Control Engineering Practice最新文献

英文中文

Identification of finite-time delay and regressors for the design of soft sensors in the presence of input collinearity

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-02-04 DOI: 10.1016/j.conengprac.2025.106267

Luca Patanè , Salvatore Graziani , Maria Gabriella Xibilia

Soft sensors (SSs) play an important role in Industry 4.0 by providing estimates of process variables when conventional sensors are impractical or unavailable. The design of SSs requires estimating the possible output finite-time delay, e.g. due to the measurement process or transport phenomena, and selecting the correct model regressors. In this context, this paper presents a new method called multiple correlation delay and regressor selection (MC-DRS), which can be used to simultaneously identify the output finite-time delay and the regressors for dynamic models in the class of finite impulse-response models. The method, which belongs to the class of filter approaches, uses data stored in historical databases and solves problems caused by the collinearity of the inputs. The MC-DRS has a low computational complexity and outperforms existing model-agnostic methods such as correlation-based methods (Pearson, Kendall, Spearman and distance correlations), maximal information coefficient, Lipshitz quotients and minimum redundancy maximum relevance algorithm. Synthetic case studies and an industrial benchmark validate its effectiveness and underline its advantages in SS design for Industry 4.0 applications. In detail, the results obtained show that the proposed method was able to detect the correct finite-time delay and the number of regressors in 100% of the case studies. None of the other methods were able to correctly identify both system parameters. Among these methods, the distance correlation was able to detect the finite-time delay in 50% of the cases, while the Lipshitz quotients were able to detect the number of regressors in 50% of the cases.

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引用次数: 0

On the attitude consensus of rigid bodies using exponential coordinates

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-02-04 DOI: 10.1016/j.conengprac.2025.106262

Javier Pliego-Jiménez , Miguel Sidón-Ayala , César Cruz-Hernadez , Marco A. Arteaga

Consensus is an emergent behavior that occurs when the robots or agents of a robotic network reach an agreement and converge to a common state. Consensus is essential in many multi-robot coordination tasks. This paper addresses the attitude consensus problem in a network of fully actuated rigid bodies. We propose two consensus control laws based on the exponential coordinates of rotation. The first controller uses absolute attitude measurements, whereas the second one employs relative measurements. Both control algorithms avoid potential singularities of the exponential coordinates and achieve almost global asymptotic stability. Under certain conditions on the network topology, the proposed consensus protocols work for either bidirectional and unidirectional communication channels. Finally, we provide numerical simulations and experimental results to assess the performance of the proposed control laws.

引用次数: 0

Control method and parametric robustness analysis of PMSM driving spiral power spring based on incremental backstepping control

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-02-04 DOI: 10.1016/j.conengprac.2025.106266

Yang Yu , Qianhui Zhang , Zongzhe Yu , Qiwen Pang

Permanent magnet synchronous motor (PMSM) with simple structure and high ratio of torque to inertia is used as the energy conversion device to tighten or release spiral power spring (SPS) in spiral spring energy storage (SSES) system. For simultaneous variations of torque and inertia of SPS and nonlinear characteristics of multivariable and strong coupling of PMSM in the operation of SSES system, current incremental backstepping controller is devised through Taylor series expansion based on the built mathematical model of SSES system and designed speed backstepping controller. The stability of the control approach and the robustness of implicit and explicit model parameters are investigated, and the control gains in incremental backstepping control (IBC) are determined. Theoretical analysis indicates that implicit model parameters uncertainty has no effect on the control performance in any case, and explicit model parameters uncertainty hardly affects system performance under IBC with appropriate control gains. The simulation and experimental results show that PMSM in IBC can track the references more accurately and quickly, compared with other three control algorithms. The dynamic tracking performance of speed and current are improved even in an abrupt change of the reference speed, which proves that the proposed control method has strong stability and robustness.

{"title":"Control method and parametric robustness analysis of PMSM driving spiral power spring based on incremental backstepping control","authors":"Yang Yu , Qianhui Zhang , Zongzhe Yu , Qiwen Pang","doi":"10.1016/j.conengprac.2025.106266","DOIUrl":"10.1016/j.conengprac.2025.106266","url":null,"abstract":"<div><div>Permanent magnet synchronous motor (PMSM) with simple structure and high ratio of torque to inertia is used as the energy conversion device to tighten or release spiral power spring (SPS) in spiral spring energy storage (SSES) system. For simultaneous variations of torque and inertia of SPS and nonlinear characteristics of multivariable and strong coupling of PMSM in the operation of SSES system, current incremental backstepping controller is devised through Taylor series expansion based on the built mathematical model of SSES system and designed speed backstepping controller. The stability of the control approach and the robustness of implicit and explicit model parameters are investigated, and the control gains in incremental backstepping control (IBC) are determined. Theoretical analysis indicates that implicit model parameters uncertainty has no effect on the control performance in any case, and explicit model parameters uncertainty hardly affects system performance under IBC with appropriate control gains. The simulation and experimental results show that PMSM in IBC can track the references more accurately and quickly, compared with other three control algorithms. The dynamic tracking performance of speed and current are improved even in an abrupt change of the reference speed, which proves that the proposed control method has strong stability and robustness.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"157 ","pages":"Article 106266"},"PeriodicalIF":5.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143302","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 semi-centralized multi-agent RL framework for efficient irrigation scheduling

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-02-01 DOI: 10.1016/j.conengprac.2024.106183

Bernard T. Agyeman , Benjamin Decardi-Nelson , Jinfeng Liu , Sirish L. Shah

Efficient water management in agriculture is essential for addressing the growing freshwater scarcity crisis. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising method for solving daily irrigation scheduling problems in spatially variable fields, where management zones are employed to account for field variability. To enhance the application of MARL to address daily irrigation scheduling in large-scale fields with significant spatial variation, this study proposes a Semi-Centralized MARL (SCMARL) framework. The SCMARL framework adopts a hierarchical structure, decomposing the daily irrigation scheduling problem into two levels of decision-making. At the top level, a centralized coordinator agent determines irrigation timing, which is modeled as a discrete variable, based on field-wide soil moisture data, crop conditions, and weather forecasts. At the lower level, decentralized local agents use local soil moisture, crop, and weather information to determine the appropriate irrigation amounts for each management zone. To address the issue of non-stationarity in this framework, a state augmentation technique is employed, wherein the coordinator’s decision is incorporated into the decision-making process of the local agents. The SCMARL framework, which leverages the Proximal Policy Optimization algorithm for training the agents, is evaluated on a large-scale field in Lethbridge, Canada, and compared with an existing MARL irrigation scheduling approach. The results demonstrate improved performance, achieving a 4.0% reduction in water use and a 6.3% increase in irrigation water use efficiency.

{"title":"A semi-centralized multi-agent RL framework for efficient irrigation scheduling","authors":"Bernard T. Agyeman , Benjamin Decardi-Nelson , Jinfeng Liu , Sirish L. Shah","doi":"10.1016/j.conengprac.2024.106183","DOIUrl":"10.1016/j.conengprac.2024.106183","url":null,"abstract":"<div><div>Efficient water management in agriculture is essential for addressing the growing freshwater scarcity crisis. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising method for solving daily irrigation scheduling problems in spatially variable fields, where management zones are employed to account for field variability. To enhance the application of MARL to address daily irrigation scheduling in large-scale fields with significant spatial variation, this study proposes a Semi-Centralized MARL (SCMARL) framework. The SCMARL framework adopts a hierarchical structure, decomposing the daily irrigation scheduling problem into two levels of decision-making. At the top level, a centralized coordinator agent determines irrigation timing, which is modeled as a discrete variable, based on field-wide soil moisture data, crop conditions, and weather forecasts. At the lower level, decentralized local agents use local soil moisture, crop, and weather information to determine the appropriate irrigation amounts for each management zone. To address the issue of non-stationarity in this framework, a state augmentation technique is employed, wherein the coordinator’s decision is incorporated into the decision-making process of the local agents. The SCMARL framework, which leverages the Proximal Policy Optimization algorithm for training the agents, is evaluated on a large-scale field in Lethbridge, Canada, and compared with an existing MARL irrigation scheduling approach. The results demonstrate improved performance, achieving a 4.0% reduction in water use and a 6.3% increase in irrigation water use efficiency.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106183"},"PeriodicalIF":5.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176191","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

Adversarial domain adaptation with norm constraints for enhanced fault diagnosis in pumping units via surface motor power

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-01-31 DOI: 10.1016/j.conengprac.2025.106265

Jiye Zuo, Shuqiang Wang, Shimin Dong, Weicheng Li, Yao Zhang

Timely downhole fault diagnosis of the pumping unit is a critical task in minimizing oilwell downtime and reducing energy consumption. Existing work has been exploring using low-cost, real-time surface motor power instead of installing dynamometer sensors at the wellhead for diagnosing faults. However, the high similarity of motor power samples across pumping condition categories leads to time-consuming and laborious manual labeling. Additionally, variations between wells cause shifting in the motor power feature distribution, reducing the diagnosis accuracy of traditional deep learning-based diagnoses. To address these challenges, this paper proposes a novel adversarial domain adaptation network with norm constraints (ADANN) for diagnosing faults in pumping units. First, the approach redefines feature extraction by incorporating modulated deformable convolution layers in place of traditional convolution modules within the deep residual network, thereby allowing for more precise and adaptive capture of geometric variations in motor power features. During domain adaptation, we innovatively introduce a norm-constrained alignment strategy into the domain adversarial training. The norm constraint, by maximizing the output variance of the batch normalization layer, encourages the model to learn more dispersed feature representations. This further enhances the ability of domain adversarial training to learn domain-invariant features, thereby improving generalization performance on the unlabeled target domain. Finally, comparative experiments on the collected dataset from real oil wells demonstrate the superior performance of ADANN.

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引用次数: 0

Analysis of modeling and performance for PV and energy storage integration in suburban railways

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-01-30 DOI: 10.1016/j.conengprac.2025.106252

Nutthaka Chinomi , Zhongbei Tian , Ning Yang , Nakaret Kano , Kejian Song , Lin Jiang

The rail sector faces growing pressure to reduce energy consumption and carbon emissions, in line with global sustainability goals. Electrification of rail routes, along with the integration of renewable energy sources (RES), has become critical for enhancing energy efficiency and minimizing emissions. This study explores the integration of photovoltaic (PV) systems and energy storage systems (ESS) into AC railways, focusing on their impact on energy consumption and overall system performance. A mathematical model of the railway system is developed, and two case studies are performed on a standard AC railway route servicing suburban train. The model’s accuracy is validated according to the British Standard EN50641 under both normal and outage conditions, with results generally aligning with reference values, except for a minor deviation observed during outage scenarios. In Case Study 1, an initial examination compares integration at the substation level with integration at the catenary level, aiming to provide insights into various integration locations. The analysis reveals that the lowest daily operational cost is achieved at the substation integration point (0 km). However, integration at the 20 km catenary point achieves a comparable cost reduction, with a decrease of 8.6%, and the cost difference between the two locations is negligible, at only 0.01%. Following this, Case Study 2 investigates how varying capacities of PV and ESS affect energy generation and daily operational costs. The findings indicate that increasing capacities leads to expected reductions in both energy generation and operational costs. Nevertheless, these cost reductions may not be optimal due to the ESS control strategy employed in the study.

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引用次数: 0

Online prognostic failure AIoT system for industrial generators maintenance service based two-stage deep learning algorithm

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-01-30 DOI: 10.1016/j.conengprac.2025.106263

Da-Thao Nguyen , Thanh-Phuong Nguyen , Ming-Yuan Cho

Intelligent anomaly diagnosis for industrial generators is essential in providing appropriate maintenance service, which makes it challenging to identify machine failures due to a complicated operational environment. For these reasons, an AIoT framework for anomaly diagnosis of industrial 125kW/250 kW generators is developed to provide indicators in maintenance services based on a two-stage deep learning convolution neural network and gate recurrent unit (CNN-GRU). In the proposed AIoT system, the IoT module collects different working features of 125kW/250 kW diesel generators in the experimental setup, including three-phase current, frequency, vibration, three-phase voltage, engine temperature, starting battery DC voltage, and power factor to generate labeled anomaly conditioning representative data. The convolution neural network is firstly deployed to reduce the dimensionality of 2D historical data, and then all the extracted valuable features are transferred to the gate recurrent unit to process sequential information. The developed algorithm was evaluated with different deep learning techniques, including the recurrent neural network (RNN), GRU, CNN, and long short-term memory (LSTM) by various benchmarks and data sequential horizons. Experiments prove that the developed CNN-GRU contains superior diagnosis capability and improved accuracy compared to other state-of-the-art deep learning models in a 10-second sample frequency dataset.

{"title":"Online prognostic failure AIoT system for industrial generators maintenance service based two-stage deep learning algorithm","authors":"Da-Thao Nguyen , Thanh-Phuong Nguyen , Ming-Yuan Cho","doi":"10.1016/j.conengprac.2025.106263","DOIUrl":"10.1016/j.conengprac.2025.106263","url":null,"abstract":"<div><div>Intelligent anomaly diagnosis for industrial generators is essential in providing appropriate maintenance service, which makes it challenging to identify machine failures due to a complicated operational environment. For these reasons, an AIoT framework for anomaly diagnosis of industrial 125kW/250 kW generators is developed to provide indicators in maintenance services based on a two-stage deep learning convolution neural network and gate recurrent unit (CNN-GRU). In the proposed AIoT system, the IoT module collects different working features of 125kW/250 kW diesel generators in the experimental setup, including three-phase current, frequency, vibration, three-phase voltage, engine temperature, starting battery DC voltage, and power factor to generate labeled anomaly conditioning representative data. The convolution neural network is firstly deployed to reduce the dimensionality of 2D historical data, and then all the extracted valuable features are transferred to the gate recurrent unit to process sequential information. The developed algorithm was evaluated with different deep learning techniques, including the recurrent neural network (RNN), GRU, CNN, and long short-term memory (LSTM) by various benchmarks and data sequential horizons. Experiments prove that the developed CNN-GRU contains superior diagnosis capability and improved accuracy compared to other state-of-the-art deep learning models in a 10-second sample frequency dataset.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"157 ","pages":"Article 106263"},"PeriodicalIF":5.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143291","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

Bidirectional heterogeneous synergistic fault detection using multiple local data in large-scale industrial systems

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-01-28 DOI: 10.1016/j.conengprac.2025.106251

Jianbo Yu , Hang Ruan , Zhi Li , Shifu Yan , Xiaofeng Yang

Technological advances have increased the complexity of industrial processes, such as semiconductor and manufacturing systems, leading to large-scale system integration. Consequently, the operational states of such systems rely heavily on complex and high-dimensional data for an effective representation. Existing strategies, such as local and local–global methods, focus on capturing local features and their interactions with global characteristics but often overlook the heterogeneity among local input units and the interconnections between subsystems within the same larger-scale system, resulting in flawed assumptions and information loss during modeling. To tackle these challenges, this paper proposes a bidirectional heterogeneous synergistic model (BHS) based on multiple local groups. Specifically, a heterogeneity-constrained agglomerative hierarchical clustering method is developed to capture and optimize the heterogeneity between local groups. Next, multiple feature extractors are constructed to capture fine-grained local features, enhancing the capability of large-scale systems to represent critical information. Subsequently, a bidirectional attention mechanism based on mutual information is proposed to synergistically uncover subsystem correlations within the same system, compensating for the loss of multiscale synergy during local modeling. Finally, feature fusion is employed to integrate information across subsystems, enabling unsupervised modeling for large-scale industrial systems. Experimental results from a simulation process, a benchmark process, and a practical semiconductor measurement task demonstrate the superiority of the proposed approach in fault detection tasks for large-scale industrial systems.

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引用次数: 0

Hybrid Input–Output Probabilistic Slow Feature Analysis for adaptive process monitoring

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-01-27 DOI: 10.1016/j.conengprac.2025.106254

Junhao Chen , Hao Wang , Chunhui Zhao , Min Xie

Industrial process data are usually dynamic due to closed-loop control systems. Current dynamic latent-variable methods generally assume that the dynamics of the process are fixed. This assumption has two implications. First, the system is not influenced by external inputs. Second, the system parameters remain time-invariant. However, in real industrial scenarios, systems are often regulated by manipulated variables and their parameters may drift over time. Failure to account for these time-varying factors will result in an increasing disparity between existing models and the actual system, ultimately leading to unreliable monitoring results. To address this issue, a Hybrid Input–Output Probabilistic Slow Feature Analysis (H-IOPSFA) model is proposed along with an adaptive process monitoring approach. The H-IOPSFA model is designed to account for the directed effect of the manipulated variables on the system dynamics and process variables in the presence of continuous and binary variables. A recursive model updating method is then introduced to accommodate normal process changes, offering significantly faster convergence than training from scratch. Additionally, by simultaneously monitoring dynamic and static variations, an adaptive monitoring strategy is developed to effectively differentiate between real faults and operating condition changes. Finally, the H-IOPSFA model and the adaptive monitoring method are applied to the TE process and a practical industrial process. Compared with classical dynamic monitoring methods, the proposed method achieves the highest fault detection rate (98.63% on the TE and 96.61% on the practical process) while realizing an acceptable fault alarm rata (8.23% on the TE and 7.33% on the practical process), which demonstrates its superior performance.

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引用次数: 0

Multivariable robust control of sirius modular current source prototype

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-01-22 DOI: 10.1016/j.conengprac.2025.106244

Thiago T. Cardoso , Pedro M. de Almeida , André A. Ferreira , Gabriel O. Brunheira , Bruno E. Limeira , Pedro G. Barbosa , Vinícius F. Montagner

This paper presents the design and experimental validation of a multivariable robust control strategy applied to the Brazilian Synchrotron Light Laboratory booster current source. The source, tasked with delivering a triangular waveform current, necessitates precise tracking with an error tolerance of less than 100 parts per million, in order to precisely inject electrons into the storage ring. Characterized by a complex, high-order multi-input multi-output structure with multiple series and parallel power modules, the system complexity is addressed through a model reduction technique based on the Hankel norm. Leveraging the reduced-order plant during controller design not only simplifies the system but also results in a lower-order controller. To guarantee robust stability and performance for the full-order plant, the approximation error is reintroduced as uncertainty into the reduced-order model. The controller design employs a weighted

H_{\infty}

approach. Experimental validation using a small-scale prototype confirms the effectiveness of the proposed methodology in achieving precise tracking and robust performance.

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