Control Engineering Practice最新文献

{"title":"An overview of Unmanned Surface Vehicles: Methods, practices, and applications","authors":"Huarong Zheng ,&nbsp;Chenguang Liu","doi":"10.1016/j.conengprac.2025.106479","DOIUrl":"10.1016/j.conengprac.2025.106479","url":null,"abstract":"<div><div>Over the past few decades, Unmanned Surface Vehicles (USVs) have transitioned from concept to reality at an impressive pace. This rapid development is fueled by advances in technology and emerging demands in science, engineering, and military applications. The emphasis on USVs has evolved from purely research-focused activities to practical commercial and engineering applications. This paper presents insights gained from a decade of USV development, providing an overview of the latest methodologies, design practices, and applications. We review and compare various USVs currently in use, including those for research and field applications. In addition, we categorize and update the state-of-the-art navigation, guidance, and control methodologies that form the core capabilities of USVs, with a particular emphasis on the growing trend of learning-based and data-driven systems. As a crucial aspect, we also discuss the architectural design, implementation, and field testing of USVs. Finally, we explore their applications in three key areas: military operations, oceanographic observation, and intelligent waterborne transportation. Given that technological and practical challenges for USVs remain, we outline considerations and highlight relevant future directions.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106479"},"PeriodicalIF":5.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633974","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":"Real-time formation drillability sensing-based hybrid online prediction method for the rate of penetration (ROP) and its industrial application for drilling processes","authors":"Chao Gan ,&nbsp;Yao Wang ,&nbsp;Wei-Hua Cao ,&nbsp;Kang-Zhi Liu ,&nbsp;Min Wu","doi":"10.1016/j.conengprac.2025.106487","DOIUrl":"10.1016/j.conengprac.2025.106487","url":null,"abstract":"<div><div>Real-time prediction of the rate of penetration (ROP) is crucial for enhancing drilling efficiency. Formation drillability (FD), a key factor that reflects the formation characteristics is rarely considered in common studies. In this paper, a real-time FD sensing-based hybrid online prediction method of ROP and its industrial application for the drilling process is proposed, which contains two main stages (FD soft sensing and online ROP prediction). In the first stage, the FD of the bottom hole is predicted in real-time based on the soft sensing techniques, which is set as one of the input parameters of the ROP prediction model. In the second stage, five parameters that have close relationships with the ROP are pre-processed online using limiting filtering and Savitzky Golay (SG) filtering first. After that, the hybrid modeling method and incremental learning strategy are introduced to establish the ROP prediction model. Finally, the proposed method is applied in the Gedian area, Central China. Compared with six well-known ROP prediction methods, the prediction accuracy is improved by at least 19%, which validates the effectiveness of the proposed method and lays a foundation for intelligent optimization control of the drilling process.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106487"},"PeriodicalIF":5.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632016","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 NMPC for energy-efficient torque vectoring in four in-wheel motor electric vehicles","authors":"Suyong Park ,&nbsp;Junghyo Kim ,&nbsp;Duc Giap Nguyen ,&nbsp;Minsoo Woo ,&nbsp;Daekwang Kim ,&nbsp;Kyoungseok Han","doi":"10.1016/j.conengprac.2025.106477","DOIUrl":"10.1016/j.conengprac.2025.106477","url":null,"abstract":"<div><div>This paper presents a hierarchical torque vectoring (TV) framework that integrates vehicle dynamics enhancement and energy efficiency improvement using nonlinear model predictive control (NMPC) for four in-wheel motor electric vehicles (4WMEVs). TV systems offer significant potential for enhancing both handling performance and energy economy by optimally distributing torque among the wheels. However, effectively balancing these two objectives remains a critical challenge. To address this, we propose a simplified hierarchical control structure that simultaneously improves handling performance and reduces energy consumption. The top-layer controller computes the desired longitudinal tire forces to generate accurate yaw moments, enhancing handling performance. The bottom-layer controller ensures energy-optimal torque allocation while preserving the control objectives of the top-layer. Simulation results demonstrate that the proposed strategy achieves a 11.27% reduction in energy consumption without compromising handling performance.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106477"},"PeriodicalIF":5.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623509","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 compound Bayesian networks gas prediction and scheduling method for blast furnace systems under various scenarios","authors":"Junyan Fan,&nbsp;Dinghui Wu,&nbsp;Shenxin Lu","doi":"10.1016/j.conengprac.2025.106463","DOIUrl":"10.1016/j.conengprac.2025.106463","url":null,"abstract":"<div><div>Changes in the production scenarios of the blast furnace system disrupt the established production rhythm and gas balance. The complex characteristics of equipment in different scenarios and the temporal coupling resulting from the interactions between scenarios present challenges in prediction and scheduling. To address this issue, a compound Bayesian networks (CBN) gas prediction and scheduling method is proposed. In light of the characteristics of gas production and consumption data from different equipment, a monotonic variable-step size sliding window event extraction algorithm is designed to extract event information and construct state and delay event sets. By integrating the physical production structure with these event sets, state Bayesian networks, delay Bayesian networks, and an inter-network state representation layer are constructed, forming a complete CBN. Meanwhile, a prediction and scheduling inference procedure with temporal decoupling is proposed. It decouples the energy states of production equipment across successive scenarios and incorporates the objective function to enable scheduling inference and decision-making. Actual data from the steel production process are used for experimental analysis. The results demonstrate that the proposed method enhances prediction accuracy by 4.93% and reduces delay by 5.97%. In scheduling, it decreases the proportion of instances exceeding the safety tank level by 16.67% and lowers the comprehensive energy consumption per ton of steel by 0.0054%.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106463"},"PeriodicalIF":5.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623510","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":"Globally smooth lemniscate trajectory with adaptive integral terminal sliding mode control and inversion-based hysteresis compensation for pi ezoelectric stage precise tracking","authors":"Jim-Wei Wu (Senior Member IEEE) ,&nbsp;Ting-Kuei Hsu ,&nbsp;Jia-Cheng Li ,&nbsp;Yu-Han Lin ,&nbsp;Sung-Hua Chen","doi":"10.1016/j.conengprac.2025.106486","DOIUrl":"10.1016/j.conengprac.2025.106486","url":null,"abstract":"<div><div>High-precision measurement systems use an <em>xy</em>-axis piezoelectric scanner following a pre-determined trajectory to achieve three-dimensional scanning at micro/nano-scales. Most systems employ a raster scanning trajectory due to ease of implementation; however, raster scanning generates infinite odd harmonics, which can induce mechanical resonance leading to image distortion. Moreover, the nonlinear response characteristics of piezoelectric materials frequently result in unexpected displacements. This paper presents a novel sequential scanning trajectory that eliminates the need for a step function and minimizes the risk of mapping errors. An advanced controller combining an inverse hysteresis model with adaptive integral terminal sliding mode control (AITSMC) was developed and first applied in the piezoelectric scanner through theoretical derivation and stability analysis to prove its feasibility. In simulations and experiments, the proposed controller significantly mitigated the effects of hysteresis during trajectory tracking and achieved superior tracking accuracy and lower RMSE compared to existing sliding mode controllers.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106486"},"PeriodicalIF":5.4,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606156","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":"Intuitive hand motion-based teleoperation system for human-mobile manipulator interaction using mixed reality","authors":"Hua-Hsuan Yeh,&nbsp;Yu-Wei Chang,&nbsp;Yen-Chen Liu","doi":"10.1016/j.conengprac.2025.106467","DOIUrl":"10.1016/j.conengprac.2025.106467","url":null,"abstract":"<div><div>This paper presents an intuitive teleoperation interface for human-mobile manipulator interaction, addressing the challenges posed by unstructured, dynamic environments and the inherent uncertainties in robot dynamics. Traditional teleoperation systems often lack intuitiveness and immersiveness, particularly when controlling high-DoF (degree-of-freedom) robotic systems. The proposed approach integrates IMU (Inertial Measurement Unit)-based hand motion tracking with mixed reality technology, enabling seamless control in both Manipulation and Locomotion modes. In Manipulation mode, a torque-based controller regulates the motion of the manipulator’s end-effector, while a radial basis function neural network compensates for dynamic uncertainties. The null-space projection method further provides self-autonomous redundancy resolution. In Locomotion mode, a velocity-based control strategy is implemented to govern the motion of the mobile platform. The mixed reality environment, constructed using SLAM (Simultaneous Localization and Mapping), 3D point-cloud data, and real-time 2D visual streaming, provides immersive and comprehensive visual feedback through a head-mounted display device. Stability of the Manipulation mode is analyzed via Lyapunov stability theorem and validated through numerical studies. Experiments conducted on a mobile manipulator, comprising a differential-drive base and a 7-DoF manipulator, demonstrate effective performance in complex scenarios. Additionally, comparative experiments with multiple input devices show that the proposed system offers superior usability and imposes a lower operator workload than alternative interfaces. These results highlight the robustness, intuitiveness, and practical applicability of the proposed method in dynamic and hazardous environments.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106467"},"PeriodicalIF":5.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596252","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":"Fault detection for electric magnesium melting furnace based on cooperative modeling","authors":"Yunpeng Fan ,&nbsp;Yingwei Zhang ,&nbsp;Chaomin Luo ,&nbsp;Zhuming Bi","doi":"10.1016/j.conengprac.2025.106490","DOIUrl":"10.1016/j.conengprac.2025.106490","url":null,"abstract":"<div><div>A high level of process monitoring is necessary for the safety and product quality of an electric magnesium melting furnace. However, plant upgrading, such as the addition of cameras, has brought challenges to the traditional monitoring methods although more information about furnace conditions can be obtained. This is because graphic data and sensor data are completely different in structure and calculation. Therefore, a compatible collaborative modeling and fault detection method is proposed in this paper. Firstly, the system and residual subspace of data are obtained by minimizing the statistical distance of data from different information sources through collaborative modeling. Then the weighted information fusion of subspace data is carried out to develop and integrate a new hierarchical fault detection and alarm scheme. Finally, the feasibility and effectiveness of the proposed method are verified by the real data of magnesia smelting process.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106490"},"PeriodicalIF":5.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596253","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":"Robust observer-based adaptive backstepping levitation control for maglev trains with stochastic time delay and disturbance","authors":"Zikang Li,&nbsp;Cuicui Huang,&nbsp;Xiaolong Li,&nbsp;Zhiqiang Long","doi":"10.1016/j.conengprac.2025.106465","DOIUrl":"10.1016/j.conengprac.2025.106465","url":null,"abstract":"<div><div>Electromagnetic levitation system (EMLS) plays a crucial role in ensuring the safety and riding comfort of maglev trains. As operating speeds increase and systems undergo long-term service, EMLS is required to exhibit robust performance subject to external disturbances and input delay, and a composite levitation control strategy for the vehicle-magnetic coupled system is proposed in this paper. The mathematical model of EMLS is analyzed at first. To eliminate the impact of time delay on dynamic response, the Padé approximation is utilized to convert the original time-delay system into an equivalent delay-free formulation. An adaptive disturbance observer is then designed to estimate stochastic disturbances and provide feedforward compensation. In this way, the robust adaptive controller via backstepping technique is formulated to regulate the levitation gap between the carriage and the track. The asymptotic stability of the closed-loop system is rigorously proved by constructing appropriate Lyapunov functions. A parameter tuning guideline based on the Multiple Population Genetic Algorithm is integrated to enhance control accuracy and minimize steady-state tracking errors. Numerical simulations and experiments are conducted to validate the effectiveness and feasibility of the proposed control approach.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106465"},"PeriodicalIF":5.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596254","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":"Improved dynamic modeling for controlled server queues","authors":"Tommi Berner ,&nbsp;Max Nyberg Carlsson ,&nbsp;Johan Ruuskanen ,&nbsp;Martina Maggio ,&nbsp;Karl-Erik Årzén","doi":"10.1016/j.conengprac.2025.106473","DOIUrl":"10.1016/j.conengprac.2025.106473","url":null,"abstract":"<div><div>Resource provisioning for applications hosted in the cloud is a difficult task due to inherent performance variability in the infrastructure. Control theory has proven to be an efficient tool to increase the predictability of cloud applications. However, a prerequisite for a successful control design is an adequate model of the involved dynamics. In this paper we focus on modeling of <em>controlled</em> server queues that are subject to actuators, such as frequency scaling or admission control. We show that today’s models are only applicable to specific server types, characterized by <em>queuing disciplines</em>, and propose a model structure that can be applied for more general settings. Our structure is nonlinear, yet simple enough to allow for control design. We compare our approach to state-of-the-art models in an extensive simulation campaign, showing the superior versatility of our model. We also evaluate the model using measured data from a cloud-based face detection algorithm run in Kubernetes. Furthermore, we use our model in control design examples to show the insights that can be gained. We identify a critical frequency range where the characteristics of the involved service time distribution affect the control design, and where a more advanced controller structure might be needed. Finally, we present a feedback linearization control design based on our model that is evaluated using both simulations and a cloud-based application.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106473"},"PeriodicalIF":5.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588589","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":"Transient heat conduction modelling for real-time operation and control of steel-making reheating furnaces - a physics-informed knowledge distillation-assisted EngGeneNet approach","authors":"Xiaofei Han ,&nbsp;Kang Li ,&nbsp;Chuan Wang ,&nbsp;Jiayang Zhang ,&nbsp;Yukun Hu","doi":"10.1016/j.conengprac.2025.106472","DOIUrl":"10.1016/j.conengprac.2025.106472","url":null,"abstract":"<div><div>Accurate and fast transient temperature distribution prediction is a long-standing technically challenging open problem in real-time operation and control of many energy-intensive industrial processes involving massive heat conduction. The powerful fitting capabilities of deep learning models to perform parallel computations make them ideal surrogate models to meet the requirements for real-time applications. However, collecting and processing a large amount of labelled data is tedious and challenging if not possible. Furthermore, most neural models are black-box models, hence suffer from a few well-known problems such as poor generalization performance and slow convergence speed. This paper proposes a physics-informed deep learning modelling framework, namely EngGeneNet to capture the salient features and functional relationships of system variables to predict the transient temperature distribution of large-scale intermediate steel products in reheating furnaces. The network can learn a mapping between the current and the future transient 2D temperature field at a given ambient temperature, equivalent to solving the partial differential equations in real-time. The heat conduction governing equations and the boundary conditions are formulated as the loss function to guide the accurate and efficient training of the proposed EngGeneNet model. Then, an ‘Eng-Gene’ module is embedded into the deep learning model to accelerate the training convergence and enhance generalization performance. The ‘Eng-Gene’ is the salient physical relationship among variables that are extracted from the first-principle knowledge of the target system. Furthermore, the knowledge distillation approach is adopted, where a computationally expensive but more accurate numerical method namely alternating direction implicit (ADI) is applied to generate sufficient training data for training the deep learning models. To improve the adaptability of the EngGeneNet model to varying product batches, transfer learning is adopted to mitigate the dataset feature space variations under different operating conditions. The proposed method has been validated on a pilot-scale walking-beam furnace with a range of steel bloom batches under different operating conditions. The results suggest that the EngGeneNet framework can effectively improve the generalization and convergence performance of the deep learning model. In terms of processing time for predicting each frame of the heat distribution map, the proposed model achieves an improvement of approximately 96% in computational efficiency with comparable accuracy of the conventional method used in real-life applications, paving the way for real-time applications in many energy-intensive engineering processes.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106472"},"PeriodicalIF":5.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579283","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}