Pub Date : 2024-11-30DOI: 10.1016/j.conengprac.2024.106181
Liang Xu , Li Xie , Le Sun , Yuqing Cao
The variational autoencoder (VAE) has garnered extensive attention in the field of soft sensor modeling due to its superior capabilities in probabilistic data description and feature extraction. However, a single-layer VAE is challenging to extract higher-level features in the face of strong nonlinear process data. This paper proposes a gated stacked target-supervised VAE with variable weights (W-GSTVAE) to improve the modeling prediction performance of VAE. First, a stacked VAE is employed to enhance the feature extraction capability. In the pretraining phase, to enhance the correlation between the features and the target variable, feature learning is guided by incorporating the prediction error of target values into the loss function as well as calculating the maximum information coefficient between input and target variables. Meanwhile, in the fine-tuning phase, to make full use of shallow features, gated linear units are used to integrate the output features of each layer, fully exploiting the information from all layers. Finally, the effectiveness and superiority of the proposed model is demonstrated through two real industrial cases.
{"title":"A novel soft sensor modeling method based on gated stacked target-supervised VAE with variable weights","authors":"Liang Xu , Li Xie , Le Sun , Yuqing Cao","doi":"10.1016/j.conengprac.2024.106181","DOIUrl":"10.1016/j.conengprac.2024.106181","url":null,"abstract":"<div><div>The variational autoencoder (VAE) has garnered extensive attention in the field of soft sensor modeling due to its superior capabilities in probabilistic data description and feature extraction. However, a single-layer VAE is challenging to extract higher-level features in the face of strong nonlinear process data. This paper proposes a gated stacked target-supervised VAE with variable weights (W-GSTVAE) to improve the modeling prediction performance of VAE. First, a stacked VAE is employed to enhance the feature extraction capability. In the pretraining phase, to enhance the correlation between the features and the target variable, feature learning is guided by incorporating the prediction error of target values into the loss function as well as calculating the maximum information coefficient between input and target variables. Meanwhile, in the fine-tuning phase, to make full use of shallow features, gated linear units are used to integrate the output features of each layer, fully exploiting the information from all layers. Finally, the effectiveness and superiority of the proposed model is demonstrated through two real industrial cases.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106181"},"PeriodicalIF":5.4,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746064","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 : 2024-11-28DOI: 10.1016/j.conengprac.2024.106170
Qiankun Sun , Weifeng Liu , Lei Cai
In response to the issues of mutual interference among multiple targets, dynamic changes in coverage area, and the difficulty in accurately estimating the optimal coverage positions during the coverage and tracking control process, this article proposes a multi-dynamic target coverage tracking control strategy based on Multiple Unmanned Aerial Vehicle (Multi-UAV) cooperation. To address the problem of estimating the number and location of optimal coverage points for multi-dynamic targets, we propose a dynamic estimation strategy of optimal coverage points for multi-dynamic targets; for the under-coverage problem, we propose an optimal coverage point estimation method for under-coverage conditions in order to improve the cost-effectiveness of coverage. Subsequently, a multi-UAV scheduling strategy based on the coverage cleanup cost is proposed, which assigns appropriate UAVs to perform coverage cleanup tasks for different coverage points. Addressing the performance constraints, path smoothness, collision avoidance, and path tracking problems of UAVs, we introduce B-spline-based multi-UAV path planning and line-of-sight-based guidance methods to achieve multi-UAV coverage tracking control for multi-dynamic targets. Finally, the proposed method is applied to an ocean oil spill coverage cleanup task. Both simulation and emulation results validate the theoretical findings and demonstrate the effectiveness of the proposed method for such applications.
{"title":"Multi-dynamic target coverage tracking control strategy based on multi-UAV collaboration","authors":"Qiankun Sun , Weifeng Liu , Lei Cai","doi":"10.1016/j.conengprac.2024.106170","DOIUrl":"10.1016/j.conengprac.2024.106170","url":null,"abstract":"<div><div>In response to the issues of mutual interference among multiple targets, dynamic changes in coverage area, and the difficulty in accurately estimating the optimal coverage positions during the coverage and tracking control process, this article proposes a multi-dynamic target coverage tracking control strategy based on Multiple Unmanned Aerial Vehicle (Multi-UAV) cooperation. To address the problem of estimating the number and location of optimal coverage points for multi-dynamic targets, we propose a dynamic estimation strategy of optimal coverage points for multi-dynamic targets; for the under-coverage problem, we propose an optimal coverage point estimation method for under-coverage conditions in order to improve the cost-effectiveness of coverage. Subsequently, a multi-UAV scheduling strategy based on the coverage cleanup cost is proposed, which assigns appropriate UAVs to perform coverage cleanup tasks for different coverage points. Addressing the performance constraints, path smoothness, collision avoidance, and path tracking problems of UAVs, we introduce B-spline-based multi-UAV path planning and line-of-sight-based guidance methods to achieve multi-UAV coverage tracking control for multi-dynamic targets. Finally, the proposed method is applied to an ocean oil spill coverage cleanup task. Both simulation and emulation results validate the theoretical findings and demonstrate the effectiveness of the proposed method for such applications.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106170"},"PeriodicalIF":5.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746154","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 : 2024-11-28DOI: 10.1016/j.conengprac.2024.106175
Philippe Schuchert, Alireza Karimi
Next-generation motion control systems require fast and precise control. However, advanced control strategies often rely on complex and costly system models. Data-driven methods have been proposed to design high-performance controllers without requiring a parametric model of the system. In particular, methods using frequency response functions (FRFs) have been widely applied to mechatronic systems due to their good performance, and the industry’s familiarity with obtaining FRFs. This paper applies a recently developed method to design a controller for an industrial robotic arm with three translational degrees of freedom, using only the FRF of the robot around different operating points. Focused on motion control, the objective is to attain the desired reference tracking performance through the design of a linear-parameter-varying (LPV) two-degree-of-freedom (2DoF) controller. Performance is further improved by tuning an additional filter to compensate for inaccuracies in the transmission.
{"title":"High-precision control of a robotic arm using frequency-based data-driven methods","authors":"Philippe Schuchert, Alireza Karimi","doi":"10.1016/j.conengprac.2024.106175","DOIUrl":"10.1016/j.conengprac.2024.106175","url":null,"abstract":"<div><div>Next-generation motion control systems require fast and precise control. However, advanced control strategies often rely on complex and costly system models. Data-driven methods have been proposed to design high-performance controllers without requiring a parametric model of the system. In particular, methods using frequency response functions (FRFs) have been widely applied to mechatronic systems due to their good performance, and the industry’s familiarity with obtaining FRFs. This paper applies a recently developed method to design a controller for an industrial robotic arm with three translational degrees of freedom, using only the FRF of the robot around different operating points. Focused on motion control, the objective is to attain the desired reference tracking performance through the design of a linear-parameter-varying (LPV) two-degree-of-freedom (2DoF) controller. Performance is further improved by tuning an additional filter to compensate for inaccuracies in the transmission.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106175"},"PeriodicalIF":5.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746063","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 : 2024-11-26DOI: 10.1016/j.conengprac.2024.106174
KangKang Xu , Hao Bao , Xi Jin , XianBing Meng , Zhan Li , XiaoLiang Zhao , LuoKe Hu
Distributed parameter systems (DPSs) widely exist in industrial thermal processes. Modeling of such processes is challenging for the following reasons: (1) nonlinear spatiotemporal coupling dynamics, (2) model uncertainty, and (3) time-varying dynamics. To address these problems, an adaptive-node broad learning (AN-BL) based incremental spatiotemporal model is developed for nonlinear time-varying DPSs. First, incremental kernel Karhunen–Loève (IK-KL) decouples nonlinear spatio-temporal coupling dynamics and derives adaptive spatial basis functions to represent the nonlinear time-varying dynamics in the spatial domain. The application of kernel method can better deal with nonlinear spatio-temporal characteristics. Second, a broad learning (BL) based on pruning strategy was developed to estimate the unknown time-varying dynamics in the time domain. The adaptive pruning strategy greatly reduced the redundancy of the network structure and reduce computational burden. The proposed online modeling scheme can adaptively adjust the model structure and parameters under streaming data environments, which makes it promising for dealing with time-varying DPSs.
{"title":"An adaptive-node broad learning based incremental model for time-varying nonlinear distributed thermal processes","authors":"KangKang Xu , Hao Bao , Xi Jin , XianBing Meng , Zhan Li , XiaoLiang Zhao , LuoKe Hu","doi":"10.1016/j.conengprac.2024.106174","DOIUrl":"10.1016/j.conengprac.2024.106174","url":null,"abstract":"<div><div>Distributed parameter systems (DPSs) widely exist in industrial thermal processes. Modeling of such processes is challenging for the following reasons: (1) nonlinear spatiotemporal coupling dynamics, (2) model uncertainty, and (3) time-varying dynamics. To address these problems, an adaptive-node broad learning (AN-BL) based incremental spatiotemporal model is developed for nonlinear time-varying DPSs. First, incremental kernel Karhunen–Loève (IK-KL) decouples nonlinear spatio-temporal coupling dynamics and derives adaptive spatial basis functions to represent the nonlinear time-varying dynamics in the spatial domain. The application of kernel method can better deal with nonlinear spatio-temporal characteristics. Second, a broad learning (BL) based on pruning strategy was developed to estimate the unknown time-varying dynamics in the time domain. The adaptive pruning strategy greatly reduced the redundancy of the network structure and reduce computational burden. The proposed online modeling scheme can adaptively adjust the model structure and parameters under streaming data environments, which makes it promising for dealing with time-varying DPSs.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106174"},"PeriodicalIF":5.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704060","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 : 2024-11-26DOI: 10.1016/j.conengprac.2024.106161
Julin Hu , Hongwen He , Zexing Wang , Shuang Ji , Zhihui Duan
In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 . It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.
{"title":"A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle","authors":"Julin Hu , Hongwen He , Zexing Wang , Shuang Ji , Zhihui Duan","doi":"10.1016/j.conengprac.2024.106161","DOIUrl":"10.1016/j.conengprac.2024.106161","url":null,"abstract":"<div><div>In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 <span><math><mrow><mi>m</mi><mo>/</mo><msup><mrow><mi>s</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106161"},"PeriodicalIF":5.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703358","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 : 2024-11-25DOI: 10.1016/j.conengprac.2024.106173
Po Li , Changxing Liu , Xiaoshan Tong , Xiang Li , Gaofeng Zheng
Voltage ripple is introduced to the DC link when a single-phase rectifier operates, which affects the energy balance of both the DC and AC sides. Accurate acquisition and fast, precise tracking of the decoupling capacitor voltage and decoupling inductor current reference values are challenges in the design of active power decoupling controllers. This article employs instantaneous ripple power control, shifting the focus from the accuracy of the decoupling capacitor voltage and decoupling inductor current reference values to the accuracy of the ripple power reference value. A ripple separation-based active power decoupling control strategy is proposed. By designing a time-varying observer, the amplitude feedback signal of the output voltage’s second-harmonic ripple is extracted in real time to generate the ripple power reference, enhancing its accuracy and reliability. The endpoint equivalent modulation method is adopted to track the instantaneous ripple power. Compared with traditional finite control set model predictive control, it achieves better tracking performance at the same control frequency, with a fixed switching frequency. Additionally, measures are proposed to address input current distortion caused by output voltage ripple entering the rectifier’s grid-side current control loop. This avoids the pollution of the input current by the output ripple voltage. Simulations and experimentations are performed to test the proposed control strategy.
{"title":"Improved direct ripple power predictive control of single-phase rectifier based on ripple separation","authors":"Po Li , Changxing Liu , Xiaoshan Tong , Xiang Li , Gaofeng Zheng","doi":"10.1016/j.conengprac.2024.106173","DOIUrl":"10.1016/j.conengprac.2024.106173","url":null,"abstract":"<div><div>Voltage ripple is introduced to the DC link when a single-phase rectifier operates, which affects the energy balance of both the DC and AC sides. Accurate acquisition and fast, precise tracking of the decoupling capacitor voltage and decoupling inductor current reference values are challenges in the design of active power decoupling controllers. This article employs instantaneous ripple power control, shifting the focus from the accuracy of the decoupling capacitor voltage and decoupling inductor current reference values to the accuracy of the ripple power reference value. A ripple separation-based active power decoupling control strategy is proposed. By designing a time-varying observer, the amplitude feedback signal of the output voltage’s second-harmonic ripple is extracted in real time to generate the ripple power reference, enhancing its accuracy and reliability. The endpoint equivalent modulation method is adopted to track the instantaneous ripple power. Compared with traditional finite control set model predictive control, it achieves better tracking performance at the same control frequency, with a fixed switching frequency. Additionally, measures are proposed to address input current distortion caused by output voltage ripple entering the rectifier’s grid-side current control loop. This avoids the pollution of the input current by the output ripple voltage. Simulations and experimentations are performed to test the proposed control strategy.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106173"},"PeriodicalIF":5.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704061","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 : 2024-11-25DOI: 10.1016/j.conengprac.2024.106176
Kai Zhang , Xiaowen Zhang , Kaixiang Peng
In complex industrial processes, process operators often intervene in automatic control systems based on their assessment of the operating state. Traditional operating state evaluation methods do not take into consideration or cannot effectively use this intervention information, and thus may incorrectly evaluate the operating state. In this paper, a convolutional-neural network-conditional variational auto-encoder (CNN-CVAE)-based method for evaluating the operating state is proposed to address this problem. First, the operating-state labels are constructed considering the operator-intervention information. Next, the features of operator-intervention variables (OIVs) are extracted based on CNN, and the obtained probabilities of belonging to different operating states are used as conditional probabilities of CVAE to supervise the feature extraction from the ordinary process data. Finally, both features are fused in a fully connected layer to obtain the predicted operating state. Compared with traditional methods, CNN-CVAE can capture features from both OIVs and process data for evaluating the operating state. The proposed method is validated in a real, hot strip rolling mill process. The results show that the proposed method improves the evaluation accuracy by 54.72% compared with five methods that do not fully use the OIVs.
{"title":"Evaluating the process operating state taking into consideration operator interventions with application to a hot rolling mill process","authors":"Kai Zhang , Xiaowen Zhang , Kaixiang Peng","doi":"10.1016/j.conengprac.2024.106176","DOIUrl":"10.1016/j.conengprac.2024.106176","url":null,"abstract":"<div><div>In complex industrial processes, process operators often intervene in automatic control systems based on their assessment of the operating state. Traditional operating state evaluation methods do not take into consideration or cannot effectively use this intervention information, and thus may incorrectly evaluate the operating state. In this paper, a convolutional-neural network-conditional variational auto-encoder (CNN-CVAE)-based method for evaluating the operating state is proposed to address this problem. First, the operating-state labels are constructed considering the operator-intervention information. Next, the features of operator-intervention variables (OIVs) are extracted based on CNN, and the obtained probabilities of belonging to different operating states are used as conditional probabilities of CVAE to supervise the feature extraction from the ordinary process data. Finally, both features are fused in a fully connected layer to obtain the predicted operating state. Compared with traditional methods, CNN-CVAE can capture features from both OIVs and process data for evaluating the operating state. The proposed method is validated in a real, hot strip rolling mill process. The results show that the proposed method improves the evaluation accuracy by 54.72% compared with five methods that do not fully use the OIVs.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106176"},"PeriodicalIF":5.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703359","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 : 2024-11-22DOI: 10.1016/j.conengprac.2024.106178
Lin Liu , Heng Dong , Xiaobing Xu , Zanxian Tan , Jiayuan Geng , Baiyang Liu
The model-free sliding mode control with sliding mode disturbance observer (SMDO) for interior permanent magnet synchronous motor (IPMSM) is affected by feedback delays caused by mismatch of motor parameters. The observer is lagged behind the change of external disturbances, the speed tracking accuracy and transient control performance for IPMSM drives are reduced. In order to solve the issues, an improved higher-order sliding mode disturbance observer-based model-free finite-time terminal sliding mode control (HOSMDO-MFFTTSMC) strategy is proposed in this paper. First, a finite-time terminal sliding mode surface (FTTSMS) is designed, and a rotation speed-loop-based MFFTTSMC strategy is designed by combining the ultra-local model. The system control state is converged in finite time and the accurate tracking of observation error is realized. In addition, the non-singular fast terminal sliding mode is introduced into the observer, the higher-order SMDO is designed. The unknown part of disturbances is observed and compensated in real time, the fast-tracking response capability and anti-disturbance capability for IPMSM are improved, and the stator current harmonics are effectively suppressed. Finally, the proposed HOSMDO-MFFTTSMC strategy is experimentally demonstrated with a 6.6kW motor. The correctness and effectiveness of the HOSMDO-MFFTTSMC strategy are verified by simulation and experimental results.
{"title":"Improved sliding mode disturbance observer-based model-free finite-time terminal sliding mode control for IPMSM speed ripple minimization","authors":"Lin Liu , Heng Dong , Xiaobing Xu , Zanxian Tan , Jiayuan Geng , Baiyang Liu","doi":"10.1016/j.conengprac.2024.106178","DOIUrl":"10.1016/j.conengprac.2024.106178","url":null,"abstract":"<div><div>The model-free sliding mode control with sliding mode disturbance observer (SMDO) for interior permanent magnet synchronous motor (IPMSM) is affected by feedback delays caused by mismatch of motor parameters. The observer is lagged behind the change of external disturbances, the speed tracking accuracy and transient control performance for IPMSM drives are reduced. In order to solve the issues, an improved higher-order sliding mode disturbance observer-based model-free finite-time terminal sliding mode control (HOSMDO-MFFTTSMC) strategy is proposed in this paper. First, a finite-time terminal sliding mode surface (FTTSMS) is designed, and a rotation speed-loop-based MFFTTSMC strategy is designed by combining the ultra-local model. The system control state is converged in finite time and the accurate tracking of observation error is realized. In addition, the non-singular fast terminal sliding mode is introduced into the observer, the higher-order SMDO is designed. The unknown part of disturbances is observed and compensated in real time, the fast-tracking response capability and anti-disturbance capability for IPMSM are improved, and the stator current harmonics are effectively suppressed. Finally, the proposed HOSMDO-MFFTTSMC strategy is experimentally demonstrated with a 6.6kW motor. The correctness and effectiveness of the HOSMDO-MFFTTSMC strategy are verified by simulation and experimental results.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106178"},"PeriodicalIF":5.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704062","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 : 2024-11-22DOI: 10.1016/j.conengprac.2024.106172
Muhammad Saadullah , Zhipeng Zhang , Hao Hu
Urban transportation systems, characterized by multiple modes and complex dynamics, present significant challenges for the efficient management and optimization of traffic. Addressing these challenges, this study utilizes the Macroscopic Fundamental Diagram to develop and implement Model Predictive Control (MPC) strategies aimed at optimizing traffic flow across multiple urban reservoirs. By designing optimal controllers that regulate the transfer flow of trucks and passenger vehicles, this study aims to maintain vehicle accumulation at a critical level. For this purpose, Centralized Model Predictive Control (C-MPC) and Decentralized Model Predictive Control (DC-MPC) approaches have been formulated to maximize the accumulation of passenger vehicles while reducing the number of trucks in the reservoir system. The findings reveal that the unified approach of C-MPC effectively reduces truck traffic but results in a higher change in passenger travel time. The outcome for segmented C-MPC shows a slower rate of change in vehicle accumulation. While DC-MPC offers a better balance and keeps accumulation for both trucks and passenger vehicles within predefined limits. It contributes to the theoretical understanding of traffic flow optimization and practical insights for city planners and engineers seeking to implement advanced traffic management solutions. Future work can explore the scalability of these controllers and their adaptation to real-time traffic data.
{"title":"Optimizing multi-modal urban traffic flow: Utilizing macroscopic fundamental diagram and Model Predictive Control","authors":"Muhammad Saadullah , Zhipeng Zhang , Hao Hu","doi":"10.1016/j.conengprac.2024.106172","DOIUrl":"10.1016/j.conengprac.2024.106172","url":null,"abstract":"<div><div>Urban transportation systems, characterized by multiple modes and complex dynamics, present significant challenges for the efficient management and optimization of traffic. Addressing these challenges, this study utilizes the Macroscopic Fundamental Diagram to develop and implement Model Predictive Control (MPC) strategies aimed at optimizing traffic flow across multiple urban reservoirs. By designing optimal controllers that regulate the transfer flow of trucks and passenger vehicles, this study aims to maintain vehicle accumulation at a critical level. For this purpose, Centralized Model Predictive Control (C-MPC) and Decentralized Model Predictive Control (DC-MPC) approaches have been formulated to maximize the accumulation of passenger vehicles while reducing the number of trucks in the reservoir system. The findings reveal that the unified approach of C-MPC effectively reduces truck traffic but results in a higher change in passenger travel time. The outcome for segmented C-MPC shows a slower rate of change in vehicle accumulation. While DC-MPC offers a better balance and keeps accumulation for both trucks and passenger vehicles within predefined limits. It contributes to the theoretical understanding of traffic flow optimization and practical insights for city planners and engineers seeking to implement advanced traffic management solutions. Future work can explore the scalability of these controllers and their adaptation to real-time traffic data.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"155 ","pages":"Article 106172"},"PeriodicalIF":5.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704063","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}
This paper is concerned with sensor fault diagnosis problems for multi-axis carving machine systems (MACMSs) with repetitive machining tasks. A novel fault diagnosis method that combines the multi-feature fusion technology and Gaussian mixture hidden Markov models (GMHMMs) is proposed, which is inspired by a data- and model-driven collaborative perspective. With fault-sensitive features first extracted from both the time domain and time–frequency domain, the composite health index (CHI) is constructed to facilitate the understanding of the time-varying evolution. Then, GMHMMs are established to characterize the probabilistic relationship between the hidden states and CHI. To achieve high-precision fault classification, a well-designed global objective function is adopted to dynamically optimize both the CHI construction and classifier model training in a closed-loop feedback mechanism. Specifically, the fusion coefficients with range and equality constraints are integrated as part of the model parameters into the global optimization objective function, thereby reducing the search range and improving convergence speed. Besides, the well-trained GMHMMs interact with each other to capture the correlation information between different faults, and are utilized for online fault diagnosis. Finally, experiments are conducted on a self-developed multi-axis carving machine platform. The results exhibit outstanding performance in comparison with existing methods, particularly attaining a diagnostic accuracy of 95.37%.
本文主要研究具有重复性加工任务的多轴雕刻机系统（MACMS）的传感器故障诊断问题。本文从数据和模型驱动的协作视角出发，提出了一种结合多特征融合技术和高斯混合隐马尔可夫模型（GMHMMs）的新型故障诊断方法。首先从时域和时频域提取故障敏感特征，构建综合健康指数 (CHI)，以便于理解时变演化。然后，建立 GMHMMs 来描述隐藏状态与 CHI 之间的概率关系。为实现高精度故障分类，采用了精心设计的全局目标函数，在闭环反馈机制中动态优化 CHI 构建和分类器模型训练。具体来说，具有范围和相等约束的融合系数作为模型参数的一部分被集成到全局优化目标函数中，从而缩小了搜索范围，提高了收敛速度。此外，训练有素的 GMHMM 相互影响，捕捉不同故障之间的相关信息，并用于在线故障诊断。最后，在自主研发的多轴雕刻机平台上进行了实验。实验结果表明，与现有方法相比，该方法性能卓越，尤其是诊断准确率达到了 95.37%。
{"title":"Fault diagnosis for multi-axis carving machine systems with Gaussian mixture hidden Markov models: A data-model interactive perspective","authors":"Xiang Qiu , Wei Chen , Qi Wu , Yao-Wei Wang , Caoyuan Gu , Wen-An Zhang","doi":"10.1016/j.conengprac.2024.106163","DOIUrl":"10.1016/j.conengprac.2024.106163","url":null,"abstract":"<div><div>This paper is concerned with sensor fault diagnosis problems for multi-axis carving machine systems (MACMSs) with repetitive machining tasks. A novel fault diagnosis method that combines the multi-feature fusion technology and Gaussian mixture hidden Markov models (GMHMMs) is proposed, which is inspired by a data- and model-driven collaborative perspective. With fault-sensitive features first extracted from both the time domain and time–frequency domain, the composite health index (CHI) is constructed to facilitate the understanding of the time-varying evolution. Then, GMHMMs are established to characterize the probabilistic relationship between the hidden states and CHI. To achieve high-precision fault classification, a well-designed global objective function is adopted to dynamically optimize both the CHI construction and classifier model training in a closed-loop feedback mechanism. Specifically, the fusion coefficients with range and equality constraints are integrated as part of the model parameters into the global optimization objective function, thereby reducing the search range and improving convergence speed. Besides, the well-trained GMHMMs interact with each other to capture the correlation information between different faults, and are utilized for online fault diagnosis. Finally, experiments are conducted on a self-developed multi-axis carving machine platform. The results exhibit outstanding performance in comparison with existing methods, particularly attaining a diagnostic accuracy of 95.37%.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106163"},"PeriodicalIF":5.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700409","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}
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