Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166648
Hao Nie, Jinna Li
Optimal control design methods for multiple time-scale systems are a hot research topic in recent years. In this paper, a comprehensive overview of the design methods for optimal control of multiple time-scale systems is presented. Firstly, the mathematical model of the optimal control problem of multiple time-scale systems is given, and the key difficulties of the related research are analysed. Secondly, the design methods for optimal control of multiple time-scale systems based on the model and reinforcement learning (RL) methods are given respectively. Thirdly, the performance analysis and practical application of the multi-time scale system are analyzed. Finally, the current problems in solving the optimization of multiple time-scale systems are analysed, and the research directions of optimal control of multiple time-scale systems are prospected.
{"title":"An Overview of Optimal Control Methods for Singularly Perturbed Systems","authors":"Hao Nie, Jinna Li","doi":"10.1109/DDCLS58216.2023.10166648","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166648","url":null,"abstract":"Optimal control design methods for multiple time-scale systems are a hot research topic in recent years. In this paper, a comprehensive overview of the design methods for optimal control of multiple time-scale systems is presented. Firstly, the mathematical model of the optimal control problem of multiple time-scale systems is given, and the key difficulties of the related research are analysed. Secondly, the design methods for optimal control of multiple time-scale systems based on the model and reinforcement learning (RL) methods are given respectively. Thirdly, the performance analysis and practical application of the multi-time scale system are analyzed. Finally, the current problems in solving the optimization of multiple time-scale systems are analysed, and the research directions of optimal control of multiple time-scale systems are prospected.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133053999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167235
Y. Gao, Huaiping Jin, Bin Wang, Biao Yang, Wangyang Yu
In recent years, deep learning techniques have been widely applied in soft sensor modeling. Stacked autoencoder (SAE) networks are particularly effective at discovering complex data patterns due to their hierarchical structures. However, process data are typically generated as data streams, which poses a great challenge to capture the time-varying characteristics of the process for traditional soft sensor models based on SAE. Furthermore, the insufficiency of offline pre-training data further limits the feature representation capability of SAE. To address these problems, an online deep evolving fuzzy system (ODEFS) based adaptive soft sensor method for process data streams is proposed. In the offline modeling phase, quality-related stacked autoencoder (QSAE) is pre-trained as representation layer to mine quality-related feature representations, while an evolving fuzzy system with self-organization capability is built as the prediction layer. In the online implementation phase, the topology-preserving loss is added to the learning process of QSAE feature network to enable continuous learning of feature representations and alleviate the catastrophic forgetting problem. Meanwhile, the shallow EFS network handles concept drift in data patterns by self-adjusting the structure and parameters. The proposed ODEFS method can improve the feature representation capability of SAE in a data streaming environment and the ability to handle time-varying characteristics, thus ensuring better prediction accuracy. The effectiveness and superiority of the proposed method are verified on TE process.
{"title":"An Adaptive Soft Sensor Method based on Online Deep Evolving Fuzzy System for Industrial Process Data Streams","authors":"Y. Gao, Huaiping Jin, Bin Wang, Biao Yang, Wangyang Yu","doi":"10.1109/DDCLS58216.2023.10167235","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167235","url":null,"abstract":"In recent years, deep learning techniques have been widely applied in soft sensor modeling. Stacked autoencoder (SAE) networks are particularly effective at discovering complex data patterns due to their hierarchical structures. However, process data are typically generated as data streams, which poses a great challenge to capture the time-varying characteristics of the process for traditional soft sensor models based on SAE. Furthermore, the insufficiency of offline pre-training data further limits the feature representation capability of SAE. To address these problems, an online deep evolving fuzzy system (ODEFS) based adaptive soft sensor method for process data streams is proposed. In the offline modeling phase, quality-related stacked autoencoder (QSAE) is pre-trained as representation layer to mine quality-related feature representations, while an evolving fuzzy system with self-organization capability is built as the prediction layer. In the online implementation phase, the topology-preserving loss is added to the learning process of QSAE feature network to enable continuous learning of feature representations and alleviate the catastrophic forgetting problem. Meanwhile, the shallow EFS network handles concept drift in data patterns by self-adjusting the structure and parameters. The proposed ODEFS method can improve the feature representation capability of SAE in a data streaming environment and the ability to handle time-varying characteristics, thus ensuring better prediction accuracy. The effectiveness and superiority of the proposed method are verified on TE process.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167076
Chengwu Jin, Yong Yang, Xia Liu, Xiaoyu Shi
This paper focuses on the adaptive funnel control of a flexible exoskeleton joint based on the singular perturbation method. The singular perturbation is used to find the asymptotic solution of a differential equation by decomposing the system into two subsystems. For the fast subsystem, a torque-feedback-based subcontroller is proposed to ensure the suppression of flexible vibration. For the remaining slow subsystem, an improved funnel error transformation is introduced and integrated into the controller design to achieve a specified tracking error performance. Fuzzy logic systems are employed to deal with the nonlinear uncertainties, and an adaptive fuzzy funnel controller is constructed by backstepping method. The simulation results verify the feasibility of the proposed control scheme.
{"title":"Robust Fuzzy Adaptive Funnel Control of Flexible Exoskeleton Joints Based on Singular Perturbation Method","authors":"Chengwu Jin, Yong Yang, Xia Liu, Xiaoyu Shi","doi":"10.1109/DDCLS58216.2023.10167076","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167076","url":null,"abstract":"This paper focuses on the adaptive funnel control of a flexible exoskeleton joint based on the singular perturbation method. The singular perturbation is used to find the asymptotic solution of a differential equation by decomposing the system into two subsystems. For the fast subsystem, a torque-feedback-based subcontroller is proposed to ensure the suppression of flexible vibration. For the remaining slow subsystem, an improved funnel error transformation is introduced and integrated into the controller design to achieve a specified tracking error performance. Fuzzy logic systems are employed to deal with the nonlinear uncertainties, and an adaptive fuzzy funnel controller is constructed by backstepping method. The simulation results verify the feasibility of the proposed control scheme.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114230602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work investigates the problem of random successive data dropout at the output side of stochastic linear systems and presents a novel successive updating scheme (SUS) based on iterative learning control (ILC) to avoid control failures due to data loss. In particular, the successively lost output data in the latest iteration is compensated via predictive information estimated successfully with the same time instant label in the previous iteration by the multi-step predictive model. Mathematical induction is used to demonstrate the convergence of the proposed ILC scheme. Lastly, a simulation example is provided to back up the theoretical analysis.
{"title":"A Novel Successive Updating Scheme of Iterative Learning Control for Networked Control System with Output Data Dropouts","authors":"Zhiyang Zhang, Zhenxuan Li, Shuang Guo, Chenkun Yin","doi":"10.1109/DDCLS58216.2023.10167132","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167132","url":null,"abstract":"This work investigates the problem of random successive data dropout at the output side of stochastic linear systems and presents a novel successive updating scheme (SUS) based on iterative learning control (ILC) to avoid control failures due to data loss. In particular, the successively lost output data in the latest iteration is compensated via predictive information estimated successfully with the same time instant label in the previous iteration by the multi-step predictive model. Mathematical induction is used to demonstrate the convergence of the proposed ILC scheme. Lastly, a simulation example is provided to back up the theoretical analysis.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114476253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166698
Qi Zhang, Shenquan Wang, Wenchengyu Ji
In this paper, a fault detection (FD) method with moving window subspace identification method (MW-SIM) is proposed for the problem of difficult detection of incipient faults. Since the size of the window length has a direct relationship with the fault detection rate, the optimal window length is found by the sparrow search algorithm (SSA) to obtain the maximum fault detection rate. Furthermore, applying event-triggered strategy to subspace identification algorithms can effectively reduce data transmission. Finally, the effectiveness of the designed strategy is verified by the Tennessee Eastman (TE) process simulation.
{"title":"Event-triggered-based subspace identification fault detection with an optimized moving window","authors":"Qi Zhang, Shenquan Wang, Wenchengyu Ji","doi":"10.1109/DDCLS58216.2023.10166698","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166698","url":null,"abstract":"In this paper, a fault detection (FD) method with moving window subspace identification method (MW-SIM) is proposed for the problem of difficult detection of incipient faults. Since the size of the window length has a direct relationship with the fault detection rate, the optimal window length is found by the sparrow search algorithm (SSA) to obtain the maximum fault detection rate. Furthermore, applying event-triggered strategy to subspace identification algorithms can effectively reduce data transmission. Finally, the effectiveness of the designed strategy is verified by the Tennessee Eastman (TE) process simulation.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117157737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166958
Minghao Jiang, Dong-dong Zheng
In this paper, a novel adaptive impedance control strategy for the flexible joint robot (FJR) is proposed. To simplify the controller design process, the singular perturbation technique is used to decompose the original high-order system into low-order subsystems. To reduce the mismatch of the system model, the neural network is used to estimate the friction and unknown system dynamic, where an improved optimal bounded ellipsoid (IOBE) algorithm is adopted to optimize the weight matrix of the neural network, which can fix the learning gain matrix vanishing or unbounded growth in traditional OBE algorithm. Different from traditional impedance controllers with fixed impedance parameters, in this paper, the variable stiffness and damping coefficients are used, which can maintain a fast response speed when the FJR is moving freely and can show more compliance characteristics when the FJR is interacting with the environment. The stability of the closed-loop system is proved via the Lyapunov approach and the effectiveness of the algorithm is verified by simulations.
{"title":"Neural network-based variable impedance control of flexible joint robots","authors":"Minghao Jiang, Dong-dong Zheng","doi":"10.1109/DDCLS58216.2023.10166958","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166958","url":null,"abstract":"In this paper, a novel adaptive impedance control strategy for the flexible joint robot (FJR) is proposed. To simplify the controller design process, the singular perturbation technique is used to decompose the original high-order system into low-order subsystems. To reduce the mismatch of the system model, the neural network is used to estimate the friction and unknown system dynamic, where an improved optimal bounded ellipsoid (IOBE) algorithm is adopted to optimize the weight matrix of the neural network, which can fix the learning gain matrix vanishing or unbounded growth in traditional OBE algorithm. Different from traditional impedance controllers with fixed impedance parameters, in this paper, the variable stiffness and damping coefficients are used, which can maintain a fast response speed when the FJR is moving freely and can show more compliance characteristics when the FJR is interacting with the environment. The stability of the closed-loop system is proved via the Lyapunov approach and the effectiveness of the algorithm is verified by simulations.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123401279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166913
Xiaofeng Yuan, Zhenzhen Jia, Lingjian Ye, Kai Wang, Yalin Wang
The industrial processes are commonly characterized by nonlinearities and dynamics. Therefore, long short-term memory (LSTM) networks are often adopted to extract the nonlinear dynamic features for the prediction of industrial quality indicators. However, traditional LSTM only captures the temporal characteristics of input variables but ignores the output variables. Therefore, a multi-model integrated method (MMIM) is proposed for simultaneously extracting the input and output temporal characteristics in this study. In the MMIM, a LSTM and other static models are used to collect the temporal and static characteristics for the inputs, while a RNN is applied to predict the output variable. The effectiveness and performance are verified on an industrial hydrocracking plant for the prediction of light naphtha isopentane and heavy naphtha quality.
{"title":"Industrial Soft Sensor Prediction based on Multi-model Integrated Method","authors":"Xiaofeng Yuan, Zhenzhen Jia, Lingjian Ye, Kai Wang, Yalin Wang","doi":"10.1109/DDCLS58216.2023.10166913","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166913","url":null,"abstract":"The industrial processes are commonly characterized by nonlinearities and dynamics. Therefore, long short-term memory (LSTM) networks are often adopted to extract the nonlinear dynamic features for the prediction of industrial quality indicators. However, traditional LSTM only captures the temporal characteristics of input variables but ignores the output variables. Therefore, a multi-model integrated method (MMIM) is proposed for simultaneously extracting the input and output temporal characteristics in this study. In the MMIM, a LSTM and other static models are used to collect the temporal and static characteristics for the inputs, while a RNN is applied to predict the output variable. The effectiveness and performance are verified on an industrial hydrocracking plant for the prediction of light naphtha isopentane and heavy naphtha quality.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123421130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166520
Honghai Ji, Yuxin Wu, Shida Liu, Li Wang, Lingling Fan, Shuangshuang Xiong
This paper is concerned with distributed state estimation problem over sensor networks with uncertainty in communication networks. Because of the instability of communication in real systems, it is meaningful to consider packet loss and topology change. Thus, based on Kalman consensus filtering algorithm and Data-driven filtering technique, we proposed a modified Data-driven Distributed information-weighted Kalman Consensus Filter to estimate the state. Finally, the effectiveness of the designed algorithm is validated by a simulation example.
{"title":"A Modified Data-driven Distributed Information-Weighted Kalman Consensus Filtering with Switching Topology and Packet Loss","authors":"Honghai Ji, Yuxin Wu, Shida Liu, Li Wang, Lingling Fan, Shuangshuang Xiong","doi":"10.1109/DDCLS58216.2023.10166520","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166520","url":null,"abstract":"This paper is concerned with distributed state estimation problem over sensor networks with uncertainty in communication networks. Because of the instability of communication in real systems, it is meaningful to consider packet loss and topology change. Thus, based on Kalman consensus filtering algorithm and Data-driven filtering technique, we proposed a modified Data-driven Distributed information-weighted Kalman Consensus Filter to estimate the state. Finally, the effectiveness of the designed algorithm is validated by a simulation example.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"177 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123571796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10165958
Liangbin Wang, Renhai Yu, Jin Lv, Bo Zhang, Fuzhi Wang, Fei Teng
The application of shipboard microgrids (SMGs) makes it possible to effectively use renewable new energy on the shipboard platform. As renewable energy sources are connected to SMGs in the form of distributed generators (DGs), the openness of the system increases and so does the risk of exposure to cyber attacks. In this paper, a resilient distributed secondary frequency control strategy for SMGs is constructed to resist false data injection (FDI) attacks. An attacker can tamper with the information in the communication links between the DGs of a SMG to prevent the DGs from outputting stable power, thereby causing oscillations in the entire SMG. To increase resilience to FDI attacks, the proposed resilient control strategy introduces a control network layer interconnected with the original data transmission layer to form a hierarchical communication network. By setting the SMG parameters, the proposed strategy can well reduce the negative effects of FDI attacks on DGs and ensure the stable operation of SMGs. Finally, the simulation results verify the effectiveness of the strategy.
{"title":"Resilient Distributed Secondary Control Strategy for New Energy Shipboard Microgrid Against Bounded FDI Attacks","authors":"Liangbin Wang, Renhai Yu, Jin Lv, Bo Zhang, Fuzhi Wang, Fei Teng","doi":"10.1109/DDCLS58216.2023.10165958","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10165958","url":null,"abstract":"The application of shipboard microgrids (SMGs) makes it possible to effectively use renewable new energy on the shipboard platform. As renewable energy sources are connected to SMGs in the form of distributed generators (DGs), the openness of the system increases and so does the risk of exposure to cyber attacks. In this paper, a resilient distributed secondary frequency control strategy for SMGs is constructed to resist false data injection (FDI) attacks. An attacker can tamper with the information in the communication links between the DGs of a SMG to prevent the DGs from outputting stable power, thereby causing oscillations in the entire SMG. To increase resilience to FDI attacks, the proposed resilient control strategy introduces a control network layer interconnected with the original data transmission layer to form a hierarchical communication network. By setting the SMG parameters, the proposed strategy can well reduce the negative effects of FDI attacks on DGs and ensure the stable operation of SMGs. Finally, the simulation results verify the effectiveness of the strategy.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124706104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167075
Lin Gao, Tianhong Pan
Model Predictive Control (MPC) has been widely used in the permanent magnet synchronous motor. However, in the finite control set MPC, only one voltage vector is applied, which leads to high current harmonics and torque fluctuations. Meanwhile, three-vector MPC inevitably increases the switching frequency of inverter. In this article, a multi-vector switching control approach is established. Based on the location information of the created reference voltage vector, the relevant control technique is implemented. The proposed control method with single-vector, two-vector and three-vector composite modes of action is designed to achieve low switching frequency with excellent steady-state performance. The proposed method's effectiveness is confirmed by the experimental results.
{"title":"Composite Multi-Vector Model Predictive Control for Permanent Magnet Synchronous Motor","authors":"Lin Gao, Tianhong Pan","doi":"10.1109/DDCLS58216.2023.10167075","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167075","url":null,"abstract":"Model Predictive Control (MPC) has been widely used in the permanent magnet synchronous motor. However, in the finite control set MPC, only one voltage vector is applied, which leads to high current harmonics and torque fluctuations. Meanwhile, three-vector MPC inevitably increases the switching frequency of inverter. In this article, a multi-vector switching control approach is established. Based on the location information of the created reference voltage vector, the relevant control technique is implemented. The proposed control method with single-vector, two-vector and three-vector composite modes of action is designed to achieve low switching frequency with excellent steady-state performance. The proposed method's effectiveness is confirmed by the experimental results.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124817863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：