Journal of Automation and Intelligence最新文献

Adaptive neural network fixed-time formation control of unmanned ground vehicles 无人地面车辆自适应神经网络定时编队控制

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2026-01-19 DOI: 10.1016/j.jai.2026.01.002

Ziyi Zheng, Yang Yu, Wei Wang, Shuai Sui

引用次数: 0

Prescribed performance robust output regulation of uncertain Euler–Lagrange systems via self-learning mechanism 基于自学习机制的不确定欧拉-拉格朗日系统的规定性能鲁棒输出调节

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2025-11-24 DOI: 10.1016/j.jai.2025.11.006

Haoze Chen, Wei Liu

{"title":"Prescribed performance robust output regulation of uncertain Euler–Lagrange systems via self-learning mechanism","authors":"Haoze Chen, Wei Liu","doi":"10.1016/j.jai.2025.11.006","DOIUrl":"10.1016/j.jai.2025.11.006","url":null,"abstract":"<div><div>This paper addresses the prescribed performance robust output regulation problem for Euler–Lagrange systems, where real-time reference signal information (such as position, velocity, and acceleration) is unavailable. We propose a control law that utilizes only the position tracking error and system velocity, while incorporating a self-learning mechanism for the reference signals. Additionally, two internal models are introduced to tackle the second-order dynamics of the Euler–Lagrange system, where the desired trajectory and disturbances are characterized by outputs of exosystems. These internal models reproduce the steady-state information, solving the trajectory tracking problem under uncertain system parameters. To ensure both steady-state and transient performance, we design a control strategy that guarantees that the output position error remains within a time-varying constraint function, while also ensuring that the error converges to zero asymptotically. This approach provides an effective solution to the robust output regulation problem with prescribed performance for Euler–Lagrange systems. Finally, the effectiveness of the proposed control strategy is demonstrated through comparative simulations.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 3","pages":"Pages 247-256"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum regarding duplicated references in previously published articles in Volume 3–Volume 5 关于第3卷-第5卷以前发表的文章中重复引用的勘误

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2026-09-24 DOI: 10.1016/j.jai.2026.09.001

引用次数: 0

Supervisory control of Petri nets with uncontrollable and unobservable transitions under replacement attacks 替换攻击下具有不可控和不可观察转换的Petri网的监督控制

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2026-02-17 DOI: 10.1016/j.jai.2026.02.002

Zhenhua Yu, Yanwei Fang, Haoming Zhu, Yifei Jia, Maria Pia Fanti, Xuya Cong

{"title":"Supervisory control of Petri nets with uncontrollable and unobservable transitions under replacement attacks","authors":"Zhenhua Yu, Yanwei Fang, Haoming Zhu, Yifei Jia, Maria Pia Fanti, Xuya Cong","doi":"10.1016/j.jai.2026.02.002","DOIUrl":"10.1016/j.jai.2026.02.002","url":null,"abstract":"<div><div>In this paper, we study the robust control of discrete event systems modeled by Petri nets with uncontrollable and unobservable transitions under replacement attacks. The existing supervisory control methods for discrete event systems modeled as Petri nets under replacement attack can only deal with the systems with observable and controllable transitions, and cannot deal with the systems with unobservable and uncontrollable transitions. Three methods are proposed in this paper to prevent the system from reaching illegal markings by disabling some controllable transitions according to a generalized mutual exclusion constraint. These methods can deal with Petri nets containing transitions that are not observed or controlled. The proposed framework provides a versatile range of strategies, from optimal control policies for high-precision requirements to efficient structural algorithms suitable for large-scale systems. It effectively mitigates the impact of replacement attacks while ensuring rigorous system safety. By extending Petri net supervisors to compromised environments with incomplete information, this work establishes a robust theoretical foundation for securing complex industrial discrete event systems.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 3","pages":"Pages 218-228"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global quantized output feedback control of nonlinear systems under non-Lipschitz condition 非lipschitz条件下非线性系统的全局量化输出反馈控制

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2025-10-28 DOI: 10.1016/j.jai.2025.10.004

Zhirong Zhang, Long Chen, Tao Wang, Frank L. Lewis

{"title":"Global quantized output feedback control of nonlinear systems under non-Lipschitz condition","authors":"Zhirong Zhang, Long Chen, Tao Wang, Frank L. Lewis","doi":"10.1016/j.jai.2025.10.004","DOIUrl":"10.1016/j.jai.2025.10.004","url":null,"abstract":"<div><div>In this paper, a novel backstepping-based output feedback control algorithm is developed for uncertain nonlinear systems subject to both input and output quantization, while ensuring the prescribed transient performance. The development of this control approach involves several crucial steps that tackle specific challenges. Firstly, by creating invariant sets and choosing appropriate parameters, a prescribed performance control method is proposed. Subsequently, by leveraging the maximum values of relevant variables or functions within these invariant sets, the requirement for iterative differentiation of the virtual control signals in the controller design is circumvented. Furthermore, by simplifying the structure of the controller design, the effects arising from output quantization are effectively handled. Notably, the proposed method no longer requires the nonlinear function of the system model to satisfy global Lipschitz continuity, and it no longer yields semi-global results. Instead, it achieves global stability for the closed-loop system and provides clear guidance for the adjustment of its parameters. Finally, the effectiveness and advantages of the proposed quantized control approach are validated through a numerical case study.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 3","pages":"Pages 183-191"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on model-free adaptive trajectory tracking cooperative control method of dual-arm 双臂无模型自适应轨迹跟踪协同控制方法研究

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2025-12-05 DOI: 10.1016/j.jai.2025.12.001

You Wu, Guotan Liu, Mingyan Zhu, Jin Guo, Tian Wang, Rui Chen, Yanyao Wang, Haoran Tan, Yaonan Wang

{"title":"Research on model-free adaptive trajectory tracking cooperative control method of dual-arm","authors":"You Wu, Guotan Liu, Mingyan Zhu, Jin Guo, Tian Wang, Rui Chen, Yanyao Wang, Haoran Tan, Yaonan Wang","doi":"10.1016/j.jai.2025.12.001","DOIUrl":"10.1016/j.jai.2025.12.001","url":null,"abstract":"<div><div>Addressing the problem of trajectory tracking accuracy degradation caused by high-degree-of-freedom dynamic coupling, model uncertainty, and communication delay in dual-arm cooperative systems, this paper presents a model-free adaptive control method that utilizes partial form dynamic linearization (PFDL-MFAC). A hierarchical control architecture combining kinematic decoupling with distributed cooperation decomposes the complex high-degree-of-freedom coupled system into parallel joint-space optimization processes, thereby reducing computational complexity. The multi-step historical input increment is introduced to compensate for the joint drive lag effect, while the online pseudo-gradient estimation with a reset mechanism is incorporated to improve the tracking robustness under time-varying delays. The proposed global unified parameter strategy verifies the algorithm’s adaptability to degree-of-freedom expansion and heterogeneous joints. The simulation results confirm its stable tracking performance under ideal conditions. Comparative experiments under random delay conditions demonstrate that the proposed control scheme enhances tracking precision over traditional PID control methods, reducing the steady-state error and peak displacement. Experimental evidence shows that the algorithm has strong robustness against time-delay disturbances. This method offers a novel solution for high-precision cooperative control of networked robotic systems operating under both time-delay constraints and dynamic uncertainties.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 3","pages":"Pages 192-201"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Region-weighted gradient and motion-aware camera exposure control for robust visual odometry 鲁棒视觉里程计的区域加权梯度和运动感知相机曝光控制

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2025-10-17 DOI: 10.1016/j.jai.2025.10.001

Qirui Sun, Qi Ouyang, Xiaolong Li, Zhaoying Cheng

{"title":"Region-weighted gradient and motion-aware camera exposure control for robust visual odometry","authors":"Qirui Sun, Qi Ouyang, Xiaolong Li, Zhaoying Cheng","doi":"10.1016/j.jai.2025.10.001","DOIUrl":"10.1016/j.jai.2025.10.001","url":null,"abstract":"<div><div>Visual odometry (VO) often suffers from performance degradation in high dynamic range (HDR) scenarios due to uneven illumination conditions, adversely affecting localization accuracy. To address this challenge, we propose a camera exposure control algorithm designed to enhance the robustness of visual odometry by adaptively adjusting the camera’s shutter speed and gain parameters to obtain high-quality images. Specifically, the algorithm employs a gradient-weighted sum of local image regions as a quality evaluation metric, and iteratively updates exposure parameters by computing the first-order derivative of this metric with respect to the exposure settings. Moreover, scene irradiance information is incorporated to determine an appropriate update step size. To further mitigate motion blur, we constrain the camera’s maximum shutter time based on feedback from the visual odometry system. the efficacy of the proposed approach is validated through experiments conducted in both HDR and fast-motion scenarios. Additionally, we integrate the exposure control strategy into two representative VO frameworks, ORB SLAM3 and VINS MONO, and evaluate their performance in both indoor and outdoor environments. The experimental results demonstrate that our exposure control method significantly improves the overall accuracy and robustness of visual odometry systems.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 3","pages":"Pages 236-246"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dynamic weighted deep-broad learning architecture for wind power interval prediction 风电区间预测的动态加权深度学习体系结构

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2026-02-03 DOI: 10.1016/j.jai.2026.01.003

Xuguo Jiao, Daoyuan Zhang, Zhenyong Zhang, Weiwei Cao, Hongpeng Yin, Simon X. Yang

{"title":"A dynamic weighted deep-broad learning architecture for wind power interval prediction","authors":"Xuguo Jiao, Daoyuan Zhang, Zhenyong Zhang, Weiwei Cao, Hongpeng Yin, Simon X. Yang","doi":"10.1016/j.jai.2026.01.003","DOIUrl":"10.1016/j.jai.2026.01.003","url":null,"abstract":"<div><div>Predicting wind power is essential for the stable and economical operation of power grids. Traditional point forecasts often struggle to capture volatility, while wind power interval prediction (WPIP) effectively quantifies associated uncertainties, providing comprehensive information for power dispatch. This paper proposes a novel deep-broad WPIP ensemble framework. First, considering the relationships among variables collected by the wind turbine’s supervisory control and data acquisition system, an operational mechanism-based method is designed to identify the outliers, while a novel multiple imputation approach is proposed to fill in the missing values and rectify the detected outliers to guarantee good data quality. Then, based on fuzzy and rough set principles, rolling fuzzy information granulation is used to capture the wind power fluctuation and construct high-quality training labels. Subsequently, the graph attention network with dynamic attention and gated recurrent unit, and the stacked broad learning system are developed separately as wind power interval predictors. These two models can extract and fuse spatial–temporal features selected from the training data thoroughly and efficiently to enhance the WPIP accuracy. Moreover, the human evolutionary optimization algorithm is used to dynamically optimize the combined weights online and integrate the prediction results of these two predictors to improve the interval prediction performance effectively. The comparative and ablation experiments demonstrate that our model outperforms the benchmark methods.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 3","pages":"Pages 202-217"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generation of periodic steady-state dynamics of a hyperbolic PDE system via boundary actuation 边界驱动双曲PDE系统周期稳态动力学的生成

Journal of Automation and Intelligence Pub Date : 2026-09-01 Epub Date: 2025-11-05 DOI: 10.1016/j.jai.2025.11.002

Yury Orlov, Luis T. Aguilar

引用次数: 0

From black box to physically interpretable: Trustworthy computing for AI-driven decision-making and control 从黑箱到物理可解释：人工智能驱动的决策和控制的可靠计算

Journal of Automation and Intelligence Pub Date : 2026-06-01 Epub Date: 2025-09-24 DOI: 10.1016/j.jai.2025.09.003

Yongduan Song , Anguo Zhang

{"title":"From black box to physically interpretable: Trustworthy computing for AI-driven decision-making and control","authors":"Yongduan Song , Anguo Zhang","doi":"10.1016/j.jai.2025.09.003","DOIUrl":"10.1016/j.jai.2025.09.003","url":null,"abstract":"<div><div>As Artificial Intelligence (AI) becomes deeply integrated into the physical world, ensuring verifiable, robust, and safe decision-making and control is paramount. Purely data-driven methods, while successful in perception tasks, often suffer from a “black box” nature when deployed in safety-critical environments, leading to insufficient generalization and potential hazards. Therefore, the paradigm of trustworthy decision-making and control guided by physical world information has emerged. It integrates physical laws, system dynamics, and environmental constraints into AI models, either as regularization terms in the loss function, embedded within the network architecture, or through hybrid models. It allows AI to simultaneously learn from data and consistently follow physical laws. This paper compares trustworthy computing guided by physical knowledge with traditional data-driven methods, focusing on two representative paradigms: Physics-Informed Neural Networks (PINNs) and Physics-Informed Reinforcement Learning (PIRL). We present their core concepts and integration mechanisms, embedding physical laws via loss regularization or network architecture, and incorporating physics into the learning or control process. This paper reveals the significant improvements of the trustworthy computing in interpretability, robustness, and safety, providing a new path from “black box” to “physically interpretability” transformation for safety-critical domains such as autonomous driving, robot collaboration, and industrial process control. Understanding the differences and connections within this paradigm is essential for advancing AI technologies that can serve human society safely and reliably.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 91-111"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0