Pub Date : 2024-11-01DOI: 10.1016/j.conengprac.2024.106143
Jiandong Yang, Xuefeng Yan
Common multivariate statistical quality-related process monitoring methods often separate feature extraction from quality-related process modeling, which can lead to insufficient extraction of quality-related information. In this paper, a quality-related maximization model with global and local preservation constraints is proposed. The process data are mapped to a high-dimensional feature space using kernel projection, which better linearizes the nonlinear data. Kernel sparse representation local linear embedding is applied to adaptively determine local relationships. Based on these local relationships, global-local constraints are constructed, and quality-related features are extracted according to the principle of maximizing correlation with quality indicators, resulting in a low-dimensional embedding matrix. This embedding matrix is used for process monitoring by dividing the quality-related and quality-independent subspaces and constructing a monitoring statistical strategy. The effectiveness of the proposed method is verified using the Tennessee-Eastman process, and it is further applied to a fluid catalytic cracking process.
{"title":"Global–local preserving method of quality-related maximization and its application for process monitoring","authors":"Jiandong Yang, Xuefeng Yan","doi":"10.1016/j.conengprac.2024.106143","DOIUrl":"10.1016/j.conengprac.2024.106143","url":null,"abstract":"<div><div>Common multivariate statistical quality-related process monitoring methods often separate feature extraction from quality-related process modeling, which can lead to insufficient extraction of quality-related information. In this paper, a quality-related maximization model with global and local preservation constraints is proposed. The process data are mapped to a high-dimensional feature space using kernel projection, which better linearizes the nonlinear data. Kernel sparse representation local linear embedding is applied to adaptively determine local relationships. Based on these local relationships, global-local constraints are constructed, and quality-related features are extracted according to the principle of maximizing correlation with quality indicators, resulting in a low-dimensional embedding matrix. This embedding matrix is used for process monitoring by dividing the quality-related and quality-independent subspaces and constructing a monitoring statistical strategy. The effectiveness of the proposed method is verified using the Tennessee-Eastman process, and it is further applied to a fluid catalytic cracking process.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106143"},"PeriodicalIF":5.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571576","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}
Pub Date : 2024-11-01DOI: 10.1016/j.conengprac.2024.106133
A. Benevieri, M. Marchesoni, M. Passalacqua, P. Pozzobon, L. Vaccaro
A direct torque control (DTC) algorithm with synchronous modulation for high-power induction motors is presented in this paper. While maintaining the dynamic response and robustness of a PI-based DTC operating in the stationary reference frame, the proposed scheme is able to keep an integer PWM modulation ratio, adjusting the stator flux angle and the switching period at each control step so that the synchronicity condition is always satisfied. In this way, it is possible to achieve an improvement of the very low-frequency harmonic spectrum of the torque, in particular by reducing torque sub-harmonics. These represent one of the main problems associated with low-frequency modulation typical of high-power drives and their reduction allows to avoid drawbacks such as resonance and mechanical stresses. The performance of the proposed algorithm is evaluated with experimental tests on a small-scale test bench.
本文提出了一种针对大功率感应电机的同步调制直接转矩控制（DTC）算法。在保持基于 PI 的 DTC 在静态参考帧中运行的动态响应和鲁棒性的同时，所提出的方案能够保持整数 PWM 调制比，在每个控制步骤中调整定子磁通角和开关周期，从而始终满足同步性条件。通过这种方式，可以改善转矩的极低频谐波频谱，特别是通过减少转矩次谐波。次谐波是与大功率驱动器典型的低频调制相关的主要问题之一，减少次谐波可以避免共振和机械应力等缺点。通过在小型试验台上进行实验测试，对所提出算法的性能进行了评估。
{"title":"Synchronous DTC for torque sub-harmonic reduction in low switching frequency induction motor drives","authors":"A. Benevieri, M. Marchesoni, M. Passalacqua, P. Pozzobon, L. Vaccaro","doi":"10.1016/j.conengprac.2024.106133","DOIUrl":"10.1016/j.conengprac.2024.106133","url":null,"abstract":"<div><div>A direct torque control (DTC) algorithm with synchronous modulation for high-power induction motors is presented in this paper. While maintaining the dynamic response and robustness of a PI-based DTC operating in the stationary reference frame, the proposed scheme is able to keep an integer PWM modulation ratio, adjusting the stator flux angle and the switching period at each control step so that the synchronicity condition is always satisfied. In this way, it is possible to achieve an improvement of the very low-frequency harmonic spectrum of the torque, in particular by reducing torque sub-harmonics. These represent one of the main problems associated with low-frequency modulation typical of high-power drives and their reduction allows to avoid drawbacks such as resonance and mechanical stresses. The performance of the proposed algorithm is evaluated with experimental tests on a small-scale test bench.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106133"},"PeriodicalIF":5.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571577","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}
Pub Date : 2024-10-30DOI: 10.1016/j.conengprac.2024.106141
Catalin Stefan Teodorescu, Andrew West, Barry Lennox
Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator’s authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.
{"title":"Bayesian optimization with embedded stochastic functionality for enhanced robotic obstacle avoidance","authors":"Catalin Stefan Teodorescu, Andrew West, Barry Lennox","doi":"10.1016/j.conengprac.2024.106141","DOIUrl":"10.1016/j.conengprac.2024.106141","url":null,"abstract":"<div><div>Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator’s authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106141"},"PeriodicalIF":5.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553443","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}
Pub Date : 2024-10-30DOI: 10.1016/j.conengprac.2024.106138
Jiahui Hu , Yonghua Lu , Jing Li , Haibo Yang , Jingjing Liu
Gaze is a non-verbal behavior that is an important communication cue and a direct reflection of subjective intent. However, few research works have intervened gaze into the aerial teleoperation circuits of unmanned aerial vehicles (UAVs). This paper proposed an aerial teleoperation framework based on gaze-guidance, mainly built on the novel theory of non-invasive gaze tracking and gaze-drive. We demonstrate how a monocular gaze tracker can acquire human gaze signals and convert them into lupin and efficient control intentions, thus allowing humans to assign tasks to an automated quadrotor without body movements. Extensive and complex simulations and real-world experiments are conducted to verify the superior performance of the proposed method in obstacle traversal.
{"title":"Aerial teleoperation for quadrotors based on gaze-guidance","authors":"Jiahui Hu , Yonghua Lu , Jing Li , Haibo Yang , Jingjing Liu","doi":"10.1016/j.conengprac.2024.106138","DOIUrl":"10.1016/j.conengprac.2024.106138","url":null,"abstract":"<div><div>Gaze is a non-verbal behavior that is an important communication cue and a direct reflection of subjective intent. However, few research works have intervened gaze into the aerial teleoperation circuits of unmanned aerial vehicles (UAVs). This paper proposed an aerial teleoperation framework based on gaze-guidance, mainly built on the novel theory of non-invasive gaze tracking and gaze-drive. We demonstrate how a monocular gaze tracker can acquire human gaze signals and convert them into lupin and efficient control intentions, thus allowing humans to assign tasks to an automated quadrotor without body movements. Extensive and complex simulations and real-world experiments are conducted to verify the superior performance of the proposed method in obstacle traversal.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106138"},"PeriodicalIF":5.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553444","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}
Pub Date : 2024-10-29DOI: 10.1016/j.conengprac.2024.106140
Liujiayi Zhao, Pengyu Song, Chunhui Zhao
Existing root cause diagnosis (RCD) methods infer causal relationships among abnormal variables by decomposing causal graphs into intra-group and inter-group levels, reducing redundancy according to direct causality. However, the indirect causality trigged by wide-range fault propagation may be ignored when inferring within groups, leading to the mismatch between causality distribution and grouping results. To overcome the challenge, we propose a causal similarity learning method with multi-level predictive relation aggregation, which contains a complementary similarity measurement framework covering both single-level and high-level causal relationships. First, an attention mechanism with temporal misalignment is designed, which can convert the undirected correlations of features into directed high-level causal similarity by extracting lagged predictive relations. Further, a graph-cutting penalty term is proposed to promote causality distribution to exhibit intra-group denseness and inter-group sparsity, so that single-level causal similarity can be considered during grouping. Finally, a dual RCD method is proposed to search root causes from the causal graph with intra-group and inter-group causality. In this way, numerous redundant causations caused by complex fault propagation can be succinctly described by inter-group causation, and the search for root cause variables can be limited to subgroups to improve diagnosis efficiency. The validity of the proposed method is illustrated through both the Tennessee Eastman benchmark example and a real industrial process.
{"title":"Causal similarity learning with multi-level predictive relation aggregation for grouped root cause diagnosis of industrial faults","authors":"Liujiayi Zhao, Pengyu Song, Chunhui Zhao","doi":"10.1016/j.conengprac.2024.106140","DOIUrl":"10.1016/j.conengprac.2024.106140","url":null,"abstract":"<div><div>Existing root cause diagnosis (RCD) methods infer causal relationships among abnormal variables by decomposing causal graphs into intra-group and inter-group levels, reducing redundancy according to direct causality. However, the indirect causality trigged by wide-range fault propagation may be ignored when inferring within groups, leading to the mismatch between causality distribution and grouping results. To overcome the challenge, we propose a causal similarity learning method with multi-level predictive relation aggregation, which contains a complementary similarity measurement framework covering both single-level and high-level causal relationships. First, an attention mechanism with temporal misalignment is designed, which can convert the undirected correlations of features into directed high-level causal similarity by extracting lagged predictive relations. Further, a graph-cutting penalty term is proposed to promote causality distribution to exhibit intra-group denseness and inter-group sparsity, so that single-level causal similarity can be considered during grouping. Finally, a dual RCD method is proposed to search root causes from the causal graph with intra-group and inter-group causality. In this way, numerous redundant causations caused by complex fault propagation can be succinctly described by inter-group causation, and the search for root cause variables can be limited to subgroups to improve diagnosis efficiency. The validity of the proposed method is illustrated through both the Tennessee Eastman benchmark example and a real industrial process.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106140"},"PeriodicalIF":5.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539824","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}
Pub Date : 2024-10-29DOI: 10.1016/j.conengprac.2024.106134
Junlin Zhou , Christopher T. Freeman , William Holderbaum
Model-based iterative learning control (ILC) algorithms achieve high accuracy but often exhibit poor robustness to model uncertainty, causing divergence and long-term instability as the number of trials increases. To address this, an estimation-based multiple-model switched ILC (EMMILC) approach is developed based on novel theorem results which guarantee stability if the true plant lies within a uncertainty space defined by the designer. Using gap metric analysis, EMMILC eliminates restrictive assumptions on the uncertainty structure assumed in existing multiple-model ILC methods. Our design framework minimises computational load while maximising tracking accuracy. Applied to a common rehabilitation scenario, EMMILC outperforms the standard ILC approaches that have been previously employed in this setting. This is confirmed by experimental tests with four participants where performance increased by 28%. EMMILC is the first model-based ILC framework that can guarantee high performance while not requiring any model identification or tuning, and paves the way for effective, home-based rehabilitation systems.
{"title":"Multiple-model iterative learning control with application to stroke rehabilitation","authors":"Junlin Zhou , Christopher T. Freeman , William Holderbaum","doi":"10.1016/j.conengprac.2024.106134","DOIUrl":"10.1016/j.conengprac.2024.106134","url":null,"abstract":"<div><div>Model-based iterative learning control (ILC) algorithms achieve high accuracy but often exhibit poor robustness to model uncertainty, causing divergence and long-term instability as the number of trials increases. To address this, an estimation-based multiple-model switched ILC (EMMILC) approach is developed based on novel theorem results which guarantee stability if the true plant lies within a uncertainty space defined by the designer. Using gap metric analysis, EMMILC eliminates restrictive assumptions on the uncertainty structure assumed in existing multiple-model ILC methods. Our design framework minimises computational load while maximising tracking accuracy. Applied to a common rehabilitation scenario, EMMILC outperforms the standard ILC approaches that have been previously employed in this setting. This is confirmed by experimental tests with four participants where performance increased by 28%. EMMILC is the first model-based ILC framework that can guarantee high performance while not requiring any model identification or tuning, and paves the way for effective, home-based rehabilitation systems.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106134"},"PeriodicalIF":5.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539825","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}
Pub Date : 2024-10-25DOI: 10.1016/j.conengprac.2024.106131
XinYu Zhao, LiMei Wang
The performance of permanent magnet linear synchronous motor in tracking is influenced by payload uncertainty and unknown disturbances. Traditional constant-gain super-twisting control typically use a high control gain exceeding the total disturbances to maintain the stability of the system. However, these controllers may lead to control input oversaturation when disturbances decrease and the control gain is not appropriately chosen. To address this issue, this paper proposes a new Fractional Order Barrier Function Adaptive Super-Twisting (FOBFAST) control strategy. The advantages of FOBFAST include: (1) mitigation of system chattering through the design of the super-twisting algorithm and the fractional-order integral terminal sliding mode manifold; (2) achieving convergence of system error to a predetermined zero-neighborhood without requiring information about the disturbance upper bound; (3) dynamic adjustment of control gain to a smaller value as tracking error converges to the origin. Furthermore, an improved barrier function is proposed to address the issue of large control amplitudes, limiting the maximum allowable control gain and ensuring system stability. Experimental results demonstrate that the proposed control strategy not only enhances position tracking performance but also exhibits superior robustness.
{"title":"Bounded control of PMLSM servo system based on fractional order barrier function adaptive super-twisting approach","authors":"XinYu Zhao, LiMei Wang","doi":"10.1016/j.conengprac.2024.106131","DOIUrl":"10.1016/j.conengprac.2024.106131","url":null,"abstract":"<div><div>The performance of permanent magnet linear synchronous motor in tracking is influenced by payload uncertainty and unknown disturbances. Traditional constant-gain super-twisting control typically use a high control gain exceeding the total disturbances to maintain the stability of the system. However, these controllers may lead to control input oversaturation when disturbances decrease and the control gain is not appropriately chosen. To address this issue, this paper proposes a new Fractional Order Barrier Function Adaptive Super-Twisting (FOBFAST) control strategy. The advantages of FOBFAST include: (1) mitigation of system chattering through the design of the super-twisting algorithm and the fractional-order integral terminal sliding mode manifold; (2) achieving convergence of system error to a predetermined zero-neighborhood without requiring information about the disturbance upper bound; (3) dynamic adjustment of control gain to a smaller value as tracking error converges to the origin. Furthermore, an improved barrier function is proposed to address the issue of large control amplitudes, limiting the maximum allowable control gain and ensuring system stability. Experimental results demonstrate that the proposed control strategy not only enhances position tracking performance but also exhibits superior robustness.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106131"},"PeriodicalIF":5.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532939","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}
Pub Date : 2024-10-25DOI: 10.1016/j.conengprac.2024.106139
Liang Zhou, Zhong-Qi Li, Hui Yang, Chang Tan
A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [0.176 km/h, 0.152 km/h], while the control force and acceleration are within [55.7 kN, 44.8 kN] and [0.564 m/s, 0.496 m/s], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.
{"title":"Integral predictive sliding mode control for high-speed trains: A dynamic linearization and input constraint-based data-driven scheme","authors":"Liang Zhou, Zhong-Qi Li, Hui Yang, Chang Tan","doi":"10.1016/j.conengprac.2024.106139","DOIUrl":"10.1016/j.conengprac.2024.106139","url":null,"abstract":"<div><div>A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [<span><math><mo>−</mo></math></span>0.176 km/h, 0.152 km/h], while the control force and acceleration are within [<span><math><mo>−</mo></math></span>55.7 kN, 44.8 kN] and [<span><math><mo>−</mo></math></span>0.564 m/s<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, 0.496 m/s<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106139"},"PeriodicalIF":5.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532940","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}
Pub Date : 2024-10-24DOI: 10.1016/j.conengprac.2024.106123
Songhua Liu , Xun Lang , Jiande Wu , Yufeng Zhang , Cong Lei , Hongye Su
Monitoring oscillatory behavior in industrial control systems is essential to ensure process safety and enhance productivity, but unfortunately, current decomposition-based monitoring methods struggle to extract and accurately detect multiple oscillations. This struggle is primarily due to the level of noise susceptibility in current monitoring methods and the intermittent nature of multiple oscillations in industrial control systems. As a potential solution, variational mode decomposition (VMD) shows promising advantages in processing non-stationary industrial signals with a significant amount of interference. Nevertheless, improperly configuring the number of modes in the VMD can lead to mode mixing and degrade the oscillation extraction accuracy. To overcome this challenge, we propose a corrective VMD approach that automatically adjusts the mode number to create a robust and automated framework for monitoring multiple oscillations. Our framework excels in detecting oscillatory behavior and quantifying the number of oscillations, even in the presence of noisy, intermittent, and irregular disturbances. To validate its effectiveness and practicality, we applied the framework to both a benchmark industrial dataset and a self-constructed industrial dataset, comparing its performance against state-of-the-art oscillation detection methods. The framework demonstrated superior accuracy, achieving 93.90% in detecting oscillations and 85.37% in quantifying the number of oscillations within the benchmark dataset, with similarly excellent results observed in the self-constructed industrial dataset.
{"title":"Corrective variational mode decomposition to detect multiple oscillations in process control systems","authors":"Songhua Liu , Xun Lang , Jiande Wu , Yufeng Zhang , Cong Lei , Hongye Su","doi":"10.1016/j.conengprac.2024.106123","DOIUrl":"10.1016/j.conengprac.2024.106123","url":null,"abstract":"<div><div>Monitoring oscillatory behavior in industrial control systems is essential to ensure process safety and enhance productivity, but unfortunately, current decomposition-based monitoring methods struggle to extract and accurately detect multiple oscillations. This struggle is primarily due to the level of noise susceptibility in current monitoring methods and the intermittent nature of multiple oscillations in industrial control systems. As a potential solution, variational mode decomposition (VMD) shows promising advantages in processing non-stationary industrial signals with a significant amount of interference. Nevertheless, improperly configuring the number of modes in the VMD can lead to mode mixing and degrade the oscillation extraction accuracy. To overcome this challenge, we propose a corrective VMD approach that automatically adjusts the mode number to create a robust and automated framework for monitoring multiple oscillations. Our framework excels in detecting oscillatory behavior and quantifying the number of oscillations, even in the presence of noisy, intermittent, and irregular disturbances. To validate its effectiveness and practicality, we applied the framework to both a benchmark industrial dataset and a self-constructed industrial dataset, comparing its performance against state-of-the-art oscillation detection methods. The framework demonstrated superior accuracy, achieving 93.90% in detecting oscillations and 85.37% in quantifying the number of oscillations within the benchmark dataset, with similarly excellent results observed in the self-constructed industrial dataset.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106123"},"PeriodicalIF":5.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532937","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}
Pub Date : 2024-10-24DOI: 10.1016/j.conengprac.2024.106130
Aliakbar Davoodi, Ahmad W. Al-Dabbagh
In industrial processes, a large number of alarms displayed on human–machine interface screens may overwhelm human operators. This prevents them from taking appropriate corrective actions in a timely manner. Therefore, this paper proposes a three-stage computational procedure for grouping and visualizing correlated alarms, such that root-cause abnormalities can be more easily identified by the human operators. In the first stage, using a word embedding-based approach, alarm tags are transformed into real-valued vectors, where time stamps of the alarms are used rather than their order of occurrence. In the second stage, a multi-level density-based clustering approach is utilized to group correlated alarms hierarchically. In the third stage, a hierarchical visualization approach is developed to display alarm groups to human operators, which depicts hierarchical and statistical information. The implementation and effectiveness of the three-stage computational procedure are demonstrated using an alarm dataset generated for the benchmark Tennessee Eastman process system.
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