IEEE/CAA Journal of Automatica Sinica

Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear Systems

Ding Wang, Lingzhi Hu, Xiaoli Li, Junfei Qiao

, Available online , doi: 10.1109/JAS.2024.124989

[Abstract](36) [FullText HTML] (0) [PDF 1165KB](27)

Abstract:
In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator faults. First, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network (NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control, respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov principle. The tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

The Security of Using Large Language Models: A Survey With Emphasis on ChatGPT

Wei Zhou, Xiaogang Zhu, Qing-Long Han, Lin Li, Xiao Chen, Sheng Wen, Yang Xiang

, Available online , doi: 10.1109/JAS.2024.124983

[Abstract](190) [FullText HTML] (0) [PDF 1381KB](52)

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ChatGPT is a powerful artificial intelligence (AI) language model that has demonstrated significant improvements in various natural language processing (NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse, attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions. Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

Qinglai Wei, Shanshan Jiao, Qi Dong, Fei-Yue Wang

, Available online , doi: 10.1109/JAS.2024.124773

[Abstract](53) [FullText HTML] (0) [PDF 2078KB](28)

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This paper highlights the utilization of parallel control and adaptive dynamic programming (ADP) for event-triggered robust parallel optimal consensus control (ETRPOC) of uncertain nonlinear continuous-time multiagent systems (MASs). First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique’s introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an event-triggered mechanism is adopted to save communication resources while ensuring the system’s stability. The coupled Hamilton-Jacobi (HJ) equation’s solution is approximated using a critic neural network (NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded (UUB). Finally, numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

Distributed Economic Dispatch Algorithms of Microgrids Integrating Grid-Connected and Isolated Modes

Zhongxin Liu, Yanmeng Zhang, Yalin Zhang, Fuyong Wang

, Available online , doi: 10.1109/JAS.2024.124695

[Abstract](49) [FullText HTML] (0) [PDF 2482KB](12)

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The economic dispatch problem (EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size, the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching. Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.

Fixed-Time Stability of Random Nonlinear Systems

Fuyong Wang, Jiayi Gong, Zhongxin Liu, Fei Chen

, Available online , doi: 10.1109/JAS.2024.124353

[Abstract](21) [FullText HTML] (0) [PDF 471KB](5)

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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](21) [FullText HTML] (0) [PDF 1386KB](9)

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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.

Predefined-Time Constrained Optimization of Multi-Agent Systems Under Impulsive Effects

Zhuyan Jiang, Xiaoyang Liu, Xiang Jiang, Jinde Cao

, Available online , doi: 10.1109/JAS.2024.124710

[Abstract](24) [FullText HTML] (0) [PDF 1363KB](9)

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

Yifan Yuan, Guanqun Yang, James Z. Wang, Hui Zhang, Hongming Shan, Feiyue Wang, Junping Zhang

, Available online , doi: 10.1109/JAS.2024.124800

[Abstract](39) [FullText HTML] (0) [PDF 96202KB](11)

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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.

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](26) [FullText HTML] (0) [PDF 5402KB](7)

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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.

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](60) [FullText HTML] (0) [PDF 4176KB](19)

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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.

Value Iteration-Based Distributed Adaptive Dynamic Programming for Multi-Player Differential Game With Incomplete Information

Yun Zhang, Yuqi Wang, Yunze Cai

, Available online

[Abstract](25) [FullText HTML] (0) [PDF 1710KB](20)

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In this paper, a distributed adaptive dynamic programming (ADP) framework based on value iteration is proposed for multi-player differential games. In the game setting, players have no access to the information of others’ system parameters or control laws. Each player adopts an on-policy value iteration algorithm as the basic learning framework. To deal with the incomplete information structure, players collect a period of system trajectory data to compensate for the lack of information. The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy. Theoretical analysis shows that by adopting proximal policy searching rules, the approximated policies can converge to a neighborhood of equilibrium policies. The efficacy of our method is illustrated by three examples, which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.

Impulsive Consensus of MASs With Input Saturation and DoS Attacks

Xuyang Wang, Dengxiu Yu, Xiaodi Li

, Available online

[Abstract](13) [FullText HTML] (0) [PDF 9768KB](10)

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This paper investigates the secure impulsive consensus of Lipschitz-type nonlinear multi-agent systems (MASs) with input saturation. According to the coupling of input saturation and denial of service (DoS) attacks, impulsive control for MASs becomes extremely challenging. Considering general DoS attacks, this paper provides the sufficient conditions for the almost sure consensus of the MASs with input saturation, where the error system can achieves almost sure local exponential stability. Through linear matrix inequalities (LMIs), the relation between the trajectory boundary and DoS attacks is characterized, and the trajectory boundary is estimated. Furthermore, an optimization method of the domain of attraction is proposed to maximize the size. And a non-conservative and practical boundary is proposed to characterize the effect of DoS attacks on MASs. Finally, considering a multi-agent system with typical Chua’s circuit dynamic model, an example is provided to illustrate the theorems’ correctness.

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](35) [FullText HTML] (0) [PDF 369KB](9)

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

Shulei Zhang, Runda Jia

, Available online , doi: 10.1109/JAS.2024.124620

[Abstract](39) [FullText HTML] (0) [PDF 1270KB](17)

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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](25) [FullText HTML] (0) [PDF 486KB](11)

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Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

Yuhan Wang, Zhuping Wang, Hao Zhang, Huaicheng Yan

, Available online , doi: 10.1109/JAS.2023.123924

[Abstract](23) [FullText HTML] (0) [PDF 674KB](10)

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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](55) [FullText HTML] (0) [PDF 3229KB](17)

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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.

Soft Resource Slicing for Industrial Mixed Traffic in 5G Networks

Jingfang Ding, Meng Zheng, Haibin Yu

, Available online

[Abstract](25) [FullText HTML] (0) [PDF 1266KB](12)

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From Static and Dynamic Perspectives: A Survey on Historical Data Benchmarks of Control Performance Monitoring

Pengyu Song, Jie Wang, Chunhui Zhao, Biao Huang

, Available online , doi: 10.1109/JAS.2024.124902

[Abstract](34) [FullText HTML] (0) [PDF 2385KB](12)

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In recent decades, control performance monitoring (CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark (HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly, existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives. Specifically, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis, which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

DI-YOLOv5: An Improved Dual-Wavelet-Based YOLOv5 for Dense Small Object Detection

Zi-Xin Li, Yu-Long Wang, Fei Wang

, Available online

[Abstract](30) [FullText HTML] (0) [PDF 1368KB](14)

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

Xin Ma, Tao Chen, Youqing Wang

, Available online

[Abstract](12) [FullText HTML] (0) [PDF 1321KB](11)

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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.

Residential Energy Scheduling With Solar Energy Based on Dyna Adaptive Dynamic Programming

Kang Xiong, Qinglai Wei, Hongyang Li

, Available online

[Abstract](23) [FullText HTML] (0) [PDF 4651KB](5)

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Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna action-dependent heuristic dynamic programming (Dyna-ADHDP) method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method’s excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.

Penalty Function-Based Distributed Primal-Dual Algorithm for Nonconvex Optimization Problem

Xiasheng Shi, Changyin Sun

, Available online

[Abstract](60) [FullText HTML] (0) [PDF 1319KB](35)

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This paper addresses the distributed nonconvex optimization problem, where both the global cost function and local inequality constraint function are nonconvex. To tackle this issue, the p-power transformation and penalty function techniques are introduced to reframe the nonconvex optimization problem. This ensures that the Hessian matrix of the augmented Lagrangian function becomes local positive definite by choosing appropriate control parameters. A multi-timescale primal-dual method is then devised based on the Karush-Kuhn-Tucker (KKT) point of the reformulated nonconvex problem to attain convergence. The Lyapunov theory guarantees the model’s stability in the presence of an undirected and connected communication network. Finally, two nonconvex optimization problems are presented to demonstrate the efficacy of the previously developed method.

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](24) [FullText HTML] (0) [PDF 1888KB](4)

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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](51) [FullText HTML] (0) [PDF 2804KB](14)

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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.

An Online Exploratory Maximum Likelihood Estimation Approach to Adaptive Kalman Filtering

Jiajun Cheng, Haonan Chen, Zhirui Xue, Yulong Huang, Yonggang Zhang

, Available online , doi: 10.1109/JAS.2024.125001

[Abstract](36) [FullText HTML] (0) [PDF 6559KB](20)

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Over the past few decades, numerous adaptive Kalman filters (AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation (MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation. Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation, which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy, and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.

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](105) [FullText HTML] (0) [PDF 14823KB](38)

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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

Improved Zero-Dynamics Attack Scheduling With State Estimation

Zhe Wang, Heng Zhang, Chaoqun Yang, Xianghui Cao

, Available online

[Abstract](12) [FullText HTML] (0) [PDF 924KB](4)

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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](7) [FullText HTML] (0) [PDF 266KB](1)

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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](12) [FullText HTML] (0) [PDF 2596KB](8)

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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.

A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

Tianyu Wang, Fan Zhou, Yangjie Wu, Jun Zhao, Wei Wang

, Available online

[Abstract](24) [FullText HTML] (0) [PDF 5949KB](7)

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As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status (mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a central-wise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.

Exponential Stability of Impulsive System via Saturated Sliding Mode Control

Miaomiao Yu, Xiaodi Li

, Available online

[Abstract](14) [FullText HTML] (0) [PDF 1054KB](8)

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Dynamic Event-Triggered Active Disturbance Rejection Formation Control for Constrained Underactuated AUVs

Zhiguang Feng, Sibo Yao

, Available online

[Abstract](18) [FullText HTML] (0) [PDF 1579KB](12)

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Privacy Distributed Constrained Optimization Over Time-Varying Unbalanced Networks and Its Application in Federated Learning

Mengli Wei, Wenwu Yu, Duxin Chen, Mingyu Kang, Guang Cheng

, Available online

[Abstract](83) [FullText HTML] (0) [PDF 2537KB](34)

Abstract:
This paper investigates a class of constrained distributed zeroth-order optimization (ZOO) problems over time-varying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs. We hereby propose a novel algorithm, termed the differential privacy (DP) distributed push-sum based zeroth-order constrained optimization algorithm (DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs, offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm (ZOCOA-FL) to address challenges stemming from the time-varying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares (DLS) and decentralized federated learning (DFL) tasks.

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](63) [FullText HTML] (0) [PDF 3716KB](15)

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](51) [FullText HTML] (0) [PDF 1369KB](10)

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](16) [FullText HTML] (0) [PDF 481KB](6)

Abstract:

Finite-Time Stability of Impulsive and Switched Hybrid Systems With Delay-Dependent Impulses

Taixiang Zhang, Jinde Cao, Mahmoud Abdel-Aty, Ardak Kashkynbayev

, Available online

[Abstract](13) [FullText HTML] (0) [PDF 480KB](10)

Abstract:

Neural Tucker Factorization

Peng Tang, Xin Luo

, Available online

[Abstract](11) [FullText HTML] (0) [PDF 398KB](3)

Abstract:

Constrained Networked Predictive Control for Nonlinear Systems Using a High-Order Fully Actuated System Approach

Yi Huang, Guo-Ping Liu, Yi Yu, Wenshan Hu

, Available online

[Abstract](10) [FullText HTML] (0) [PDF 562KB](7)

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](16) [FullText HTML] (0) [PDF 6865KB](4)

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.

Broad-Learning-System-Based Model-Free Adaptive Predictive Control for Nonlinear MASs Under DoS Attacks

Hongxing Xiong, Guangdeng Chen, Hongru Ren, Hongyi Li

, Available online , doi: 10.1109/JAS.2024.124929

[Abstract](49) [FullText HTML] (0) [PDF 1967KB](16)

Abstract:
In this paper, the containment control problem in nonlinear multi-agent systems (NMASs) under denial-of-service (DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control (MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.

Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

Jiaxing Li, Zidong Wang, Jun Hu, Hongli Dong, Hongjian Liu

, Available online

[Abstract](20) [FullText HTML] (0) [PDF 1334KB](7)

Abstract:
In this paper, the problem of cubature Kalman fusion filtering (CKFF) is addressed for multi-sensor systems under amplify-and-forward (AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance’s upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

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

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

, Available online

[Abstract](42) [FullText HTML] (0) [PDF 1873KB](8)

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%.

Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

Yu-Ang Wang, Zidong Wang, Lei Zou, Bo Shen, Hongli Dong

, Available online

[Abstract](114) [FullText HTML] (0) [PDF 2000KB](59)

Abstract:
In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknown-but-bounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission. A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-to-estimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks (applied to data prior to quantization) and the recovery of data (implemented before the data reaches the estimator). The watermark-based scheme is designed to be both time-varying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks, thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

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](141) [FullText HTML] (0) [PDF 9820KB](32)

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](45) [FullText HTML] (0) [PDF 1330KB](13)

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](23) [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](70) [FullText HTML] (0) [PDF 30190KB](14)

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.

Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

Dongfang Li, Jie Huang, Rob Law, Xin Xu, Limin Zhu, Edmond Q. Wu

, Available online , doi: 10.1109/JAS.2024.124632

[Abstract](48) [FullText HTML] (0) [PDF 4075KB](15)

Abstract:
This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system’s control input and improve the robots’ tracking accuracy. Moreover, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots’ run jitter. Then, Lyapunov’s theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

A Correntropy-Based Echo State Network With Application to Time Series Prediction

Xiufang Chen, Zhenming Su, Long Jin, Shuai Li

, Available online , doi: 10.1109/JAS.2024.124932

[Abstract](22) [FullText HTML] (0) [PDF 2819KB](1)

Abstract:
As a category of recurrent neural networks, echo state networks (ESNs) have been the topic of in-depth investigations and extensive applications in a diverse array of fields, with spectacular triumphs achieved. Nevertheless, the traditional ESN and the majority of its variants are devised in the light of the second-order statistical information of data (e.g., variance and covariance), while more information is neglected. In the context of information theoretic learning, correntropy demonstrates the capacity to grab more information from data. Therefore, under the guidelines of the maximum correntropy criterion, this paper proposes a correntropy-based echo state network (CESN) in which the first-order and higher-order information of data is captured, promoting robustness to noise. Furthermore, an incremental learning algorithm for the CESN is presented, which has the expertise to update the CESN when new data arrives, eliminating the need to retrain the network from scratch. Finally, experiments on benchmark problems and comparisons with existing works are provided to verify the effectiveness and superiority of the proposed CESN.

Data-Driven Fault-Tolerant Bipartite Consensus Tracking for Multi-Agent Systems With a Non-Autonomous Leader

Yan Zhou, Guanghui Wen, Jialing Zhou, Tao Yang

, Available online

[Abstract](26) [FullText HTML] (0) [PDF 692KB](11)

Abstract:

GT-A²T: Graph Tensor Alliance Attention Network

Ling Wang, Kechen Liu, Ye Yuan

, Available online , doi: 10.1109/JAS.2024.124863

[Abstract](49) [FullText HTML] (0) [PDF 592KB](16)

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](56) [FullText HTML] (0) [PDF 1116KB](12)

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](32) [FullText HTML] (0) [PDF 2414KB](11)

Abstract:

Adaptive Control of a Flexible Manipulator With Unknown Hysteresis and Intermittent Actuator Faults

Shouyan Chen, Weitian He, Zhijia Zhao, Yun Feng, Zhijie Liu, Keum-Shik Hong

, Available online

[Abstract](28) [FullText HTML] (0) [PDF 3912KB](11)

Abstract:
In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second, a novel adaptive neural network-based control scheme is proposed to cancel the unknown input hysteresis. Subsequently, by modifying the adaptive laws and local control laws, a fault-tolerant control strategy is applied to address uncertain intermittent actuator faults in a flexible manipulator system. Through the direct Lyapunov theory, the proposed scheme allows the state errors to asymptotically converge to a specified interval. Finally, the effectiveness of the proposed scheme is verified through numerical simulations and experiments.

Distributed Observer for Full-Measured Nonlinear Systems Based on Knowledge of FMCF

Haotian Xu, Shuai Liu, Yueyang Li, Ke Li

, Available online , doi: 10.1109/JAS.2024.124467

[Abstract](65) [FullText HTML] (0) [PDF 2819KB](28)

Abstract:
Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitations: They either fail to address more complex nonlinear phenomena, rely on hard-to-verify assumptions, or encounter difficulties in solving system parameters. Consequently, this paper aims to address these challenges by investigating distributed observers for nonlinear systems through the full-measured canonical form (FMCF), which is inspired by full-measured system (FMS) theory. To begin with, this study addresses the fact that the FMCF can only be obtained through the observable canonical form (OCF) in existing FMS theories. The paper demonstrates that a class of nonlinear systems can directly obtain FMCF through state space equations, independent of OCF. Also, a general method for solving FMCF in such systems is provided. Furthermore, based on the FMCF, A distributed observer is developed for nonlinear systems under two scenarios: Lipschitz conditions and open-loop bounded conditions. The paper establishes their asymptotic omniscience and demonstrates that the designed distributed observer in this study has fewer design parameters and is more convenient to construct than existing approaches. Finally, the effectiveness of the proposed methods is validated through simulation results on Van der Pol oscillators and microgrid systems.

Stability and Stabilization of Sampled-Data Based LFC for Power Systems: A Data-Driven Method

Yu-Long Fan, Chuan-Ke Zhang, Yong He

, Available online

[Abstract](46) [FullText HTML] (0) [PDF 494KB](18)

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](18) [FullText HTML] (0) [PDF 3338KB](5)

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.

Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

Fazhi Song, Ning Cui, Shuaiqi Chen, Kai Zhang, Yang Liu, Xinkai Chen, Jiubin Tan

, Available online , doi: 10.1109/JAS.2024.124968

[Abstract](42) [FullText HTML] (0) [PDF 3520KB](16)

Abstract:
The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control (ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer (ESO) based adaptive ILC approach is proposed in the frequency domain. Despite being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

Joint Probabilistic Scheduling and Resource Allocation for Wireless Networked Control Systems

Meng Zheng, Lei Zhang, Wei Liang

, Available online

[Abstract](49) [FullText HTML] (0) [PDF 746KB](12)

Abstract:

Interpretable Data-Driven Learning With Fast Ultrasonic Detection for Battery Health Estimation

Kailong Liu, Yuhang Liu, Qiao Peng, Naxin Cui, Chenghui Zhang

, Available online

[Abstract](23) [FullText HTML] (0) [PDF 2420KB](5)

Abstract:

The H_∞ Robust Stability and Performance Conditions for Uncertain Robot Manipulators

Geun Il Song, Hae Yeon Park, Jung Hoon Kim

, Available online

[Abstract](49) [FullText HTML] (0) [PDF 957KB](23)

Abstract:

Local Search-Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

Xin Liao, Khoi Hoang, Xin Luo

, Available online

[Abstract](19) [FullText HTML] (0) [PDF 653KB](6)

Abstract:

An Improved Repetitive-Control System Using a Complex-Coefficient Filter

Qicheng Mei, Jinhua She, Fei Long, Yanjun Shen

, Available online , doi: 10.1109/JAS.2024.124554

[Abstract](67) [FullText HTML] (0) [PDF 862KB](24)

Abstract:

Deep Synchronization Control for Grid-Forming Converters: A Reinforcement Learning Approach

Zhuorui Wu, Meng Zhang, Bo Fan, Yang Shi, Xiaohong Guan

, Available online

[Abstract](31) [FullText HTML] (0) [PDF 479KB](8)

Abstract:

Physical Layer Security Scheme With AoI-Awareness for Industrial IoT Based on Covert Communications

Yaping Li, Zhi-Xin Liu, Jia-Wei Su, Ya-Zhou Yuan

, Available online

[Abstract](17) [FullText HTML] (0) [PDF 452KB](3)

Abstract:

Self-Triggered Impulsive Control for Nonlinear Stochastic Systems

Tao Zhan, Yi Ji, Yabin Gao, Hongyi Li, Yuanqing Xia

, Available online

[Abstract](39) [FullText HTML] (0) [PDF 396KB](17)

Abstract:

Output Consensus of Heterogeneous Linear MASs via Adaptive Event-Triggered Feedback Combination Control

Shuo Yuan, Chengpu Yu, Jian Sun

, Available online

[Abstract](51) [FullText HTML] (0) [PDF 592KB](505)

Abstract:

Feature-Driven Variational Mesh Denoising

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

, Available online , doi: 10.1109/JAS.2024.124923

[Abstract](17) [FullText HTML] (0) [PDF 1811KB](4)

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.

On Resilience Against Cyber-Physical Uncertainties in Distributed Nash Equilibrium Seeking Strategies for Heterogeneous Games

Maojiao Ye

, Available online

[Abstract](80) [FullText HTML] (0) [PDF 2754KB](31)

Abstract:
This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems. In particular, we are concerned with parametric uncertainties in the control channel of the players. Moreover, the weights on communication links can be compromised by time-varying uncertainties, which can result from possibly malicious attacks, faults and disturbances. To deal with the unavailability of measurement of optimization errors, an output observer is constructed, based on which adaptive laws are designed to compensate for physical uncertainties. With adaptive laws, a new distributed Nash equilibrium seeking strategy is designed by further integrating consensus protocols and gradient search algorithms. Moreover, to further accommodate compromised communication weights resulting from cyber-uncertainties, the coupling strengths of the consensus module are designed to be adaptive. As a byproduct, the coupling strengths are independent of any global information. With theoretical investigations, it is proven that the proposed strategies are resilient to these uncertainties and players’ actions are convergent to the Nash equilibrium. Simulation examples are given to numerically validate the effectiveness of the proposed strategies.

Compensation for Heterogeneous Unknowns and Performance-Prescribed Consensus

Linzhen Yu, Yungang Liu

, Available online

[Abstract](37) [FullText HTML] (0) [PDF 1930KB](15)

Abstract:
In this paper, the MASs typically with heterogeneous unknown nonlinearities and nonidentical unknown control coefficients are studied. Although the model information of MASs is coarse, the leader-following consensus is still pursued, with a prescribed performance and zero consensus errors. Leveraging a powerful funnel control strategy, a fully distributed and completely relative-state-dependent protocol is designed. Distinctively, the time-varying function characterizing the performance boundary is introduced, not only to construct the funnel gains but also as an indispensable part of the protocol, enhancing the control ability and enabling the consensus errors to converge to zero (rather than a residual set). Remark that when control directions are unknown, coexisting with inherent system nonlinearities, it is essential to incorporate an additional compensation mechanism while imposing a hierarchical structure of communication topology for the control design and analysis. Simulation examples are given to illustrate the effectiveness of the theoretical results.

K-Corruption Intermittent Attacks for Violating the Codiagnosability

Ruotian Liu, Yihui Hu, Agostino Marcello Mangini, Maria Pia Fanti

, Available online , doi: 10.1109/JAS.2024.124680

[Abstract](67) [FullText HTML] (0) [PDF 2023KB](10)

Abstract:
In this work, we address the codiagnosability analysis problem of a networked discrete event system under malicious attacks. The considered system is modeled by a labeled Petri net and is monitored by a series of sites, in which each site possesses its own set of sensors, without requiring communication among sites or to any coordinators. A net is said to be codiagnosable with respect to a fault if at least one site could deduce the occurrence of this fault within finite steps. In this context, we focus on a type of malicious attack that is called stealthy intermittent replacement attack. The stealthiness demands that the corrupted observations should be consistent with the system’s normal behavior, while the intermittent replacement setting entails that the replaced transition labels must be recovered within a bounded of consecutive corrupted observations (called as K-corruption intermittent attack). Particularly, there exists a coordination between attackers that are separately effected on different sites, which holds the same corrupted observation for each common transition under attacks. From an attacker viewpoint, this work aims to design K-corruption intermittent attacks for violating the codiagnosability of systems. For this purpose, we propose an attack automaton to analyze K-corruption intermittent attack for each site, and build a new structure called complete attack graph that is used to analyze all the potential attacked paths. Finally, an algorithm is inferred to obtain the K-corruption intermittent attacks, and examples are given to show the proposed attack strategy.

GPIO-Based Continuous Sliding Mode Control for Networked Control Systems Under Communication Delays With Experiments on Servo Motors

Kamal Rsetam, Zhenwei Cao, Zhihong Man, Xian-Ming Zhang

, Available online , doi: 10.1109/JAS.2024.124812

[Abstract](114) [FullText HTML] (0) [PDF 6049KB](32)

Abstract:
To handle input and output time delays that commonly exist in many networked control systems (NCSs), a new robust continuous sliding mode control (CSMC) scheme is proposed for the output tracking in uncertain single input-single-output (SISO) networked control systems. This scheme consists of three consecutive steps. First, although the network-induced delay in those systems can be effectively handled by using Pade approximation (PA), the unmatched disturbance cames out as another difficulty in the control design. Second, to actively estimate this unmatched disturbance, a generalized proportional integral observer (GPIO) technique is utilized based on only one measured state. Third, by constructing a new sliding manifold with the aid of the estimated unmatched disturbance and states, a GPIO-based CSMC is synthesized, which is employed to cope with not only matched and unmatched disturbances, but also network-induced delays. The stability of the entire closed-loop system under the proposed GPIO-based CSMC is detailedly analyzed. The promising tracking efficiency and feasibility of the proposed control methodology are verified through simulations and experiments on Quanser’s servo module for motion control under various test conditions.

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](32) [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](35) [FullText HTML] (0) [PDF 2230KB](10)

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.

Strong Current-State Opacity Verification of Discrete-Event Systems Modeled With Time Labeled Petri Nets

Tao Qin, Li Yin, Gaiyun Liu, Naiqi Wu, Zhiwu Li

, Available online , doi: 10.1109/JAS.2024.124560

[Abstract](120) [FullText HTML] (0) [PDF 1575KB](42)

Abstract:
This paper addresses the verification of strong current-state opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path (state-event sequence with time information) π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed. Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.

Chattering-Free Fault-Tolerant Cluster Control and Fault Direction Identification for HIL UAV Swarm With Pre-Specified Performance

Pei-Ming Liu, Xiang-Gui Guo, Jian-Liang Wang, Daniel Coutinho, Lihua Xie

, Available online , doi: 10.1109/JAS.2024.124827

[Abstract](108) [FullText HTML] (0) [PDF 2944KB](27)

Abstract:
In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop (HIL) swarm unmanned aerial vehicles (UAVs) in the presence of possible nonidentical and unknown direction faults (NUDFs) in the yaw channel. The control strategy begins with the design of a pre-specified performance event-triggered observer for each individual UAV. These observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer output. It is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs’ output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.

Unifying Fixed Time and Prescribed Time Control for Strict-Feedback Nonlinear Systems

Xiang Chen, Yujuan Wang, Yongduan Song

, Available online , doi: 10.1109/JAS.2024.124401

[Abstract](42) [FullText HTML] (0) [PDF 1308KB](21)

Abstract:
This paper investigates the prescribed-time tracking control problem for a class of multi-input multi-output (MIMO) nonlinear strict-feedback systems subject to non-vanishing uncertainties. The inherent unmatched and non-vanishing uncertainties make the prescribed-time control problem become much more nontrivial. The solution to address the challenges mentioned above involves incorporating a prescribed-time filter, as opposed to a finite-time filter, and formulating a prescribed-time Lyapunov stability lemma (Lemma 5). The prescribed-time Lyapunov stability lemma is based on time axis shifting time-varying yet bounded gain, which establishes a novel link between the fixed-time and prescribed-time control method. This allows the restriction condition that the time-varying gain function must satisfy as imposed in most exist prescribed-time control works to be removed. Under the proposed control method, the desire trajectory is ensured to closely track the output of the system in prescribed time. The effectiveness of the theoretical results are verified through numerical simulation.

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](72) [FullText HTML] (0) [PDF 18758KB](26)

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](112) [FullText HTML] (0) [PDF 682KB](52)

Abstract:

Distributed State and Fault Estimation for Cyber-Physical Systems Under DoS Attacks

Limei Liang, Rong Su, Haotian Xu

, Available online , doi: 10.1109/JAS.2024.124527

[Abstract](63) [FullText HTML] (0) [PDF 507KB](29)

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](85) [FullText HTML] (0) [PDF 825KB](26)

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](57) [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](68) [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](107) [FullText HTML] (0) [PDF 4081KB](41)

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](208) [FullText HTML] (0) [PDF 1585KB](19)

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](99) [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](105) [FullText HTML] (0) [PDF 564KB](45)

Abstract:

Semi-Decentralized Convex Optimization on

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Weijian Li, Peng Yi

, Available online , doi: 10.1109/JAS.2024.124356

[Abstract](60) [FullText HTML] (0) [PDF 377KB](12)

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](43) [FullText HTML] (0) [PDF 356KB](10)

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](106) [FullText HTML] (0) [PDF 1560KB](27)

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.

A Novel Vibration-Based Self-Adapting Method to Acquire Real-Time Following Distance for Virtually Coupled Trains

Qinglai Zhang, Jianmin Gao, Qing Wu, Qinglie He, Libin Tie, Wanming Zhai, Shengyang Zhu

, Available online , doi: 10.1109/JAS.2024.124326

[Abstract](80) [FullText HTML] (0) [PDF 3816KB](11)

Abstract:
Virtual coupling (VC) is an emerging technology for addressing the shortage of rail transportation capacity. As a crucial enabling technology, the VC-specific acquisition of train information, especially train following distance (TFD), is underdeveloped. In this paper, a novel method is proposed to acquire real-time TFD by analyzing the vibration response of the front and following trains, during which only onboard accelerometers and speedometers are required. In contrast to the traditional arts of train positioning, this method targets a relative position between two adjacent trains in VC operation, rather than the global positions of the trains. For this purpose, an adaptive system containing three strategies is designed to cope with possible adverse factors in train operation. A vehicle dynamics simulation of a heavy-haul railway is implemented for the evaluation of feasibility and performance. Furthermore, a validation is conducted using a set of data measured from in-service Chinese high-speed trains. The results indicate the method achieves satisfactory estimation accuracy using both simulated and actual data. It has favorable adaptability to various uncertainties possibly encountered in train operation. Additionally, the method is preliminarily proven to adapt to different locomotive types and even different rail transportation modes. In general, such a method with good performance, low-cost, and easy implementation is promising to apply.