Control Engineering Practice最新文献_第2页

Burn-through point prediction and control based on multi-cycle dynamic spatio-temporal feature extraction 基于多周期动态时空特征提取的烧穿点预测与控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-21 DOI: 10.1016/j.conengprac.2024.106165

Xiaoxia Chen , Chengshuo Liu , Hanzhong Xia , Zhengwei Chi

{"title":"Burn-through point prediction and control based on multi-cycle dynamic spatio-temporal feature extraction","authors":"Xiaoxia Chen , Chengshuo Liu , Hanzhong Xia , Zhengwei Chi","doi":"10.1016/j.conengprac.2024.106165","DOIUrl":"10.1016/j.conengprac.2024.106165","url":null,"abstract":"<div><div>Burn-Through Point (BTP) is a critical state in the sintering process, and maintaining a stable BTP is crucial for ensuring the quality of sintered products. However, the complex mechanistic relationships during the sintering process make it challenging to extract meaningful correlations between data, leading to suboptimal performance of prediction-based control methods. To address this issue, this paper proposed a BTP prediction method based on multi-period dynamic spatio-temporal extraction. Building upon this, a comprehensive fuzzy controller based on historical and future state recognition is introduced to achieve stable BTP. Firstly, a time series alignment method based on multi-cycle partitioning is proposed. The Fast Fourier Transform (FFT) operations is introduced to identify hidden data patterns within the observation sequence. Time series alignment is achieved by weighted time delay through fuzzy curve analysis applied to different data patterns. Temporal features are extracted along the temporal dimension using multi-scale 2D convolution, while the graph learning module generates the graph structure by introducing an attentional mechanism to capture the inter-variable dependencies in the learning window. Next, the spatial feature extraction module uses the outputs of the above two modules as inputs to further capture potential spatial features in the time series. Finally, the comprehensive fuzzy controller, by recognizing historical and future states, provides recommendations for the current sintering process speed, stabilizing the sintering process towards the desired operating states. According to the simulation results on actual datasets, this method not only exhibits high predictive accuracy but also effectively maintains control over BTP within a fluctuation range with a mean square error of 0.0109.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106165"},"PeriodicalIF":5.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700408","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}

引用次数: 0

A resilient consensus algorithm with inputs for the distributed monitoringof cyber-physical systems 带输入的弹性共识算法，用于网络物理系统的分布式监控

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-21 DOI: 10.1016/j.conengprac.2024.106166

Sabato Manfredi , David Angeli , Ciro Tortora

引用次数: 0

3DoF-KF HMPC: A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems 3DoF-KF HMPC：基于卡尔曼滤波器的混合逻辑动态系统混合模型预测控制算法

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-21 DOI: 10.1016/j.conengprac.2024.106171

Owais Khan , Mohamed El Mistiri , Sarasij Banerjee , Eric Hekler , Daniel E. Rivera

{"title":"3DoF-KF HMPC: A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems","authors":"Owais Khan , Mohamed El Mistiri , Sarasij Banerjee , Eric Hekler , Daniel E. Rivera","doi":"10.1016/j.conengprac.2024.106171","DOIUrl":"10.1016/j.conengprac.2024.106171","url":null,"abstract":"<div><div>This paper presents the formulation, design procedure, and application of a hybrid model predictive control (HMPC) scheme for hybrid systems that is embedded in a mixed logical dynamical (MLD) framework. The proposed scheme adopts a three degrees-of-freedom (3DoF) tuning method to accomplish precise setpoint tracking and ensure robustness in the face of disturbances (both measured and unmeasured) and uncertainty. Furthermore, the HMPC algorithm employs setpoint and disturbance anticipation to proactively enhance controller performance and potentially reduce control effort. Slack variables in the objective function prevent the mixed-integer quadratic problem from becoming infeasible. The effectiveness of the proposed algorithm is demonstrated through its application in three distinct case studies, which include control of production–inventory systems, time-varying behavioral interventions for physical activity, and management of epidemics/pandemic prevention. These case studies indicate that the HMPC algorithm can effectively manage hybrid dynamics, setpoint tracking and disturbance rejection in diverse and demanding circumstances, while tuned to perform well in the presence of nonlinearity and uncertainty.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106171"},"PeriodicalIF":5.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700407","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}

引用次数: 0

Ride-pooling Electric Autonomous Mobility-on-Demand: Joint optimization of operations and fleet and infrastructure design 拼车电动自主按需移动：联合优化运营、车队和基础设施设计

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-20 DOI: 10.1016/j.conengprac.2024.106169

Fabio Paparella, Karni Chauhan, Luc Koenders, Theo Hofman, Mauro Salazar

{"title":"Ride-pooling Electric Autonomous Mobility-on-Demand: Joint optimization of operations and fleet and infrastructure design","authors":"Fabio Paparella, Karni Chauhan, Luc Koenders, Theo Hofman, Mauro Salazar","doi":"10.1016/j.conengprac.2024.106169","DOIUrl":"10.1016/j.conengprac.2024.106169","url":null,"abstract":"<div><div>This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particular, we first devise a multi-layer time-invariant network flow model that jointly captures the position and state of charge of the vehicles. Second, we frame the time-optimal operational problem of the fleet, including charging and ride-pooling decisions as a mixed-integer linear program, whereby we jointly optimize the placement of the charging infrastructure. Finally, we perform a case-study using Manhattan taxi-data. Our results indicate that jointly optimizing the charging infrastructure placement allows to decrease overall energy consumption of the fleet and vehicle hours traveled by approximately 1% compared to a heuristic placement. Most significantly, ride-pooling can decrease such costs considerably more, and up to 45%. Finally, we investigate the impact of the vehicle choice on the energy consumption of the fleet, comparing a lightweight two-seater with a heavier four-seater, whereby our results show that the former and latter designs are most convenient for low- and high-demand areas, respectively.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106169"},"PeriodicalIF":5.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701108","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}

引用次数: 0

Anomaly detection using invariant rules in Industrial Control Systems 在工业控制系统中使用不变规则进行异常检测

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-19 DOI: 10.1016/j.conengprac.2024.106164

Qilin Zhu , Yulong Ding , Jie Jiang , Shuang-Hua Yang

{"title":"Anomaly detection using invariant rules in Industrial Control Systems","authors":"Qilin Zhu , Yulong Ding , Jie Jiang , Shuang-Hua Yang","doi":"10.1016/j.conengprac.2024.106164","DOIUrl":"10.1016/j.conengprac.2024.106164","url":null,"abstract":"<div><div>Industrial Control Systems (ICS) are intelligent control systems that integrate computing, physical processes, and communication to manage critical infrastructures such as power grids, oil and gas processing facilities, and water treatment plants. In recent years, ICS have been increasingly targeted by malicious attacks, causing severe consequences. Anomaly detection systems utilized in ICS are crucial in safeguarding ICS from potential threats by sending out an alert upon detecting any network attacks. However, existing methods for ICS anomaly detection often suffer from limitations. Supervised machine learning methods encounter the issue of imbalanced positive and negative samples, while residual-based anomaly detection methods face challenges in detecting stealthy attacks. This paper presents an unsupervised anomaly detection method for ICS using association rule mining techniques. Utilizing the proposed variation-driven predicate generation strategy, the method incorporates temporal features of sensor readings into the generated predicates, achieving the mining of invariant rules that take into account the temporal dependencies among physical variables. This approach allows for a more comprehensive exploration of the invariant patterns maintained in the dynamic processes of systems. Through experiments conducted on two public datasets, the method demonstrates high detection efficiency, meeting the real-time demands of online detection. Experimental results showcase its notable efficacy in anomaly detection, with a substantial enhancement in the recall rate. Furthermore, the method’s capability to promptly issue warnings enables it to detect multiple attacks with low latency.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106164"},"PeriodicalIF":5.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700404","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}

引用次数: 0

A constrained instrumental variable method for identification of industrial robots 用于识别工业机器人的受限工具变量法

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-19 DOI: 10.1016/j.conengprac.2024.106168

Fabio Ardiani , Alexandre Janot , Mourad Benoussaad

{"title":"A constrained instrumental variable method for identification of industrial robots","authors":"Fabio Ardiani , Alexandre Janot , Mourad Benoussaad","doi":"10.1016/j.conengprac.2024.106168","DOIUrl":"10.1016/j.conengprac.2024.106168","url":null,"abstract":"<div><div>Robot identification is a prolific topic with a long history of results spanning decades. In recent years, there has been a renewal of interest in this problem mainly due to a rapid increase in robotic hardware platforms capable of accurate model-based control. The standard approach exploits the inverse dynamic model’s linearity to dynamic parameters and uses the linear least-squares (LS) estimation. Since we identify robots with closed-loop procedures, a correlation between errors remains, and we should prefer the Instrumental Variable (IV) method over the LS estimation. Thanks to the increase in computational power, recent works suggest inserting physical constraints to ensure the physical plausibility of estimates. These works have emphasized the usefulness of these physical constraints, but few papers consider their insertion into IV methods, the consistency and optimality of estimates, and the effect of constraints on estimates not addressed. This paper presents a new constrained IV approach that uses physical constraints. It consists of two nested iterative algorithms: an outer one that is a standard IV method and an inner one that accounts for the constraints solved by a Gauss–Newton algorithm. Besides, the conditions to obtain consistent and optimal estimates are emphasized. Experimental results and comparisons with other methods carried out with the TX40 robot show the feasibility and effectiveness of such an IV method: we can identify 60 physically consistent parameters in less than one minute.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106168"},"PeriodicalIF":5.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700405","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}

引用次数: 0

Hybrid self-learning model for the prediction and control of sintering furnace temperature 预测和控制烧结炉温度的混合自学模型

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-18 DOI: 10.1016/j.conengprac.2024.106159

Yuanshen Dai , Ning Chen , Zhijiang Shao

{"title":"Hybrid self-learning model for the prediction and control of sintering furnace temperature","authors":"Yuanshen Dai , Ning Chen , Zhijiang Shao","doi":"10.1016/j.conengprac.2024.106159","DOIUrl":"10.1016/j.conengprac.2024.106159","url":null,"abstract":"<div><div>Ternary cathode materials are important components of lithium-ion batteries. However, the sintering process during manufacturing is challenging to control due to the inaccessibility of key dynamic variables and the frequent fluctuations in operating conditions. These lead to high energy consumption and inconsistent product quality. In this paper, we propose a hybrid self-learning prediction model and control method for sintering furnace temperature based on both first-principle and process data. Firstly, a mechanism model with temperature time delay is established based on energy flow analysis in the furnace. To capture the tail gas temperature dynamic in the mechanism model, a Ventingformer-based prediction data-driven model is proposed. In this model, a memory updating technique and an autoregressive module based on the Transformer framework are developed to identify long-time dependencies and respond to variations in input sequences. Then, a hybrid self-learning modeling framework is designed. Based on the established hybrid model, a multiscale objective function-based nonlinear model predictive control (MSCF-NMPC) method is proposed to achieve precise tracking control of the internal temperature in the furnace. A multiscale objective function with short-term cost in terms of energy consumption and tracking accuracy as well as long-term cost in terms of energy loss is constructing in the control optimization problem. Finally, the proposed hybrid self-learning model and MSCF-NMPC method are verified using the actual process data from a sintering furnace, demonstrating the effectiveness of the proposed method. The results offer practical guidance for industrial applications.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106159"},"PeriodicalIF":5.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700403","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}

引用次数: 0

Physical-anchored graph learning for process key indicator prediction 用于工艺关键指标预测的物理锚定图学习

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-16 DOI: 10.1016/j.conengprac.2024.106167

Mingwei Jia , Lingwei Jiang , Bing Guo , Yi Liu , Tao Chen

引用次数: 0

Predictive sliding mode control for flexible spacecraft attitude tracking with multiple disturbances 预测性滑动模式控制用于具有多重干扰的灵活航天器姿态跟踪

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-16 DOI: 10.1016/j.conengprac.2024.106160

Quan-Zhi Liu, Liu Zhang, Yang Xiao, Le Zhang, Guo-Wei Fan

引用次数: 0

Spatial–temporal cooperative guidance with no-fly zones avoidance 时空协同制导，避开禁飞区

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2024-11-15 DOI: 10.1016/j.conengprac.2024.106162

Kai Zhao , Jia Song , Yang Liu

{"title":"Spatial–temporal cooperative guidance with no-fly zones avoidance","authors":"Kai Zhao , Jia Song , Yang Liu","doi":"10.1016/j.conengprac.2024.106162","DOIUrl":"10.1016/j.conengprac.2024.106162","url":null,"abstract":"<div><div>This paper proposes a three-dimensional spatial–temporal cooperative guidance law for striking maneuvering targets, with consideration of no-fly zones avoidance. A fixed-time convergent integral sliding mode guidance law based on a second-order system consensus protocol is proposed to ensure the consistency of the remaining distance and radial relative velocity, instead of using estimates of time-to-go based on small angle assumptions. In the elevation and azimuth directions, to mitigate excessive guidance commands during the initial phase, a nonlinear sliding surface and a finite-time reaching law are designed to meet impact angle constraints. In addition, considering the stagnation points escape in the process of no-fly zones avoidance an integrated cooperation and obstacle avoidance guidance law is proposed, which effectively avoids no-fly zones, accelerates the convergence speed of cooperative consistency, and reduces terminal errors. Using Lyapunov’s theory, this paper theoretically proves the fixed-time and finite-time convergence characteristics of the proposed algorithm. Simulation results indicate that the miss distance and terminal elevation and azimuth angle errors of the proposed algorithm are 55.04%, 27.5%, and 81.75% of those of the comparison algorithm, respectively.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106162"},"PeriodicalIF":5.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656848","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}

引用次数: 0