Control Engineering Practice最新文献

{"title":"Real-time kinematics-based sensor-fault detection for autonomous vehicles using single and double transport with adaptive numerical differentiation","authors":"Shashank Verma,&nbsp;Dennis S. Bernstein","doi":"10.1016/j.conengprac.2025.106304","DOIUrl":"10.1016/j.conengprac.2025.106304","url":null,"abstract":"<div><div>Sensor-fault detection is crucial for the safe operation of autonomous vehicles. This paper introduces a novel kinematics-based approach for detecting and identifying faulty sensors, which is model-independent, rule-free, and applicable to ground and aerial vehicles. This method, called kinematics-based sensor fault detection (KSFD), relies on kinematic relations, sensor measurements, and real-time single and double numerical differentiation. Using onboard data from radar, rate gyros, magnetometers, and accelerometers, KSFD identifies a single faulty sensor in real time. To achieve this, adaptive input and state estimation (AISE) is used for real-time single and double numerical differentiation of the sensor data, and the kinematically exact single and double transport theorems are used to evaluate the consistency of the data. Unlike model-based and knowledge-based methods, KSFD relies solely on sensor signals, kinematic relations, and AISE for real-time numerical differentiation. For ground vehicles, KSFD requires six kinematics-based error metrics, whereas, for aerial vehicles, nine error metrics are needed. Simulated and experimental examples are provided to evaluate the effectiveness of KSFD.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106304"},"PeriodicalIF":5.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579524","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":"FPGA implementation of edge-side motor fault diagnosis using a Kalman filter-based empirical mode decomposition algorithm","authors":"Jiaxin Li,&nbsp;Maosong Cheng,&nbsp;Yongbo Wei,&nbsp;Zhimin Dai","doi":"10.1016/j.conengprac.2025.106312","DOIUrl":"10.1016/j.conengprac.2025.106312","url":null,"abstract":"<div><div>Despite the successful use of deep learning in motor fault diagnosis, its real-time applications have been greatly hindered due to the enormous computational burden and extensive processing time. Addressing this, a Kalman filter-based empirical mode decomposition (KF-EMD) algorithm is proposed, replacing the cubic spline interpolation of traditional EMD with a Kalman filter for real-time FPGA (Field Programmable Gate Array) processing. This algorithm enhances the detection of data anomalies and reduces the computational burden on a lightweight multilayer perceptron (MLP) model, which recognizes features extracted by KF-EMD at fixed intervals. The proposed real-time fault diagnosis method achieved 98.96% accuracy on the Case Western Reserve University (CWRU) dataset and 98.05% on our motor diagnosis experimental platform.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106312"},"PeriodicalIF":5.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563162","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 parallel weighted ADTC-Transformer framework with FUnet fusion and KAN for improved lithium-ion battery SOH prediction","authors":"Chuang Chen ,&nbsp;Yuheng Wu ,&nbsp;Jiantao Shi ,&nbsp;Dongdong Yue ,&nbsp;Ge Shi ,&nbsp;Dongzhen Lyu","doi":"10.1016/j.conengprac.2025.106302","DOIUrl":"10.1016/j.conengprac.2025.106302","url":null,"abstract":"<div><div>This paper delves into the extraction and integration of local and global features for lithium-ion battery State of Health (SOH) prediction, proposing an innovative parallel weighted architecture—ADTC-Transformer. This framework combines Adaptive Dilated Temporal Convolution (ADTC) with a Transformer encoder to effectively capture and balance local and global dependencies while dynamically optimizing feature contributions through a weighted fusion mechanism. Additionally, the traditional U-shaped network (Unet) is enhanced by incorporating a Feature Pyramid Network (FPN), forming the FUnet module, which significantly strengthens the fusion and utilization of multi-scale features. Building on this, the Kolmogorov–Arnold Network (KAN) is introduced as the final prediction module, leveraging Kolmogorov–Arnold representation theory to model complex high-dimensional features through local interpolation and global nonlinear transformations. This enables the KAN module to capture intricate temporal dependencies and interactions across a wide range of feature scales, thus improving the model’s ability to predict long-term SOH. Experimental results demonstrate that the proposed method markedly improves prediction accuracy across NASA, CALCE, and WRBD datasets, excelling particularly in long-term SOH prediction for lithium-ion batteries. This provides robust support for battery health management and performance optimization.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106302"},"PeriodicalIF":5.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550994","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":"U-control-based active disturbance-rejection control revealed by the sliding mode for USV heading control","authors":"Jiabin Yu ,&nbsp;Yuxiang Wei ,&nbsp;Yang Chen ,&nbsp;Huiyan Zhang ,&nbsp;Jiping Xu ,&nbsp;Zhiyao Zhao","doi":"10.1016/j.conengprac.2025.106288","DOIUrl":"10.1016/j.conengprac.2025.106288","url":null,"abstract":"<div><div>Phase lag is common in the heading system of unmanned surface vessels (USVs). This paper presents a general design method for combining sliding-mode control with U-control-based active disturbance-rejection control (UADRC). By introducing a nonlinear term, the model can reveal the differential equation of the system control error as a general convergence law in sliding-mode control. Simultaneously, the sliding-mode control law with a fast convergence speed is presented for system errors. The numerical simulations showed that the UADRC had smaller phase lag, a faster response speed, and fewer fluctuations, thus verifying the effectiveness of the proposed method in reducing phase lag.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106288"},"PeriodicalIF":5.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534366","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":"Consistency-based diagnosis using data-driven residuals and limited training data","authors":"Arman Mohammadi,&nbsp;Mattias Krysander,&nbsp;Daniel Jung","doi":"10.1016/j.conengprac.2025.106283","DOIUrl":"10.1016/j.conengprac.2025.106283","url":null,"abstract":"<div><div>Effective fault diagnosis is crucial for improving the durability and reliability of automotive systems. Developing a diagnostic system with desirable fault isolability requires an accurate model and/or representative training data covering all possible faults. One promising approach involves using physics-based neural network residuals, known as grey-box models. These networks, designed to represent the system’s nominal behavior and trained on fault-free data, are particularly advantageous when training data from faults are scarce. By incorporating causal relationships derived from physical insight, grey-box models retain the structural fault sensitivity of model-based residuals, enabling consistency-based diagnosis decision logic. However, despite their high accuracy in supervised learning benchmarks, neural networks often struggle with misclassification due to out of distribution data, a significant concern in diagnostic applications where false alarms are costly. This study highlights the importance of uncertainty quantification in neural network-based regression models and examines the interplay between different types of uncertainty in diagnostics. To address both epistemic and aleatoric uncertainties and achieve desirable fault isolation, the study applies adaptive thresholds and a measure for testing the validity of the residuals. Additionally, it proposes a consistency-based diagnosis framework using data-driven residuals, with its effectiveness demonstrated on an aftertreatment system of a heavy-duty truck under various drive cycles and fault scenarios.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106283"},"PeriodicalIF":5.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new compact belief rule model for fault diagnosis","authors":"Zhichao Ming,&nbsp;Zhijie Zhou,&nbsp;Changhua Hu,&nbsp;Zhichao Feng,&nbsp;Zheng Lian,&nbsp;Chunchao Zhang","doi":"10.1016/j.conengprac.2025.106298","DOIUrl":"10.1016/j.conengprac.2025.106298","url":null,"abstract":"<div><div>Amidst growing scrutiny of “black box” models in fault diagnosis applications that demand high interpretability, rule-based models are regaining prominence due to their inherent interpretability. Belief rule-based (BRB) models with belief distributions stand out in fault diagnosis. Nevertheless, the incorporation of multiple fault features introduces a substantial challenge for the BRB modeling, due to the phenomenon of combinatorial explosion, which leads to an exponential escalation in the number of rules required. To tackle the root cause of the exponential growth of rules in the BRB model, a new compact belief rule model (CBRM) for fault diagnosis is proposed. A flexible rule antecedent that leverages the similarity of reference values, eschewing the Cartesian product structure, is proposed to effectively diminish the complexity and size of the fault diagnosis model. Consequently, a rule activation method leveraging the hash algorithm has been proposed, wherein the spatial relationship between a sample and a rule is translated into binary code. The rule closest to the sample in each space is activated, and its corresponding activation weight is calculated combining the matching degree, fault features weights and rules weights. Subsequently, the reasoning and optimization of the CBRM are conducted to grab the diagnostic results and update parameters. Eventually, the fault diagnosis for relays is utilized to validate the proposed method.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106298"},"PeriodicalIF":5.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510418","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":"Active Network Management via grid-friendly electromobility control for curtailment minimization","authors":"Mladen Čičić ,&nbsp;Carlos Vivas ,&nbsp;Carlos Canudas-de-Wit ,&nbsp;Francisco R. Rubio","doi":"10.1016/j.conengprac.2025.106289","DOIUrl":"10.1016/j.conengprac.2025.106289","url":null,"abstract":"<div><div>We propose an integrated power and transportation system control framework, combining the power grid model with a macroscopic electromobility model including charging stations under V2G operation. In this framework, the electrical vehicles (EVs) act as energy storage, but also as additional virtual power grid links, transporting energy from one point to another. This new holistic approach is used as a basis for optimal control design seeking to provide Active Network Management, in order to minimize curtailment of renewable energy sources and loads at various ports of the network, while accounting for the structural limitation of the grid and other constraints necessary for the optimal operation of the EVs. The proposed control scheme is shown to be able to outperform uncoordinated EV charging in terms of total curtailment in various studied scenarios. Additionally, we study the case when public charging stations are able to incentivize or disincentivise EVs to use them, by dynamically varying their charging price throughout the day, and show that this additional control input can further reduce curtailment in certain scenarios.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106289"},"PeriodicalIF":5.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model parameter identification for CNC machine tool feed drive using robust recursive least squares with non-temporal adaptive weight filter","authors":"Jong-Min Lim,&nbsp;Ji-Myeong Park,&nbsp;Soon-Hong Hwang,&nbsp;Sangwon Kang,&nbsp;Byung-Kwon Min","doi":"10.1016/j.conengprac.2025.106300","DOIUrl":"10.1016/j.conengprac.2025.106300","url":null,"abstract":"<div><div>The identification result of system model parameters determines the model accuracy and its applications, such as dynamic system prediction and task performance improvement. The recursive least squares (RLS) methods are widely used for online identification because of the low computational load and high performance. However, the conventional RLS-based algorithms have limitations in improving the identification of time-invariant parameters by adjusting parameter update size, since it is challenging to make the measured system data non-temporally weighted. In addition, the user-defined factors significantly affect the identification accuracy. This requires an additional process for selecting optimal factors, leading to decreased identification efficiency. To address these limitations, this study proposes a non-temporally weighted RLS algorithm that improves the identification of time-invariant parameters with an adaptive weight filter based on the Lyapunov stability theory. The proposed algorithm is derived from a generalized parametric decoupled cost function, which allows applying multiple variable weights while making the measured system data non-temporally weighted throughout the identification process. To compute optimal weights at each step, a weight determination rule is derived from the Lyapunov stability theory, guaranteeing the identification stability. The effect of user-defined factors on the identification is eliminated by the Lyapunov stability theory-based adaptive weight filter and the property that the proposed algorithm updates parameters considering both weights and the parameter identification error at each step. The validation against conventional algorithms using simulation of feed drive model parameter identification and experiment using a commercial CNC machine tool confirms that the proposed method improves the online model parameter identification with high robustness against the user-defined factor and high identification accuracy.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106300"},"PeriodicalIF":5.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510474","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":"An integrated framework for motion planning and trajectory optimization of AGVs using spatio-temporal safety corridors","authors":"Xi Zhang ,&nbsp;Yaomin Lu ,&nbsp;Zhiyang Ju ,&nbsp;Jiarui Song ,&nbsp;Zheng Zang ,&nbsp;Jianyong Qi ,&nbsp;Jianwei Gong","doi":"10.1016/j.conengprac.2025.106297","DOIUrl":"10.1016/j.conengprac.2025.106297","url":null,"abstract":"<div><div>Efficiently generating safe and smooth trajectories for autonomous ground vehicles (AGVs) is a crucial and challenging task, particularly in dynamic environments with moving obstacles. This paper proposes an integrated motion planning and trajectory optimization (MPTO) framework that employs an optimization-based spatio-temporal safety corridors (STSC) to ensure trajectory smoothness and safety from a three-dimensional spatio-temporal perspective. The proposed MPTO framework comprises two layers. In the first layer, a multi-objective quadratic programming (MOQP) method was developed with the objective of rapidly generating smoothly varying STSC. The multi-objective cost function provides a comprehensive evaluation of the corridors in terms of their size, direction, and smoothness. Additionally, a convex polygonal feasible area (CPFA) was proposed to provide a linear obstacle-avoidance constraint for the MOQP. The smooth STSC provides within-corridor constraints for trajectory optimization, thereby ensuring collision avoidance of obstacles and reducing the dependence of trajectory optimization on the reference trajectory. In the second layer, an optimal trajectory generation method using polynomials is proposed to generate smooth and efficient trajectories. With smooth STSC constraints, the trajectory optimization model primarily focuses on smoothness, ensuring that the trajectory remains safe and smooth even with sudden changes in the feasible area. Finally, the proposed MPTO framework is validated through simulations and real vehicle experiments.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106297"},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474451","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":"Federated fault diagnosis using data fusion in large-scale heterogeneous unmanned systems","authors":"Runze Li ,&nbsp;Bin Jiang ,&nbsp;Yan Zong ,&nbsp;Ningyun Lu ,&nbsp;Li Guo","doi":"10.1016/j.conengprac.2025.106284","DOIUrl":"10.1016/j.conengprac.2025.106284","url":null,"abstract":"<div><div>Heterogeneous unmanned systems (HUS) consist of multiple types of unmanned sub-systems, and when one or more sub-systems experience failures, it can severely impact the overall system’s operation. Therefore, establishing effective fault diagnosis(FD) methods is crucial for ensuring the safety and reliability of heterogeneous unmanned systems. This paper proposes a federated fault diagnosis method based on data fusion, which combines visual images and multi-sensor information to enhance the fault identification capability of heterogeneous unmanned systems in complex environments. By using an offline broad reinforcement learning strategy, we propose a Federated Broad Reinforcement Learning fault diagnosis method. It achieves high-precision fault diagnosis under various fault conditions by iteratively reconstructing fused data and knowledge. Finally, the proposed method is validated on a hardware-in-the-loop (HIL) simulator in large-scale heterogeneous unmanned systems. Experimental results show that the proposed method improves fault diagnosis accuracy and enhances the safety and reliability of the system.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"159 ","pages":"Article 106284"},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474450","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}