observer based on the leader state information is designed. Then, a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region, which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time. Finally, a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.
Recurrent Neural Network Inspired Finite-Time Control Design
Jianan Liu, Shihua Li, Rongjie Liu
, Available online  , doi: 10.1109/JAS.2023.123297
[Abstract](96) [FullText HTML] (0) [PDF 1090KB](28)
Abstract:
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
[Abstract](108) [FullText HTML] (0) [PDF 703KB](14)
Abstract:
Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies
Xiaohua Ge, Qing-Long Han, Xian-Ming Zhang, Derui Ding
, Available online  , doi: 10.1109/JAS.2023.123507
[Abstract](235) [FullText HTML] (0) [PDF 2996KB](69)
Abstract:
Platooning represents one of the key features that connected automated vehicles can possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge of accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the inter-vehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Minfeng Qi, Ziyuan Wang, Qing-Long Han, Jun Zhang, Shiping Chen, Yang Xiang
, Available online  , doi: 10.1109/JAS.2022.106058
[Abstract](460) [FullText HTML] (0) [PDF 1296KB](92)
Abstract:
To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.
Distributed Minimum-Energy Containment Control of Continuous-Time Multi-Agent Systems by Inverse Optimal Control
Fei Yan, Xiangbiao Liu, Tao Feng
, Available online  , doi: 10.1109/JAS.2022.106067
[Abstract](140) [FullText HTML] (0) [PDF 521KB](21)
Abstract:
Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers
Meiling Xie, Derui Ding, Xiaohua Ge, Qing-Long Han, Hongli Dong, Yan Song
, Available online  , doi: 10.1109/JAS.2022.105941
[Abstract](154) [FullText HTML] (0) [PDF 1736KB](59)
Abstract:
Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
Supplementary File of “Push-Sum Based Algorithm for Constrained Convex Optimization Problem and Its Potential Application in Smart Grid”
Qian Xu, Zao Fu, Bo Zou, Hongzhe Liu, Lei Wang
, Available online  
[Abstract](100) [FullText HTML] (0) [PDF 131KB](1)
Abstract:
Supplementary Material for “Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes”
Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu
, Available online  
[Abstract](149) [FullText HTML] (0) [PDF 8129KB](6)
Abstract: