IEEE Transactions on Automation Science and Engineering最新文献

{"title":"STGN: A Spatio-Temporal Graph Network for Real-Time and Generalizable Trajectory Planning","authors":"Runjiao Bao;Yongkang Xu;Chenhao Wang;Tianwei Niu;Junzheng Wang;Shoukun Wang","doi":"10.1109/TASE.2025.3614472","DOIUrl":"10.1109/TASE.2025.3614472","url":null,"abstract":"In dynamic and unstructured environments, mobile robots need to generate safe and efficient trajectories in real time, which poses significant challenges due to the uncertainty of surrounding obstacles. To address this, this article presents a real-time obstacle avoidance trajectory planning method, built upon a spatio-temporal graph network that integrates temporal modeling with graph attention mechanisms. The proposed network captures both temporal dynamics and spatial structural dependencies in dynamic environments by integrating a temporal information module based on long short-term memory (LSTM) and a spatial module based on relational graph attention networks (RGAT). On the whole, the approach follows a two-phase pipeline. In the offline phase, a high-quality trajectory dataset is constructed to represent the heterogeneous state graph of the robot and surrounding obstacles. Then the dataset is used to train the spatio-temporal network, which learns to map environment-state graphs to optimal control commands. In the online phase, the trained network is deployed on the robot to perform real-time perception, decision-making, and control, forming a closed-loop trajectory optimization process. Extensive experiments in both simulated and real-world scenarios demonstrate that the proposed method achieves high-quality trajectory planning, robust obstacle avoidance, and fast generalization under multi-obstacle and sudden disturbance conditions, while maintaining low computational overhead. Note to Practitioners—This article addresses the generalization challenges commonly encountered in traditional supervised learning-based obstacle avoidance methods. We propose a trajectory planning framework that leverages a spatiotemporal graph network to model dynamic interactions between a robot and its surrounding obstacles. This approach enables robust and adaptable behavior in complex, changing environments by explicitly capturing both spatial and temporal dependencies. The system is implemented on a four-wheel steering (4WS) robot for experimental validation. However, its modular design ensures straightforward transferability to a wide range of mobility platforms. The proposed method requires only basic obstacle position information, readily available from standard onboard sensors such as LiDAR or radar, and does not depend on raw sensor inputs or semantic maps, making it highly suitable for real-world deployment. It can be easily integrated into existing perception–planning–control pipelines, and future extensions may focus on incorporating richer semantic information and expanding to more diverse obstacle types.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21897-21912"},"PeriodicalIF":6.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decentralized Control of Crop Growth Conditions in Vertical Farms Under Dynamic Energy Markets","authors":"Kirill Zhukovskii;Paolo Scarabaggio;Polina Ovsiannikova;Pranay Jhunjhunwala;Raffaele Carli;Mariagrazia Dotoli;Valeriy Vyatkin","doi":"10.1109/TASE.2025.3609694","DOIUrl":"10.1109/TASE.2025.3609694","url":null,"abstract":"The growing global population and the increasing scarcity of arable land highlight the urgent need for reliable and efficient food production systems. With their controlled environments, vertical farms (VFs) offer a promising solution for sustainable food security. Nevertheless, their high energy demands call for innovative approaches to optimize energy consumption while maintaining optimal growing conditions. This paper introduces a novel control-oriented model for VFs, capturing the interactions between crop growth conditions and energy consumption. To address the high energy demand of VFs, the model is integrated into a dynamic energy market characterized by time-varying energy prices and a demand response scheme, which includes a discrete reward to encourage flexible energy consumption. Then, centralized and decentralized receding horizon control approaches are proposed to minimize the energy cost of the VF while ensuring optimal crop growth. Experimental evaluations on real systems of varying scales demonstrate the effectiveness of the proposed approaches in reducing costs and ensuring sustainable agricultural practices. Note to Practitioners–This work addresses a growing challenge in operating vertical farms: reducing energy costs while maintaining optimal conditions for crop growth. We introduce a control system that helps vertical farms schedule energy-intensive activities to take advantage of dynamic electricity prices or incentives from grid operators. In particular, we focus on a binary reward structure reflecting real-world demand response programs, where financial incentives are granted only if strict consumption targets are fully met. The approach relies on forecasting and optimization techniques already compatible with standard industrial automation systems. Two control systems are proposed: a centralized controller that manages the entire facility from a single decision point and a decentralized version that allows each unit (e.g., a room or a growing tray) to make decisions independently. The decentralized version offers better scalability and can more easily adapt to farm layout or crop type changes. This framework could also be applied to greenhouses, food storage systems, or other indoor environments with high energy demand.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21498-21511"},"PeriodicalIF":6.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Robust Adaptive Error Estimation for Heterogeneous Cyber-physical Systems Over Time-Varying and Intermittent Pinning Communication","authors":"Bohui Wang","doi":"10.1109/tase.2025.3614435","DOIUrl":"https://doi.org/10.1109/tase.2025.3614435","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"89 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Modal Commonality Graph Matching Frame: A Fault Diagnosis Method for Multimode Process","authors":"Shuai Tan;Zhiyun Chen;Qingchao Jiang;Weimin Zhong","doi":"10.1109/TASE.2025.3613749","DOIUrl":"10.1109/TASE.2025.3613749","url":null,"abstract":"Fault diagnosis in complex industrial systems, such as large-scale chemical plants and advanced manufacturing lines, is critically challenged by data acquired in multiple and varying operating modes. These modal shifts, driven by different production demands and process parameters, often obscure fault signatures and undermine the reliability of conventional diagnostic models. Despite these operational variations, it is observed that variables within such multimode processes often exhibit consistent correlations. Consequently, the same fault can manifest itself as a shared propagation characteristic across different modes. To leverage these invariant features for robust fault identification, this paper proposes a novel Cross-Modal Commonality Graph Matching (CMCGM) framework. Our approach first extracts the shared causal structures of a specific fault from data across multiple modalities to construct a modality-independent “commonality fault dictionary,” which captures the essential fault signature. By transforming the diagnosis task into a local subgraph matching problem against this dictionary, the CMCGM method achieves robust and accurate fault identification while circumventing the computational costs of online feature propagation. The effectiveness and superior performance of the proposed framework are validated through extensive experiments on the Tennessee-Eastman (TE) process, a widely-recognized benchmark for complex chemical systems. Note to Practitioners—This work addresses the challenge of diagnosing faults in industrial processes where data comes from multiple working modes. In such systems, faults often propagate in similar ways across different modalities, but traditional methods struggle to leverage these shared patterns effectively. Our solution, the Cross-Modal Commonality Graph Matching (CMCGM) framework, identifies and matches these common fault signatures across modalities, enabling faster and more reliable fault diagnosis without requiring extensive computational resources. The method works by analyzing how faults propagate across different data sources, extracting their shared structural features, and building a reusable fault reference library. Instead of treating each data stream separately, it compares faults in a way that reduces redundant computations. This makes the diagnosis process more efficient while maintaining reliability. One current limitation is that it requires sufficient historical fault records to build an effective reference model. Future improvements could focus on making the system more responsive to newly emerging faults and integrating it with real-time monitoring systems. Potential applications include predictive maintenance in factories, power plants, and other complex industrial systems where early and accurate fault detection is critical.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21760-21769"},"PeriodicalIF":6.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-Preserving Distributed Fault Diagnosis for Multiple Wind Farms Using a Federated Feature Fusion Method","authors":"Zhijun Wang;Yanting Li;Zijun Zhang;Ershun Pan","doi":"10.1109/TASE.2025.3613954","DOIUrl":"https://doi.org/10.1109/TASE.2025.3613954","url":null,"abstract":"While data-driven methods have gained prominence in wind turbine fault diagnosis, their effectiveness is increasingly constrained by the proliferation of data silos. This phenomenon primarily arises from the reluctance toward data sharing, a trend exacerbated by intensifying commercial competition and mounting privacy concerns. Such limitations fundamentally challenge centralized data processing paradigms in this field. Federated learning provides a viable technical solution to mitigate data silos while preserving data sovereignty. Nevertheless, two critical challenges that substantially hinder practical implementation remain unresolved: 1) pronounced data heterogeneity among geographically distributed wind farms, and 2) prohibitive communication overhead stemming from sluggish convergence rates in distributed optimization. To bridge these gaps, we propose the Federated Feature Fusion (Fed-FF), a novel cross-farm fault diagnosis framework that synergistically integrates feature-level federation with convergence acceleration. Furthermore, the theoretical guarantees of the proposed method are studied in this paper, where the convergence risk bounds for both convex and non-convex settings are derived. The proposed method is then validated against three real-world datasets collected in Yunnan Province, Jiangsu Province and Shanghai, China. The experimental results show that the proposed method achieves an average accuracy of 93.83%, outperforming several state-of-the-art fault diagnosis methods, while communication costs are reduced by up to 94.71%. Note to Practitioners—As competition intensifies and data privacy regulations tighten, wind farm operators are increasingly withholding operational data, creating persistent data silos. Compounding this challenge, the inherent heterogeneity of SCADA measurements across geographically dispersed farms exacerbates communication overhead in conventional federated learning. To overcome these limitations, we propose a novel approach that ensures data privacy while significantly accelerating convergence and reducing communication costs, thereby lowering operational expenses for wind farms. Supported by rigorous theoretical guarantees, our method enhances both reliability and practical applicability. As a distributed fault diagnosis framework, it facilitates efficient collaboration and statistical information sharing among multiple wind farms without exchanging raw data, enabling precise fault localization. Beyond wind farms, this method is also applicable to diverse domains, including healthcare, intelligent connected vehicles, photovoltaic power plants, and smart manufacturing, offering a versatile and privacy-preserving solution for distributed learning.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21525-21540"},"PeriodicalIF":6.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional Feature Enhancement Domain Adaptation for Automatic Road Damage Detection in Hazy and Rainy Weather","authors":"Jie Li, Zhong Qu, Xuehui Yin","doi":"10.1109/tase.2025.3613972","DOIUrl":"https://doi.org/10.1109/tase.2025.3613972","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"83 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Disturbance Rejection Scheme for ASR Heading Control Based on an Improved Extended State Observer and Experiment Research","authors":"Tianyu Wei, Yulei Liao, Lei Wan, Chenguang Yang, Tuosheng Zhang, Ming Zhang","doi":"10.1109/tase.2025.3614016","DOIUrl":"https://doi.org/10.1109/tase.2025.3614016","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"35 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AccSPS Learning Rate: Accelerated Convergence through Decision-Adjusted Levels for Stochastic Polyak Stepsize","authors":"Jingtao Qin, Anbang Liu, Mikhail Bragin, Nanpeng Yu","doi":"10.1109/tase.2025.3613719","DOIUrl":"https://doi.org/10.1109/tase.2025.3613719","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Control with Steering Mode Switching for MDED-HDV via Maneuver Stability Region Analysis","authors":"Ruiqi Fang, Tong Shen, Xin Bai, Jinhao Liang, Fanxun Wang, Weichao Zhuang, Guodong Yin","doi":"10.1109/tase.2025.3613526","DOIUrl":"https://doi.org/10.1109/tase.2025.3613526","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"13 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process Optimization of Multi-Stage Continuous Production System Based on Feature Fusion Modeling","authors":"Xiaojie Li;Runlong Yu;Lei Chen;Shengjun Liu;Enhong Chen","doi":"10.1109/TASE.2025.3612822","DOIUrl":"https://doi.org/10.1109/TASE.2025.3612822","url":null,"abstract":"Modeling and process optimization of Multi-stage Continuous Production System (MCPS) is an important research topic in the field of intelligent manufacturing today. However, due to the inherent properties of MCPS, existing researches face difficulties in 1) coupling optimization across multiple stages under diverse constraints and objectives, and 2) accurately mapping controllable process variables to critical production outputs. In this work, we propose a novel multi-objective optimization method for MCPS based on feature fusion modeling, with main innovations including: 1) For MCPS modeling, we propose a Transformer network structure with a parallel dual-branch attention mechanism for linking process variables to production outputs. A multi-stage feature fusion prediction model based on DenseNet is developed, which not only improves prediction accuracy but also provides intermediate stage prediction results. 2) For process optimization of MCPS, we design a multi-constraint and multi-objective optimization model. Subsequently, we propose a dynamic multi-objective optimization algorithm framework to enhance the performance of the algorithm and improve the quality of solutions. Furthermore, we conducted experiments with a real Coke-Chem integrated production dataset, and the results show that our predictive model achieves an average MAE of 0.11, MSE of 0.04, and RMSE of 0.20, outperforming state-of-the-art methods and boosts solution coverage (up to 80%) and hypervolume (up to <inline-formula> <tex-math>$2.5times $ </tex-math></inline-formula>) when integrated with classical algorithms like NSGA-II and GDE3. Note to Practitioners—The PO-MCPS commonly found in modern industrial production is a complex issue, especially when some intermediate stages also produce end products. For example, optimizing a single stage may deteriorate other stages, causing a global loss; simply increasing production volume may also increase raw material costs and reduce overall profitability. The strategy proposed in this work can help practitioners build predictive models similar to Coke-Chem integrated production, thereby enabling the prediction of outputs at various stages of the production and supporting the overall optimization of process variables. This strategy has a high value for engineering applications.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21512-21524"},"PeriodicalIF":6.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}