IEEE/CAA Journal of Automatica Sinica

Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design

Jianqing Lin, Cheng He, Ye Tian, Linqiang Pan

, Available online , doi: 10.1109/JAS.2024.124947

[Abstract](31) [FullText HTML] (0) [PDF 7763KB](10)

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Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.

Length-Variable Bionic Continuum Robot With Millimeter-Scale Diameter and Compliant Driving Force

Shiying Zhao, Qingxin Meng, Xuzhi Lai, Jinhua She, Edwardo Fumihiko Fukushima, Min Wu

, Available online , doi: 10.1109/JAS.2024.125091

[Abstract](117) [FullText HTML] (0) [PDF 41994KB](27)

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Compared with conventional rigid-link robots, bionic continuum robots (CRs) show great potential in unstructured environments because of their adaptivity and continuous deformation ability. However, designing a CR to achieve miniaturization, variable length and compliant driving force remains a challenge. Here, inspired by the earthworm in nature, we report a length-variable bionic CR with millimeter-scale diameter and compliant driving force. The CR consists of two main components: the robot body and soft drives. The robot body is only 6 mm in diameter, and is composed of a backbone and transmission devices. The backbone is divided into three segments, and each segment is capable of adjusting its length and bending like the earthworm. The maximum length variation of the backbone can reach an astonishing 70 mm with a backbone’s initial length of 150 mm, and the maximum bending angle of each segment can reach 120 degrees. In addition, we develop soft drives using pneumatic soft actuators (PSAs) as a replacement for the rigid motors typically used in conventional CRs. These soft drives control the motions of the transmission devices, enabling length variation and bending of the backbone. By utilizing these soft drives, we ensure that the robot body has a compliant driving force, which addresses users’ concerns about human safety during interactions. In practical applications, we prove that this CR can perform delicate manipulations by successfully completing writing tasks. Additionally, we show its application value for detections and medical treatments by entering the narrow tube and the oral.

When Embodied AI Meets Industry 5.0: Human-Centered Smart Manufacturing

Jing Xu, Qiyu Sun, Qing-Long Han, Yang Tang

, Available online , doi: 10.1109/JAS.2025.125327

[Abstract](7) [FullText HTML] (0) [PDF 4526KB](2)

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As embodied intelligence (EI), large language models (LLMs), and cloud computing continue to advance, Industry 5.0 facilitates the development of industrial artificial intelligence (IndAI) through cyber-physical-social systems (CPSSs) with a human-centric focus. These technologies are organized by the system-wide approach of Industry 5.0, in order to empower the manufacturing industry to achieve broader societal goals of job creation, economic growth, and green production. This survey first provides a general framework of smart manufacturing in the context of Industry 5.0. Wherein, the embodied agents, like robots, sensors, and actuators, are the carriers for IndAI, facilitating the development of the self-learning intelligence in individual entities, the collaborative intelligence in production lines and factories (smart systems), and the swarm intelligence within industrial clusters (systems of smart systems). Through the framework of CPSSs, the key technologies and their possible applications for supporting the single-agent, multi-agent and swarm-agent embodied IndAI have been reviewed, such as the embodied perception, interaction, scheduling, multi-mode large language models, and collaborative training. Finally, to stimulate future research in this area, the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed. The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner, thereby fostering an intelligent, sustainable, and resilient industrial landscape.

A k-Winners-Take-All (kWTA) Network With Noise Characteristics Captured

Jiexing Li, Yulin Cao, Zhengtai Xie, Long Jin

, Available online

[Abstract](9) [FullText HTML] (0) [PDF 13962KB](4)

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Competition-based $ k $-winners-take-all ($ k $WTA) networks play a crucial role in multi-agent systems. However, existing $ k $WTA networks either neglect the impact of noise or only consider simple forms, such as constant noise. In practice, noises often exhibit time-varying and nonlinear characteristics, which can be modeled using nonlinear functions and approximated by high-order polynomials. Such noises pose significant challenges for current $ k $WTA networks, limiting their practical applications. To address this, a $ k $WTA network with noise characteristics captured ($ k $WTA-NCC) is proposed in this article. Theoretical analyses demonstrate that the residual error of the proposed $ k $WTA-NCC network converges to zero globally, while simulation results confirm its robustness against polynomial noises. Additionally, a $ k $WTA coordination model is constructed by integrating the proposed network with a consensus estimator to achieve multi-agent tracking tasks. Finally, simulations and physical experiments are conducted further to demonstrate the validity and practicality of the $ k $WTA coordination model.

KT-RC: Kernel Time-Delayed Reservoir Computing for Time Series Prediction

Heshan Wang, Mengmeng Chen, Kunjie Yu, Jing Liang, Zhaomin Lv, Zhong Zhang

, Available online , doi: 10.1109/JAS.2024.124986

[Abstract](32) [FullText HTML] (0) [PDF 9327KB](10)

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Reservoir computing (RC) is an efficient recurrent neural network (RNN) method. However, the performance and prediction results of traditional RCs are susceptible to several factors, such as their network structure, parameter setting, and selection of input features. In this study, we employ a kernel time-delayed RC (KT-RC) method for time series prediction. The KT-RC transforms input vectors linearly to obtain a high-dimensional set of time-delayed linear eigenvectors, which are then transformed by various kernel functions to represent the nonlinear characteristics of the input signal. Finally, the Bayesian optimization algorithm adjusts the few remaining weights and kernel parameters to minimize the manual adjustment process. The advantages of KT-RC can be summarized as follows: 1) KT-RC solves the problems of uncertainty in weight matrices and difficulty in large-scale parameter selection in the input and hidden layers of RCs. 2) The KT module can avoid massive reservoir hyperparameters and effectively reduce the hidden layer size of the traditional RC. 3) The proposed KT-RC shows good performance, strong stability, and robustness in several synthetic and real-world datasets for one-step-ahead and multistep-ahead time series prediction. The simulation results confirm that KT-RC not only outperforms some gate-structured RNNs, kernel vector regression models, and recently proposed prediction models but also requires fewer parameters to be initialized and can reduce the hidden layer size of the traditional RCs. The source code is available at https://github.com/whs7713578/RC.

LTDNet: A Lightweight Text Detector for Real-Time Arbitrary-Shape Traffic Text Detection

Runmin Wang, Yanbin Zhu, Ziyu Zhu, Lingxin Cui, Zukun Wan, Anna Zhu, Yajun Ding, Shengyou Qian, Changxin Gao, Nong Sang

, Available online , doi: 10.1109/JAS.2024.125022

[Abstract](45) [FullText HTML] (0) [PDF 14194KB](16)

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Traffic text detection plays a vital role in understanding traffic scenes. Traffic text, a distinct subset of natural scene text, faces specific challenges not found in natural scene text detection, including false alarms from non-traffic text sources, such as roadside advertisements and building signs. Existing state-of-the-art methods employ increasingly complex detection frameworks to pursue higher accuracy, leading to challenges with real-time performance. In response to this issue, we propose a real-time and efficient traffic text detector named LTDNet, which strikes a balance between accuracy and real-time capabilities. LTDNet integrates three essential techniques to address these challenges effectively. First, a cascaded multilevel feature fusion network is employed to mitigate the limitations of lightweight backbone networks, thereby enhancing detection accuracy. Second, a lightweight feature attention module is introduced to enhance inference speed without compromising accuracy. Finally, a novel point-to-edge distance vector loss function is proposed to precisely localize text instance boundaries within traffic contexts. The superiority of our method is validated through extensive experiments on five publicly available datasets, demonstrating its state-of-the-art performance. The code will be released at https://github.com/runminwang/LTDNet.

Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control

Jiacheng Li, Wenhui Ma, YangWang Fang, Dengxiu Yu, C. L. Philip Chen

, Available online

[Abstract](4) [FullText HTML] (0) [PDF 8760KB](3)

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The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the compact configuration of the swarm. In light of this problem, a novel parallel control method that utilizes space and time transformation is proposed. First, the swarm is decomposed based on a grouping-hierarchical strategy, while the distinct flight roles are assigned to each UAV. Then, to achieve the desired configuration (DCF) in the real world, a bijection transformation is conducted in the space domain, converting an arbitrarily general configuration (GCF) into a standard configuration (SCF) in the virtual space. Further, to improve the flexibility of the swarm, the time scaling transformation is adopted in the time domain, which ensures the desired prescribed-time convergence of the swarm independent of initial conditions. Finally, simulation results demonstrate that collision-free maneuvering, including formation changes and turning, can be effectively and rapidly achieved by the proposed parallel control method. Overall, this research contributes a viable solution for enhancing cooperation among large-scale UAV swarms.

E²AG: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor Modeling

Zhichao Chen, Licheng Pan, Yiran Ma, Zeyu Yang, Le Yao, Jinchuan Qian, Zhihuan Song

, Available online

[Abstract](4) [FullText HTML] (0) [PDF 3899KB](3)

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Adaptive Graph Neural Networks (AGNNs) have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables. This article introduces a novel GNN framework, termed Entropy-Regularized Ensemble Adaptive Graph (E²AG), aimed at enhancing the predictive accuracy of AGNNs. Specifically, this work pioneers a novel AGNN learning approach based on mirror descent, which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs. Subsequently, motivated by multi-head self-attention mechanism, the training of ensembled AGNNs is rigorously examined within this framework, incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph. After that, the architecture and training algorithm of the model are then concisely summarized. Finally, to ascertain the efficacy of the proposed E²AG model, extensive experiments are conducted on real-world industrial datasets. The evaluation focuses on prediction accuracy, model efficacy, and sensitivity analysis, demonstrating the superiority of E²AG in industrial soft sensing applications.

An Emulation Approach to Semi-Global Robust Output Regulation for a Class of Nonlinear Uncertain Systems

Jieshuai Wu, Maobin Lu, Fang Deng, Jie Chen

, Available online , doi: 10.1109/JAS.2024.125085

[Abstract](15) [FullText HTML] (0) [PDF 1184KB](4)

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In this paper, we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems. By the emulation approach, we propose a class of sampled-data output feedback control laws to solve this problem. In particular, we first develop a general sampled-data dynamic output feedback control law and characterize the closed-loop system by a hybrid system. Then, we design the internal model based on the sampled error output of the system. Based on the internal model principle, we convert the semi-global robust output regulation problem into a semi-global robust stabilization problem of an augmented hybrid system composed of the internal model and the original system. By proposing the sampled error output feedback control law and by means of Lyapunov analysis, we obtain the maximum allowable transmission interval for sampling and show that semi-global robust stabilization of the augmented hybrid system can be achieved by the proposed sampled-data control law and thus leading to the solution of the semi-global robust output regulation problem. Finally, we apply the proposed control approach to two practical applications to verify the effectiveness of the proposed control approach.

Instance by Instance: An Iterative Framework for Multi-Instance 3D Registration

Jiaqi Yang, Xinyue Cao, Xiyu Zhang, Yuxin Cheng, Zhaoshuai Qi, Siwen Quan

, Available online , doi: 10.1109/JAS.2024.125058

[Abstract](15) [FullText HTML] (0) [PDF 27956KB](0)

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Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. Pioneers followed a non-extensible one-shot framework, which prioritizes the registration of simple and isolated instances, often struggling to accurately register challenging or occluded instances. To address these challenges, we propose the first iterative framework for multi-instance 3D registration (MI-3DReg) in this work, termed instance-by-instance (IBI). It successively registers instances while systematically reducing outliers, starting from the easiest and progressing to more challenging ones. This enhances the likelihood of effectively registering instances that may have been initially overlooked, allowing for successful registration in subsequent iterations. Under the IBI framework, we further propose a sparse-to-dense correspondence-based multi-instance registration method (IBI-S2DC) to enhance the robustness of MI-3DReg. Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC, e.g., our mean registration F1 score is 12.02%/12.35% higher than the existing state-of-the-art on the synthetic/real datasets. The source codes are availableonline at https://github.com/caoxy01/IBI.

A Survey of Distributed Algorithms for Aggregative Games

Huaqing Li, Jun Li, Liang Ran, Lifeng Zheng, Tingwen Huang

, Available online , doi: 10.1109/JAS.2024.124998

[Abstract](10) [FullText HTML] (0) [PDF 1007KB](4)

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Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player’s decision is influenced by an aggregation of all players’ decisions. Nash equilibrium seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms’ convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions.

A Multi-Type Feature Fusion Network Based on Importance Weighting for Occluded Human Pose Estimation

Jiahong Jiang, Nan Xia, Siyao Zhou

, Available online

[Abstract](3) [FullText HTML] (0) [PDF 14610KB](2)

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Human pose estimation is a challenging task in computer vision. Most algorithms perform well in regular scenes, but lack good performance in occlusion scenarios. Therefore, we propose a multi-type feature fusion network based on importance weighting, which consists of three modules. In the first module, we propose a multi-resolution backbone with two feature enhancement sub-modules, which can extract features from different scales and enhance the feature expression ability. In the second module, we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology. In the third module, we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes, thereby improving the feature extraction ability. We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017 (COCO2017), COCO-Wholebody and CrowdPose, achieving the accuracy of 78.9%, 67.1% and 77.6%, respectively. Additionally, a series of ablation experiments are designed to show the performance of our work. Finally, we present the visualization of different scenarios to verify the effectiveness of our work.

Necessary and Sufficient Conditions for Controllability and Essential Controllability of Directed Circle and Tree Graphs

Jijun Qu, Zhijian Ji, Jirong Wang, Yungang Liu

, Available online , doi: 10.1109/JAS.2024.124866

[Abstract](59) [FullText HTML] (0) [PDF 1116KB](16)

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The multi-agent controllability is intrinsically affected by the network topology and the selection of leaders. A focus of exploring this problem is to uncover the relationship between the eigenspace of Laplacian matrix and network topology. For strongly connected directed circle graphs, we elaborate how the zero entries in the left eigenvectors of Laplacian matrix L arise. The topologies arising from left eigenvectors with zero entries are filtered to construct essentially controllable directed circle graphs regardless of the choice of leaders. We propose two methods for constructing a substantial quantity of essentially controllable graphs, with a focus on utilizing essentially controllable circle graphs as the foundation. For a special directed graph-OT tree, the controllability is shown to be related with its substructure-paths. This promotes the establishment of a sufficient and necessary condition for controllability. Finally, a method is presented to check the controllable subspace by identifying the left eigenvectors and generalized left eigenvectors.

Analysis of Students’ Positive Emotion and Smile Intensity Using Sequence-Relative Key-Frame Labeling and Deep-Asymmetric Convolutional Neural Network

Zhenzhen Luo, Xiaolu Jin, Yong Luo, Qiangqiang Zhou, Xin Luo

, Available online

[Abstract](7) [FullText HTML] (0) [PDF 59344KB](4)

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Positive emotional experiences can improve learning efficiency and cognitive ability, stimulate students’ interest in learning, and improve teacher-student relationships. However, positive emotions in the classroom are primarily identified through teachers’ observations and postclass questionnaires or interviews. The expression intensity of students, which is extremely important for fine-grained emotion analysis, is not considered. Hence, a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented. This method aims to recognize the positive emotion levels of a student in an end-to-end framework. First, the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity. Then, a deep-asymmetric convolutional neural network learns the expression model through dual neural networks, to enhance the stability of the network model and avoid the extreme attention region learned. Further, dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information. Thus, diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives. Finally, comparative experiments for the convergence speed, model-training parameters, confusion matrix, and classification probability are performed. The proposed method was applied to a real classroom scene to analyze the emotions of students. Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.

Robust Predefined-Time Control for Optimal Formation of Networked Mobile Vehicle Systems

Jing-Zhe Xu, Zhi-Wei Liu, Dingxin He, Ming-Feng Ge, Ming Chi

, Available online

[Abstract](10) [FullText HTML] (0) [PDF 628KB](7)

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Bearings-Only Target Motion Analysis Via Deep Reinforcement Learning

Chengyi Zhou, Meiqin Liu, Senlin Zhang, Ronghao Zheng, Shanling Dong

, Available online

[Abstract](5) [FullText HTML] (0) [PDF 334KB](0)

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Applications of Domain Generalization to Machine Fault Diagnosis: A Survey

Yongyi Chen, Dan Zhang, Ruqiang Yan, Min Xie

, Available online , doi: 0.1109/JAS.2025.125120

[Abstract](27) [FullText HTML] (0) [PDF 2681KB](7)

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In actual industrial scenarios, the variation of operating conditions, the existence of data noise, and failure of measurement equipment will inevitably affect the distribution of perceptive data. Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed, and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data. Domain generalization (DG) aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training. The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained. In this article, for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years. Specifically, we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations. Then, the recent trend of DG for fault diagnosis is also analyzed. Finally, the existing problems and future prospect is performed.

A Transactional-Behavior-Based Hierarchical Gated Network for Credit Card Fraud Detection

Yu Xie, MengChu Zhou, Guanjun Liu, Honghao Zhu, Lifei Wei, Pasquale De Meo

, Available online , doi: 10.1109/JAS.2025.125243

[Abstract](25) [FullText HTML] (0) [PDF 11494KB](4)

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The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users’ historical and current transactional behaviour. Such information is crucial for revealing users’ sequential behaviour patterns. Our approach, called Transactional-behaviour-based Hierarchical Gated Network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art.

Multi-Spacecraft Formation Control Under False Data Injection Attack: A Cross Layer Fuzzy Game Approach

Yifan Zhong, Yuan Yuan, Huanhuan Yuan, Mengbi Wang, Huaping Liu

, Available online , doi: 10.1109/JAS.2024.124872

[Abstract](90) [FullText HTML] (0) [PDF 1684KB](20)

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In this paper, we address a cross-layer resilient control issue for a kind of multi-spacecraft system (MSS) under attack. Attackers with bad intentions use the false data injection (FDI) attack to prevent the MSS from reaching the goal of consensus. In order to ensure the effectiveness of the control, the embedded defender in MSS preliminarily allocates the defense resources among spacecrafts. Then, the attacker selects its target spacecrafts to mount FDI attack to achieve the maximum damage. In physical layer, a Nash equilibrium (NE) control strategy is proposed for MSS to quantify system performance under the effect of attacks by solving a game problem. In cyber layer, a fuzzy Stackelberg game framework is used to examine the rivalry process between the attacker and defender. The strategies of both attacker and defender are given based on the analysis of physical layer and cyber layer. Finally, a simulation example is used to test the viability of the proposed cross layer fuzzy game algorithm.

Dissecting and Mitigating Semantic Discrepancy in Stable Diffusion for Image-to-Image Translation

Yifan Yuan, Guanqun Yang, James Z. Wang, Hui Zhang, Hongming Shan, Fei-Yue Wang, Junping Zhang

, Available online , doi: 10.1109/JAS.2024.124800

[Abstract](106) [FullText HTML] (0) [PDF 96267KB](19)

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Finding suitable initial noise that retains the original image’s information is crucial for image-to-image (I2I) translation using text-to-image (T2I) diffusion models. A common approach is to add random noise directly to the original image, as in SDEdit. However, we have observed that this can result in “semantic discrepancy” issues, wherein T2I diffusion models misinterpret the semantic relationships and generate content not present in the original image. We identify that the noise introduced by SDEdit disrupts the semantic integrity of the image, leading to unintended associations between unrelated regions after U-Net upsampling. Building on the widely-used latent diffusion model, Stable Diffusion, we propose a training-free, plug-and-play method to alleviate semantic discrepancy and enhance the fidelity of the translated image. By leveraging the deterministic nature of denoising diffusion implicit models (DDIMs) inversion, we correct the erroneous features and correlations from the original generative process with accurate ones from DDIM inversion. This approach alleviates semantic discrepancy and surpasses recent DDIM-inversion-based methods such as PnP with fewer priors, achieving a speedup of 11.2 times in experiments conducted on COCO, ImageNet, and ImageNet-R datasets across multiple I2I translation tasks. The codes are available at https://github.com/Sherlockyyf/Semantic_Discrepancy.

A Homotopy Method for Continuous-Time Model-Free LQR Control Based on Policy Iteration

Wenwu Fan, Junlin Xiong

, Available online , doi: 10.1109/JAS.2025.125132

[Abstract](43) [FullText HTML] (0) [PDF 1038KB](16)

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In recent years, reinforcement learning control theory has been well developed. However, model-free value iteration needs many iterations to achieve the desired precision, and model-free policy iteration requires an initial stabilizing control policy. It is significant to propose a fast model-free algorithm to solve the continuous-time linear quadratic control problem without an initial stabilizing control policy. In this paper, we construct a homotopy path on which each point corresponds to an linear quadratic regulator problem. Based on policy iteration, model-based and model-free homotopy algorithms are proposed to solve the optimal control problem of continuous-time linear systems along the homotopy path. Our algorithms are speeded up using first-order differential information and do not require an initial stabilizing control policy. Finally, several practical examples are used to illustrate our results.

An Intelligent Optimization Strategy for Blast Furnace Charging Operation Considering Three-Dimensional Burden Surface Shape

Jicheng Zhu, Zhaohui Jiang, Dong Pan, Haoyang Yu, Chuan Xu, Ke Zhou, Weihua Gui

, Available online , doi: 10.1109/JAS.2025.125192

[Abstract](67) [FullText HTML] (0) [PDF 4132KB](20)

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Today, a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace (BF). Previous research on charging operations always focused on the two-dimensional shape of the burden surface (i.e., a single radial profile) while neglecting the unique feature of global dissymmetry, severely restricting the development of precise charging. For this reason, this study proposes an innovative optimization strategy for the charging operation under the three-dimensional burden surface, which is the first attempt in this field. First, a practicable region partitioning scheme is introduced, and the partitioning results are then integrated with the charging mechanism to construct a three-dimensional burden surface prediction model. Next, the intrinsic relationship between the operational parameters and charging volume is revealed based on the law of mass conservation, which forms the basis for defining a novel operational parameter with variable-speed utility, referred to as the neotype charging matrix (NCM). To find the best NCM, a customized NCM optimization strategy, involving a dual constraint handling technique in conjunction with a two-stage hybrid variable differential evolution algorithm, is further developed. The industrial experiment results manifest that the partitioning scheme significantly enhances the accuracy of burden surface description. Moreover, the NCM optimization strategy offers greater flexibility and higher accuracy than current mainstream optimization strategies for the charging matrix (CM).

A Diffusion Model for Traffic Data Imputation

Bo Lu, Qinghai Miao, Yahui Liu, Tariku Sinshaw Tamir, Hongxia Zhao, Xiqiao Zhang, Yisheng Lv, Fei-Yue Wang

, Available online

[Abstract](53) [FullText HTML] (0) [PDF 2571KB](16)

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Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems (ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling etc. and now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing rates. The experimental results demonstrate that the model improves imputation accuracy and generalization capability.

Camera Planning for Physical Safety of Outdoor Electronic Devices: Perspective and Analysis

Qin Su, Lei Shu, Gerhard Petrus Hancke, Kai Huang, Edmond Nurellari, Qingsong Zhao, Nikumani Choudhury, Anakhi Hazarika

, Available online , doi: 10.1109/JAS.2025.125129

[Abstract](29) [FullText HTML] (0) [PDF 2265KB](5)

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Camera technology advancement and deployment continue to play a vital role in modern life and production, amongst other capabilities, generating relevant visual information. The challenge of optimizing the use and deployment of camera networks in various applications (e.g., surveillance, traffic monitoring, and public safety to name but just a few) has attracted considerable attention from both academia and industries. Camera planning is the first step in addressing this challenge. The surveillance objectives and scenes of a camera network dictate the modelling and optimization algorithms for camera planning. However, existing reviews have primarily focused on models or optimization algorithms, with insufficient attention given to surveillance scenes. This review aims to bridge this gap by 1) Classifying surveillance scenes into the urban environment and rural outdoor environment and comparing the surveillance requirements and challenges; 2) Summarizing the details of camera coverage optimization in the relevant literature from the perspective of deployment scenes; and 3) Proposing a new surveillance scene—Solar Insecticidal Lamps with the Internet of Things—as a case study to analyze the surveillance requirement and challenges in agricultural outdoor environment. Finally, we state the technical outlook on the physical safety of outdoor electronic devices in agriculture settings and provide insights to draw more attention and effort into this area.

Comprehensive Dynamic Model of Vertical Pneumatic Bellows Actuator System Considering Bidirectional Asymmetric Hysteresis

Huai Xiao, Xuzhi Lai, Qingxin Meng, Jinhua She, Edwardo F. Fukushima, Min Wu

, Available online , doi: 10.1109/JAS.2025.125162

[Abstract](28) [FullText HTML] (0) [PDF 4366KB](3)

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The complex nonlinear characteristics of pneumatic soft actuators, such as asymmetric hysteresis, rate-dependence, and mechanical load-dependence, pose a challenge in accurately modeling their dynamics. To address this challenge, this paper proposes a comprehensive dynamic model aimed at describing bidirectional asymmetric hysteresis, rate-dependent, and mechanical load-dependent characteristics of a vertical pneumatic bellows actuator (PBA) system. The dynamic model contains a hysteresis submodel and a load-dependent dynamic submodel. The hysteresis submodel consists of several sets of weighted double-side play (DSP) and weighted dead-zone (DZ) operators connected in series, and it is used to model the bidirectional asymmetric hysteresis of the system. The load-dependent dynamic submodel is built based on the gated recurrent unit (GRU) neural network, and it is used to fit the nonlinear relationship between the displacement of the system and the frequency of the input air pressure as well as the mechanical load. The model parameters of the hysteresis submodel and the load-dependent dynamic submodel are determined by intelligent optimization method and neural network training method, reseparately. The fitness value (FV) between the output of the dynamic model and the experimental data is calculated to be 96.1736%, demonstrating that the parameters of the dynamic model are valid. We conduct six set of experiments to compare the model output with the experimental data, and calculate the root-mean-square errors and the maximum error, respectively. The experimental results show that, the root-mean-square error remains consistently below 2.7700%, while the maximum error remains below 8.4000% across all experiments, thereby substantiating the validity and generality of the proposed model.

Path Following Control of Uncertain and Underactuated Autonomous Surface Vessels

Yang Wu, Yueying Wang, Zhiguang Feng, Xiangpeng Xie

, Available online , doi: 10.1109/JAS.2024.124713

[Abstract](74) [FullText HTML] (0) [PDF 635KB](30)

Abstract:
This paper studies two critical issues in the path following of underactuated autonomous surface vessels (ASVs): 1) most existing vector field (VF) guidances only apply to straight lines or circular paths, limiting their practical applications when dealing with curves; and 2) while most existing barrier function-based sliding mode controllers (BFBSMCs) maintain strong robustness and reduce control conservatism, they often suffer from the issue of excessive initial control force/moment magnitudes. Thus, this paper proposes a novel VF guidance law and integrates a time based generator into the design of BFBSMC to tackle the problems above, significantly advancing the practical application of path following of ASVs in engineering contexts. Simulations verifies the theoretical findings above.

Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

Xiaorui Li, Xiaojuan Ban, Haoran Qiao, Zhaolin Yuan, Hong-Ning Dai, Chao Yao, Yu Guo, Mohammad S. Obaidat, George Q. Huang

, Available online

[Abstract](52) [FullText HTML] (0) [PDF 2857KB](15)

Abstract:
In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network (MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant’s heat transfer tube dataset, it captures 90% of defect instances with 75% middle localization F1 score.

Self-cumulative Contrastive Graph Clustering

Xiaoqiang Yan, Kun Deng, Quan Zou, Zhen Tian, Hui Yu

, Available online , doi: 10.1109/JAS.2024.125025

[Abstract](41) [FullText HTML] (1) [PDF 1455KB](15)

Abstract:
Contrastive graph clustering (CGC) has become a prominent method for self-supervised representation learning by contrasting augmented graph data pairs. However, the performance of CGC methods critically depends on the choice of data augmentation, which usually limits the capacity of network generalization. Besides, most existing methods characterize positive and negative samples based on the nodes themselves, ignoring the influence of neighbors with different hop numbers on the node. In this study, a novel self-cumulative contrastive graph clustering (SC-CGC) method is devised, which is capable of dynamically adjusting the influence of neighbors with different hops. Our intuition is that better neighbors are closer and distant ones are further away in their feature space, thus we can perform neighbor contrasting without data augmentation. To be specific, SC-CGC relies on two neural networks, i.e., autoencoder network (AE) and graph autoencoder network (GAE), to encode the node information and graph structure, respectively. To make these two networks interact and learn from each other, a dynamic fusion mechanism is devised to transfer the knowledge learned by AE to the corresponding GAE layer by layer. Then, a self-cumulative contrastive loss function is designed to characterize the structural information by dynamically accumulating the influence of the nodes with different hops. Finally, our approach simultaneously refines the representation learning and clustering assignments in a self-supervised manner. Extensive experiments on 8 realistic datasets demonstrate that SC-CGC consistently performs better over SOTA techniques.

Nonlinear Control for Unstable Networked Plants in the Presence of Actuator and Sensor Limitations Using Robust Right Coprime Factorization

Yuanhong Xu, Mingcong Deng

, Available online , doi: 10.1109/JAS.2024.124854

[Abstract](54) [FullText HTML] (0) [PDF 2160KB](13)

Abstract:
In this paper, a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed. The actuator is limited by upper and lower constraints and the sensor in the feedback loop is subjected to network-induced unknown time-varying delay and noise. With this nonlinear control method, we first employ right coprime factorization based on isomorphism and operator theory to factorize the plant, so that bounded input bounded output (BIBO) stability can be guaranteed. Next, continuous-time generalized predictive control (CGPC) is utilized for the unstable operator of the right coprime factorized plant to guarantee inner stability and enables the closed-loop dynamics of the system with predictive characteristics. Meanwhile, a second-DoF (degrees of freedom) switched controller that satisfies a perturbed Bezout identity and a robustness condition is designed. By using the CGPC controller that possesses predictive behavior and the second-DoF switched stabilizer, the overall stability of the plant subjected to actuator limitations is guaranteed. To address sensor limitations that exist in networked plants in the form of delay and noise which often cause system performance degradation, we implement an identity operator definition in the feedback loop to compensate for these adverse effects. Further, a pre-operator is designed to ensure that the plant output tracks the reference input. Finally, the effectiveness of the proposed design scheme is demonstrated by simulations.

CRDet: An Artificial Intelligence-Based Framework for Automated Cheese Ripeness Assessment From Digital Images

Alessandra Perniciano, Luca Zedda, Cecilia Di Ruberto, Barbara Pes, Andrea Loddo

, Available online , doi: 10.1109/JAS.2024.125061

[Abstract](69) [FullText HTML] (0) [PDF 5395KB](23)

Abstract:
Assessing cheese quality and ripeness is a crucial challenge in the dairy industry, with significant implications for product quality, consumer satisfaction, and economic impact. Traditional evaluation methods relying on visual inspection and human expertise are susceptible to errors and time constraints. This study proposes an innovative approach leveraging machine learning and computer vision techniques for automated cheese ripeness detection to address these limitations.The key contributions of this work include the release of the first comprehensive public dataset of cheese wheel images depicting various products at different ripening stages comprising more than 775 images, CR-IDB, an extensive comparative analysis of the performance of machine learning classifiers trained with features extracted from convolutional neural networks and handcrafted descriptors, along with the evaluation of different feature selection techniques, and finally, a proposal of a novel AI-based framework built upon a Random Forest classifier for cheese ripeness detection, called CRDet.The novelty of CRDet lies in its enforceability across multiple types and dairy industries, which has not been previously addressed in the literature. Unlike earlier methodologies that focused on specific cheese types or relied on subjective visual inspections, this study introduces a comprehensive, noninvasive, and automated approach that demonstrates superior classification performance in differentiating ripeness phases. Thus, it overcomes the limitations of traditional methods and enhances the reliability of cheese ripening assessments.With performance in terms of F1 above 90%, the proposed approach reduces reliance on human expertise, ensuring efficient and reliable evaluation methods for the diverse cheese production landscape. The findings provide valuable insights into the potential of feature selection methods for advancing cheese quality analysis, with implications for the broader dairy industry.

Event-Triggered Adaptive Horizon DMPC for Discrete-Time Coupled Nonlinear Systems

Rui Guo, Jianwen Feng, Jingyi Wang, Yi Zhao

, Available online , doi: 10.1109/JAS.2024.124704

[Abstract](33) [FullText HTML] (0) [PDF 643KB](10)

Abstract:

Deep Reinforcement Learning for Zero-Shot Coverage Path Planning With Mobile Robots

José Pedro Carvalho, A. Pedro Aguiar

, Available online , doi: 10.1109/JAS.2024.125064

[Abstract](173) [FullText HTML] (0) [PDF 1606KB](63)

Abstract:
The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges, particularly Coverage Path Planning. While this task has been typically tackled with classical algorithms, these often struggle with flexibility and adaptability in unknown environments. On the other hand, recent advances in Reinforcement Learning offer promising approaches, yet a significant gap in the literature remains when it comes to generalization over a large number of parameters. This paper presents a unified, generalized framework for coverage path planning that leverages value-based deep reinforcement learning techniques. The novelty of the framework comes from the design of an observation space that accommodates different map sizes, an action masking scheme that guarantees safety and robustness while also serving as a learning-from-demonstration technique during training, and a unique reward function that yields value functions that are size-invariant. These are coupled with a curriculum learning-based training strategy and parametric environment randomization, enabling the agent to tackle complete or partial coverage path planning with perfect or incomplete knowledge while generalizing to different map sizes, configurations, sensor payloads, and sub-tasks. Our empirical results show that the algorithm can perform zero-shot learning scenarios at a near-optimal level in environments that follow a similar distribution as during training, outperforming a greedy heuristic by sixfold. Furthermore, in out-of-distribution environments, our method surpasses existing state-of-the-art algorithms in most zero-shot and all few-shot scenarios, paving the way for generalizable and adaptable path-planning algorithms.

FDTs: A Feature Disentangled Transformer for Interpretable Squamous Cell Carcinoma Grading

Pan Huang, Xin Luo

, Available online

[Abstract](104) [FullText HTML] (0) [PDF 6689KB](19)

Abstract:

Global Sampled-Data Output Feedback Stabilization for Nonlinear Systems via Intermittent Hold

Le Chang, Cheng Fu, Huanjun Zhang

, Available online

[Abstract](72) [FullText HTML] (0) [PDF 1318KB](29)

Abstract:
This paper introduces a sampled-data and intermittent-hold controller for nonlinear feedforward systems. The intermittent hold allows the control signal to be held in a portion of each sampled period, which does not require the control to be persistently implemented, and thus has less control time. But, less control time degrades the performance of a continuous-time control system or even destabilizes it, especially when the holding portion is sufficiently small. To tackle this obstacle, we first introduce the notion of activating rate to describe the intermittent hold, and give the sampled-data and intermittent-hold controller based on some tuning parameters. Then it is proved that for any activating rate, these parameters can be designed to achieve the stability of the considered systems under appropriately choosing the sampling size. Finally, simulation examples are given to illustrate the effectiveness of the proposed method.

Fixed-Time Stability of Random Nonlinear Systems

Fuyong Wang, Jiayi Gong, Zhongxin Liu, Fei Chen

, Available online , doi: 10.1109/JAS.2024.124353

[Abstract](56) [FullText HTML] (0) [PDF 471KB](14)

Abstract:

Hazard-Aware Weighted Advantage Combination for UAV Target Tracking and Obstacle Avoidance

Lele Xu, Jian Liu, Xiaoguang Chang, Xuping Liu, Changyin Sun

, Available online , doi: 10.1109/JAS.2024.124920

[Abstract](85) [FullText HTML] (0) [PDF 4176KB](22)

Abstract:
In recent years, the rapid evolution of unmanned aerial vehicles (UAVs) has brought about transformative changes across various industries. However, addressing fundamental challenges in UAV technology, particularly target tracking and obstacle avoidance, remains crucial for wildlife protection, military industry security, etc. Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained, and cannot be quickly and effectively extended to other scenarios. To this end, we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance. First, we independently trained the UAV target tracking and obstacle avoidance using the Dueling Double Deep Q-Network reinforcement learning algorithm. Subsequently, in a multitasking scenario, we introduce the two pre-trained networks. Meanwhile, we design a weight determined by the present risk level encountered by the UAV. This weight is utilized to perform a weighted summation of the advantage values from both networks, eliminating the need for retraining to obtain the final action. We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim. The results demonstrate that our method outperforms current state-of-the-art techniques, achieving superior performance in both tracking accuracy and avoidance of collisions.

DoS Attack Schedules for Remote State Estimation in CPSs with two-hop relay networks Under Round-Robin Protocol

Shuo Zhang, Lei Miao, Xudong Zhao

, Available online , doi: 10.1109/JAS.2024.124755

[Abstract](56) [FullText HTML] (0) [PDF 369KB](14)

Abstract:

A Self-Healing Predictive Control Method for Discrete-Time Nonlinear Systems

Shulei Zhang, Runda Jia

, Available online , doi: 10.1109/JAS.2024.124620

[Abstract](66) [FullText HTML] (0) [PDF 1270KB](19)

Abstract:
In this work, a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states. First, a robust MPC controller for the normal case is constructed, which can drive the system to the equilibrium point when the closed-loop states are in the predetermined safe set. In this controller, the tubes are built based on the incremental Lyapunov function to tighten nominal constraints. To deal with the infeasible controller when abnormal states occur, a self-healing predictive control method is further proposed to realize self-healing by driving the system towards the safe set. This is achieved by an auxiliary soft-constrained recovery mechanism that can solve the constraint violation caused by the abnormal states. By extending the discrete-time robust control barrier function theory, it is proven that the auxiliary problem provides a predictive control barrier bounded function to make the system asymptotically stable towards the safe set. The theoretical properties of robust recursive feasibility and bounded stability are further analyzed. The efficiency of the proposed controller is verified by a numerical simulation of a continuous stirred-tank reactor process.

Convex Optimization-Based Model Predictive Control for Mars Ascent Vehicle Guidance System

Kun Li, Yanning Guo, Guangtao Ran, Yueyong Lyu, Guangfu Ma

, Available online , doi: 10.1109/JAS.2024.124587

[Abstract](46) [FullText HTML] (0) [PDF 486KB](13)

Abstract:

Improving Control Performance by Cascading Observers: Case of ADRC With Cascade ESO

Ahmed T.-E. Benyahia, Momir Stanković, Rafal Madonski, Oluleke Babayomi, Stojadin M. Manojlović

, Available online , doi: 10.1109/JAS.2024.124995

[Abstract](76) [FullText HTML] (0) [PDF 3229KB](23)

Abstract:
In this paper, we show the performance benefits of connecting multiple observers within a control system. We focus here on a particular observer-based control approach, namely the active disturbance rejection control (ADRC) with cascade extended state observer (ESO). For this framework, we analyze the control performance in terms of quality of observer estimation, reference tracking, disturbance rejection, sensitivity to measurement noise/unmodeled dynamics, and overall stability. A comprehensive frequency response analysis is performed to study the influence of cascading the observers on the selected quality criteria. To make the inquiry beneficial also to practitioners, FPGA-in-the-loop tests are conducted using a guided missiles gimbaled seeker. They validate the theoretical findings in discrete-time settings, where the sampling time and hardware resource requirements become a factor. The results of the investigation are distilled into guidelines for prospective users on when and how a cascade observer structure can be useful for controls.

Dynamic Process Monitoring Based on Dot Product Feature Analysis for Thermal Power Plants

Xin Ma, Tao Chen, Youqing Wang

, Available online

[Abstract](40) [FullText HTML] (0) [PDF 1321KB](21)

Abstract:
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems, such as thermal power plants being studied in this work. Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms. Mainstream dynamic algorithms rely on concatenating current measurement with past data. This work proposes a new, alternative dynamic process monitoring algorithm, using dot product feature analysis (DPFA). DPFA computes the dot product of consecutive samples, thus naturally capturing the process dynamics through temporal correlation. At the same time, DPFA’s online computational complexity is lower than not just existing dynamic algorithms, but also classical static algorithms (e.g. principal component analysis and slow feature analysis). The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems: sensor bias, process fault and gain change fault. Through experiments with a numerical example and real data from a thermal power plant, the DPFA algorithm is shown to be superior to the state-of-the-art methods, in terms of better monitoring performance (fault detection rate and false alarm rate) and lower computational complexity.

Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

Ying Chen, Feilong Lin, Zhongyu Chen, Changbing Tang, Cailian Chen

, Available online , doi: 10.1109/JAS.2024.124626

[Abstract](42) [FullText HTML] (0) [PDF 1888KB](7)

Abstract:
The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchain-based peer-to-peer (P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users’ privacy via blockchain-based smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable, and achieves 90% fairness of enterprises.

A Learning-Based Passive Resilient Controller for Cyber-Physical Systems: Countering Stealthy Deception Attacks and Complete Loss of Actuators Control Authority

Liang Xin, Zhi-Qiang Long

, Available online , doi: 10.1109/JAS.2024.124683

[Abstract](85) [FullText HTML] (0) [PDF 2804KB](22)

Abstract:
Cyber-physical systems (CPSs) are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world, which is augmented by Internet connectivity. This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs. However, current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks (SDAs) due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks. Moreover, some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority. To address these issues, we introduce a novel learning-based passive resilient controller (LPRC). Our approach, unlike observer-based state reconstruction, shows enhanced effectiveness in countering SDAs. We developed a safety state set, represented by an ellipsoid, to ensure CPS stability under SDA conditions, maintaining system trajectories within this set. Additionally, by employing deep reinforcement learning (DRL), the LPRC acquires the capacity to adapt and diverse evolving attack strategies. To empirically substantiate our methodology, various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.

PromptFusion: Harmonized Semantic Prompt Learning for Infrared and Visible Image Fusion

Jinyuan Liu, Xingyuan Li, Zirui Wang, Zhiying Jiang, Wei Zhong, Wei Fan, Bin Xu

, Available online , doi: 10.1109/JAS.2024.124878

[Abstract](193) [FullText HTML] (0) [PDF 14823KB](58)

Abstract:
The goal of infrared and visible image fusion (IVIF) is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene. However, existing methods struggle to effectively handle modal disparities, resulting in visual degradation of the details and prominent targets of the fused images. To address these challenges, we introduce PromptFusion, a prompt-based approach that harmoniously combines multi-modality images under the guidance of semantic prompts. Firstly, to better characterize the features of different modalities, a contourlet autoencoder is designed to separate and extract the high-/low-frequency components of different modalities, thereby improving the extraction of fine details and textures. We also introduce a prompt learning mechanism using positive and negative prompts, leveraging Vision-Language Models to improve the fusion model’s understanding and identification of targets in multi-modality images, leading to improved performance in downstream tasks. Furthermore, we employ bi-level asymptotic convergence optimization. This approach simplifies the intricate non-singleton non-convex bi-level problem into a series of convergent and differentiable single optimization problems that can be effectively resolved through gradient descent. Our approach advances the state-of-the-art, delivering superior fusion quality and boosting the performance of related downstream tasks. Project page: https://github.com/hey-it-s-me/PromptFusion

A Verification Theorem for Feedback Nash Equilibrium in Multiple-Player Nonzero-Sum Impulse Game

Ruihai Li, Yaoyao Tan, Xiaojie Su, Jiangshuai Huang

, Available online

[Abstract](31) [FullText HTML] (0) [PDF 266KB](3)

Abstract:

Enhanced Tube-Based Event-Triggered Stochastic Model Predictive Control With Additive Uncertainties

Chenxi Gu, Xinli Wang, Kang Li, Xiaohong Yin, Shaoyuan Li, Lei Wang

, Available online

[Abstract](51) [FullText HTML] (0) [PDF 2596KB](14)

Abstract:
This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant (LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system uncertainties. Assisted with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of an HVAC system confirm the efficacy of the proposed control.

Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-dendrite Spiking Neuron and Dynamic Thresholds

Xingyue Liang, Qiaoyun Wu, Yun Zhou, Chunyu Tan, Hongfu Yin, Changyin Sun

, Available online , doi: 10.1109/JAS.2024.124551

[Abstract](125) [FullText HTML] (0) [PDF 3716KB](34)

Abstract:
Deep reinforcement learning (DRL) achieves success through the representational capabilities of deep neural networks (DNNs). Compared to DNNs, spiking neural networks (SNNs), known for their binary spike information processing, exhibit more biological characteristics. However, the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains, directly related to the information integration and transmission in SNNs. Inspired by the advanced computational power of dendrites in biological neurons, we propose a multi-dendrite spiking neuron (MDSN) model based on Multi-compartment spiking neurons (MCN), expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane potential. We apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decision-making tasks. The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources. Our model uses a bioinspired event-enhanced dendrite structure to emphasize features. Meanwhile, by utilizing dynamic membrane potential thresholds, it adaptively maintains the homeostasis of MDSN. Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.

Cyber-Attacks with Resource Constraints on Discrete Event Systems Under Supervisory Control

Zhaoyang He, Naiqi Wu, Rong Su, Zhiwu Li

, Available online

[Abstract](79) [FullText HTML] (0) [PDF 1369KB](26)

Abstract:
With the development of cyber-physical systems, system security faces more risks from cyber-attacks. In this work, we study the problem that an external attacker implements covert sensor and actuator attacks with resource constraints (the total resource consumption of the attacks is not greater than a given initial resource of the attacker) to mislead a discrete event system under supervisory control to reach unsafe states. We consider that the attacker can implement two types of attacks: One by modifying the sensor readings observed by a supervisor and the other by enabling the actuator commands disabled by the supervisor. Each attack has its corresponding resource consumption and remains covert. To solve this problem, we first introduce a notion of combined-attackability to determine whether a closed-loop system may reach an unsafe state after receiving attacks with resource constraints. We develop an algorithm to construct a corrupted supervisor under attacks, provide a verification method for combined-attackability in polynomial time based on a plant, a corrupted supervisor, and an attacker’s initial resource, and propose a corresponding attack synthesis algorithm. The effectiveness of the proposed method is illustrated by an example.

Resilient Nonlinear MPC With a Dynamic Event-Triggered Strategy Under DoS Attacks

Shuang Shen, Runqi Chai, Yuanqing Xia, Senchun Chai

, Available online

[Abstract](29) [FullText HTML] (0) [PDF 481KB](8)

Abstract:

Consensus Control Strategy for the Treatment of Tumour With Neuroadaptive Cellular Immunotherapy

Jiayue Sun, Dongni Li, Huaguang Zhang, Lu Liu, Wenyue Zhao

, Available online

[Abstract](47) [FullText HTML] (0) [PDF 6865KB](7)

Abstract:
This paper presents a novel neuro-adaptive cellular immunotherapy control strategy that leverages the high efficiency and applicability of chimeric antigen receptor-engineered T (CAR-T) cells in treating cancer. The proposed real-time control strategy aims to maximize tumor regression while ensuring the safety of the treatment. A dynamic growth model of cancer cells under the influence of cellular immunotherapy is established for the first time, which aligns with clinical experimental results. Utilizing the backstepping method, a novel consensus reference model is designed to consider the characteristics of cancer cell changes during the treatment process and conform to clinical rules. The model is segmented and continuous, with cancer cells expected to decrease in a step-like manner. Furthermore, a prescribed performance mechanism is constructed to maintain the therapeutic effect of the proposed scheme while ensuring the transient performance of the system. Through the analysis of Lyapunov stability, all signals within the closed-loop system are proven to be semiglobally uniformly ultimately bounded (SGUUB). Simulation results demonstrate the effectiveness of the proposed control strategy, highlighting its potential for clinical application in cancer treatment.

SILIC: Intelligent On/Off Control for Networked Solar Insecticidal Lamps

Heyang Yao, Lei Shu, Yuli Yang, Miguel Martínez-García, Wei Lin

, Available online

[Abstract](91) [FullText HTML] (0) [PDF 1873KB](13)

Abstract:
The solar insecticidal lamp (SIL) is an innovative green control device. Nevertheless, a major challenge is often encountered when carrying out insecticidal work is low energy utilization efficiency. The substantial energy consumption required to turn on the SIL, coupled with the extension of insecticidal working time during the low pest activity periods, can result in low energy efficiency. Especially when the energy storage level is below 50%, the inefficient use of energy significantly reduces the effectiveness of pest control. Consequently, an ineffective on/off scheme for these lamps may lead to suboptimal energy utilization. In this paper, we present the solar insecticidal lamp intelligent energy management scheme (SIL-IEMS) to address the challenge of inefficient energy utilization in the solar insecticidal lamp internet of things (SIL-IoT). SIL-IEMS primarily utilizes genetic algorithm (GA) and greedy algorithms to optimize insecticidal working time by considering constraints such as residual energy and the number of trap pests. Comparing SIL-IEMS to the traditional remote switching method (TRSM) and the solar insecticidal lamp genetic algorithm (SILGA), our simulation results showcase its superior energy efficiency and pest control effectiveness. Particularly noteworthy is the SILIEMS’s 17.6% increase in insecticidal efficiency compared to TRSM and 6% improvement over SILGA when the SIL begins with a remaining energy level of 15%.

From Singleton to Collaboration: Robust 3D Cooperative Positioning for Intelligent Connected Vehicles Based on Hybrid Range-Azimuth-Elevation Under Zero-Trust Driving Environments

Zhenyuan Zhang, Heng Qin, Darong Huang, Xin Fang, Mu Zhou, Shenghui Guo

, Available online

[Abstract](197) [FullText HTML] (0) [PDF 9820KB](53)

Abstract:
Reliable and accurate cooperative positioning is vital to intelligent connected vehicles (ICVs), in which vehicle-vehicle relative measurements are integrated to provide stable location-aware services. However, in zero-trust autonomous driving environments, the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance. With this in mind, this paper presents an ultra-wide bandwidth (UWB) based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments. Firstly, to overcome measurement degradation under non-line-of-sight (NLOS) propagation conditions, this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter (DKF) by integrating relative range-azimuth-elevation measurements, unlike the state-of-the-art methods that rely on only one single relative range information to update motion states. More specifically, in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack (either false data injection (FDI) or denial of service (DoS)), we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks. To this end, a singular-value decomposition (SVD)-assisted decoupled DKF algorithm is proposed in this work, in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals. To verify the effectiveness in practical 3D NLOS scenarios, we design an intelligent connected multi-robot platform based on a robot operating system (ROS) and UWB technology. Consequently, extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks, especially in the case of hybrid FDI and DoS attacks. In addition, several critical discussions, including the impact of attack parameters, resilience assessment, and a comparison with event-triggered methods, are provided in this work. Moreover, a demo video has been uploaded in the supplementary materials for a detailed presentation.

Fault Warning of Satellite Momentum Wheels With a Lightweight Transformer Improved by FastDTW

Yiming Gao, Shi Qiu, Ming Liu, Lixian Zhang, Xibin Cao

, Available online , doi: 10.1109/JAS.2024.124689

[Abstract](71) [FullText HTML] (0) [PDF 1330KB](16)

Abstract:
The momentum wheel assumes a dominant role as an inertial actuator for satellite attitude control systems. Due to the effects of structural aging and external interference, the momentum wheel may experience the gradual emergence of irreversible faults. These fault features will become apparent in the telemetry signal transmitted by the momentum wheel. This paper introduces ADTWformer, a lightweight model for long-term prediction of time series, to analyze the time evolution trend and multi-dimensional data coupling mechanism of satellite momentum wheel faults. Moreover, the incorporation of the approximate Markov blanket with the maximum information coefficient presents a novel methodology for performing correlation analysis, providing significant perspectives from a data-centric standpoint. Ultimately, the creation of an adaptive alarm mechanism allows for the successful attainment of the momentum wheel fault warning by detecting the changes in the health status curves. The analysis methodology outlined in this article has exhibited positive results in identifying instances of satellite momentum wheel failure in two scenarios, thereby showcasing considerable promise for large-scale applications.

Nonlinear Integral-Ameliorated Model for Dynamic Convex Optimization With Perturbance Considered

Kangze Zheng, Yunong Zhang

, Available online

[Abstract](40) [FullText HTML] (0) [PDF 8944KB](8)

Abstract:
This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints. They pose a challenge as their optimal solutions evolve with time. Traditional iteration-based methods that exactly solve the problem at each time instant, fail to precisely and real-time track the solution due to computational and communication bottlenecks. Our model, through rigorous theoretical analyses, is able to reduce the optimality gap (i.e., the difference between the model state and optimal solution) to zero in a finite time, and thus, track the solution online. Besides, perturbance is taken into account. We prove that under certain conditions, our model can totally tolerate an important kind of noise that we call “error-related noise”. In numerical experiments, compared with six existing methods, our model exhibits superior robustness when contaminated by the error-related noise. The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information, and introducing an integral term as well as the class $ {{{\mathrm{C}}}^0_\text{L}} $ functions to enhance convergence and noise resistance. Finally, we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations. The results indicate that our model achieves the best performance among various models employed within the framework.

Joint Super-Resolution and Nonuniformity Correction Model for Infrared Light Field Images Based on Frequency Correlation Learning

You Du, Yong Ma, Jun Huang, Xiaoguang Mei, Jinhui Qin, Fan Fan

, Available online , doi: 10.1109/JAS.2024.124881

[Abstract](104) [FullText HTML] (0) [PDF 30190KB](19)

Abstract:
Super-resolution (SR) for the camera array-based infrared light field (IRLF) images aims to reconstruct high-resolution sub-aperture images (SAIs) from their low-resolution counterparts. Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band. However, they fail to adaptively correct the nonuniform noise in IRLF images, resulting in over-smoothness or artifacts in their results. This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity. The main idea is to decompose the structure and nonuniform noise into high- and low-frequency components and then learn the frequency correlations to help correct the nonuniformity. To learn the frequency correlation, intra- and inter-frequency units are designed. The former learns the correlation of neighboring pixels within each component, aiming to reconstruct the structure and coarsely remove nonuniform noise. The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise. Both units are equipped with our designed triple-attention mechanism, which can jointly exploit spatial, angular, and frequency information. Moreover, we collected two real-world IRLF-image datasets with significant nonuniformity, which can be used as a common base in the field. Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts. The code is available at https://github.com/DuYou2023/IRLF-FSR.

GT-A²T: Graph Tensor Alliance Attention Network

Ling Wang, Kechen Liu, Ye Yuan

, Available online , doi: 10.1109/JAS.2024.124863

[Abstract](87) [FullText HTML] (0) [PDF 592KB](22)

Abstract:

Fuzzy Prescribed-Time Control for Uncertain Nonlinear Pure Feedback Systems

Qidong Li, Changchun Hua, Kuo Li

, Available online , doi: 10.1109/JAS.2024.124848

[Abstract](82) [FullText HTML] (0) [PDF 1116KB](19)

Abstract:

Latent-Factorization-of-Tensors-Incorporated Battery Cycle Life Prediction

Minzhi Chen, Li Tao, Jungang Lou, Xin Luo

, Available online , doi: 10.1109/JAS.2024.124602

[Abstract](67) [FullText HTML] (0) [PDF 2414KB](14)

Abstract:

Multi-Phase Degradation Modeling Based on Uncertain Random Process for Remaining Useful Life Prediction Under Triple Uncertainties

Xuerui Cao, Kaixiang Peng, Ruihua Jiao

, Available online

[Abstract](32) [FullText HTML] (0) [PDF 3338KB](6)

Abstract:
Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure, degradations of some equipment are characterized by multi-phase and jumps. Meanwhile, equipment is subject to inherent fluctuations, limited data and imperfect measurements resulting in aleatory, epistemic and measurement uncertainties of the degradation process. This paper proposes a degradation model and remaining useful life (RUL) prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps. First, a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes. Afterward, the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time. A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data. Furthermore, the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering. Finally, the effectiveness of the method is verified by simulation example and practical case.

Feature-Driven Variational Mesh Denoising

Jianbin Yang, Cong Wang, Hui Hou, Mingyuan Wang, Xuelong Li

, Available online , doi: 10.1109/JAS.2024.124923

[Abstract](29) [FullText HTML] (0) [PDF 1811KB](6)

Abstract:
This work elaborates an innovative mesh denoising approach that combines feature recovery and denoising in an alternating manner. It proposes a feature-driven variational model and introduces an iterative scheme that alternates between feature recovery and the denoising process. The main idea is to estimate feature candidates, filter noisy face normals in the smooth (non-feature) domain, and utilize erosion and dilation operators on the feature candidates. By imposing connectivity constraints on normal vectors with large amplitude variations, the proposed scheme effectively removes noise and progressively recovers both sharp and small-scale features during the iterative process. To validate its effectiveness, this work conducts extensive numerical experiments on both simulated and real-scanned data. The results demonstrate significant improvements in noise reduction and feature preservation compared to existing methods.

Robust Pose Graph Optimization Against Outliers Using Consistency Credibility Factor

Jie Cai, Guoliang Wei, Wangyan Li, Yaolei Wang

, Available online , doi: 10.1109/JAS.2023.123897

[Abstract](52) [FullText HTML] (0) [PDF 513KB](6)

Abstract:

A Game-Theoretic Approach to Solving the Roman Domination Problem

Xiuyang Chen, Changbing Tang, Zhao Zhang, Guanrong Chen

, Available online , doi: 10.1109/JAS.2023.123840

[Abstract](53) [FullText HTML] (0) [PDF 2230KB](11)

Abstract:
The Roman domination problem is an important combinatorial optimization problem that is derived from an old story of defending the Roman Empire and now regains new significance in cyber space security, considering backups in the face of a dynamic network security requirement. In this paper, firstly, we propose a Roman domination game (RDG) and prove that every Nash equilibrium (NE) of the game corresponds to a strong minimal Roman dominating function (S-RDF), as well as a Pareto-optimal solution. Secondly, we show that RDG is an exact potential game, which guarantees the existence of an NE. Thirdly, we design a game-based synchronous algorithm (GSA), which can be implemented distributively and converge to an NE in $ O(n)$ rounds, where n is the number of vertices. In GSA, all players make decisions depending on local information. Furthermore, we enhance GSA to be enhanced GSA (EGSA), which converges to a better NE in $ O(n^2)$ rounds. Finally, we present numerical simulations to demonstrate that EGSA can obtain a better approximate solution in promising computation time compared with state-of-the-art algorithms.

Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP

Siwei Lou, Chunjie Yang, Zhe Liu, Shaoqi Wang, Hanwen Zhang, Ping Wu

, Available online , doi: 10.1109/JAS.2024.124821

[Abstract](108) [FullText HTML] (0) [PDF 18758KB](33)

Abstract:
Data-driven techniques are reshaping blast furnace iron-making process (BFIP) modeling, but their “black-box” nature often obscures interpretability and accuracy. To overcome these limitations, our mechanism and data co-driven strategy (MDCDS) enhances model transparency and molten iron quality (MIQ) prediction. By zoning the furnace and applying mechanism-based features for material and thermal trends, coupled with a novel stationary broad feature learning system (StaBFLS), interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined. Subsequently, by integrating stationary feature representation with mechanism features, our temporal matching broad learning system (TMBLS) aligns process and quality variables using MIQ as the target. This integration allows us to establish process monitoring statistics using both mechanism and data-driven features, as well as detect modeling deviations. Validated against real-world BFIP data, our MDCDS model demonstrates consistent process alignment, robust feature extraction, and improved MIQ modeling—yielding better fault detection. Additionally, we offer detailed insights into the validation process, including parameter baselining and optimization. Details of the code are available online.¹

Federated Experiments: Generative Causal Inference Powered by LLM-based Agents Simulation and RAG-based Domain Docking

De-Yu Zhou, Xiao Xue, Qun Ma, Chao Guo, Li-Zhen Cui, Yong-Lin Tian, Jing Yang, Fei-Yue Wang

, Available online , doi: 10.1109/JAS.2024.124671

[Abstract](161) [FullText HTML] (0) [PDF 682KB](60)

Abstract:

Multi-Interval-Aggregation Failure Point Approximation for Remaining Useful Life Prediction

Linchuan Fan, Xiaolong Chen, Shuo Li, Yi Chai

, Available online , doi: 10.1109/JAS.2024.124593

[Abstract](105) [FullText HTML] (0) [PDF 825KB](27)

Abstract:

Data-Driven Iterative Learning Consensus Tracking Based on Robust Neural Models for Unknown Heterogeneous Nonlinear Multiagent Systems With Input Constraints

Chong Zhang, Yunfeng Hu, TingTing Wang, Xun Gong, Hong Chen

, Available online

[Abstract](73) [FullText HTML] (0) [PDF 409KB](18)

Abstract:

Optimal Secure Control of Networked Control Systems Under False Data Injection Attacks: A Multi-Stage Attack-Defense Game Approach

Dajun Du, Yi Zhang, Baoyue Xu, Minrui Fei

, Available online , doi: 10.1109/JAS.2023.124005

[Abstract](85) [FullText HTML] (0) [PDF 878KB](23)

Abstract:

Distributed Optimal Formation Control of Unmanned Aerial Vehicles: Theory and Experiments

Gang Wang, Zhenhong Wei, Peng Li

, Available online , doi: 10.1109/JAS.2024.124518

[Abstract](123) [FullText HTML] (0) [PDF 4081KB](44)

Abstract:

Accumulative-Error-Based Event-Triggered Control for Discrete-Time Linear Systems: A Discrete-Time Looped Functional Method

Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Bao-Lin Zhang

, Available online , doi: 10.1109/JAS.2024.124476

[Abstract](250) [FullText HTML] (5) [PDF 1585KB](21)

Abstract:
This paper is concerned with event-triggered control of discrete-time systems with or without input saturation. First, an accumulative-error-based event-triggered scheme is devised for control updates. When the accumulated error between the current state and the latest control update exceeds a certain threshold, an event is triggered. Such a scheme can ensure the event-generator works at a relatively low rate rather than falls into hibernation especially after the system steps into its steady state. Second, the looped functional method for continuous-time systems is extended to discrete-time systems. By introducing an innovative looped functional that links the event-triggered scheme, some sufficient conditions for the co-design of control gain and event-triggered parameters are obtained in terms of linear matrix inequalities with a couple of tuning parameters. Then, the proposed method is applied to discrete-time systems with input saturation. As a result, both suitable control gains and event-triggered parameters are also co-designed to ensure the system trajectories converge to the region of attraction. Finally, an unstable reactor system and an inverted pendulum system are given to show the effectiveness of the proposed method.

Non-Singular Practical Fixed-time Prescribed Performance Adaptive Fuzzy Consensus Control for Multi-Agent Systems Based on an Observer

Chi Ma, Dianbiao Dong

, Available online , doi: 10.1109/JAS.2024.124428

[Abstract](116) [FullText HTML] (0) [PDF 2534KB](46)

Abstract:
In this paper, the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states, nonlinearities, and non-strict feedback. To mitigate the nonlinearity, a fuzzy logic algorithm is applied to approximate the intrinsic dynamics of the system. Furthermore, a fuzzy logic system state observer based on leader state information is designed to address the partial unobservability of followers. Subsequently, the power integral method is incorporated into the backstepping approach to avoid singularities in the fixed-time controller. A command filter method is introduced into the standard backstepping approach to reduce the computational complexity of controller design. Then, a non-singular fixed-time adaptive control strategy with prescribed performance is proposed by constraining the tracking error within a prescribed range. Rigorous theoretical analysis ensures the convergence of consensus error in the multi-agent system to the prescribed performance region within a fixed time. Finally, the practicality of the algorithm is validated through numerical simulations.

Distributed Finite-Time Formation Control of Multiple Mobile Robot Systems Without Global Information

Xunhong Sun, Haibo Du, Weile Chen, Wenwu Zhu

, Available online , doi: 10.1109/JAS.2023.123981

[Abstract](124) [FullText HTML] (0) [PDF 564KB](46)

Abstract:

Semi-Decentralized Convex Optimization on

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Weijian Li, Peng Yi

, Available online , doi: 10.1109/JAS.2024.124356

[Abstract](73) [FullText HTML] (0) [PDF 377KB](15)

Abstract:

New Controllability Criteria for Linear Switched and Impulsive Systems

Jiayuan Yan, Bin Hu, Zhi-Hong Guan, Yandong Hou, Lei Shi

, Available online , doi: 10.1109/JAS.2024.124272

[Abstract](69) [FullText HTML] (0) [PDF 356KB](11)

Abstract:

Set-Valued State Estimation of Nonlinear Discrete-Time Systems and Its Application to Attack Detection

Hao Liu, Qing-Long Han, Yuzhe Li

, Available online

[Abstract](129) [FullText HTML] (0) [PDF 1560KB](30)

Abstract:
This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded (UBB) noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets. First, properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity. Then, the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets. Based on generalized intersection, the utilization of set-based estimation for attack detection is analyzed. Finally, an example is given to show the efficiency of our results.