The present indoor environment control is conducted according to the set-points given by room occupants or building managers. This control method might exist improper temperature set-points so that result in discomfort of overheating/overcooling and corresponding energy waste. For the purpose of solving these problems, a smart solution for indoor environment control, which is based on online learned thermal comfort model using infrared thermal imaging, is proposed to take place of the set-points based control. Experiments were conducted to study the feasibility, user acceptance, and energy performance of the proposed smart control method. The experiment results show that shows that the users are satisfactory with this control system, which means the proposed indoor thermal environment control method based on thermal sensation prediction is feasible for actual application and effective for achieve more satisfactory indoor thermal environment using a smarter way.
{"title":"Smart control of indoor thermal environment based on online learned thermal comfort model using infrared thermal imaging","authors":"Fulin Wang, Binruo Zhu, Rui Li, Dianshan Han, Zeyun Sun, Saejin Moon, Ziyang Gong, Wenhong Yu","doi":"10.1109/COASE.2017.8256221","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256221","url":null,"abstract":"The present indoor environment control is conducted according to the set-points given by room occupants or building managers. This control method might exist improper temperature set-points so that result in discomfort of overheating/overcooling and corresponding energy waste. For the purpose of solving these problems, a smart solution for indoor environment control, which is based on online learned thermal comfort model using infrared thermal imaging, is proposed to take place of the set-points based control. Experiments were conducted to study the feasibility, user acceptance, and energy performance of the proposed smart control method. The experiment results show that shows that the users are satisfactory with this control system, which means the proposed indoor thermal environment control method based on thermal sensation prediction is feasible for actual application and effective for achieve more satisfactory indoor thermal environment using a smarter way.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129544604","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256178
Xiaojian Liu, Chenrui Wu, Le-miao Qiu, Yang Wang, Shuyou Zhang
Machining accuracy is one of the major parameters of machine tools which is determined by geometric accuracy design to a large extent. In order to improve the comprehensiveness and veracity in geometric accuracy design, this paper proposed a geometric errors analysis method integrated clamping error and wear out error over working space. A multi-rigid-body model which included the cutting tool's wear out error and work-piece's clamping error is established to represent the position relationships of machine tools' working components. The expression of geometric error was converted from matrix form to screw form through a screw mapping method so that geometric error of all the six degree of freedom in global coordinate frame can be straightly expressed. Based on this, the key geometric errors that affecting the machining accuracy were identified through the improved sensitivity analysis in which motion rules through working space were considered. Finally, a case study on geometric accuracy design stage of a horizontal boring machine was carried out which highlights the advantages of the proposed methodology.
{"title":"A geometric errors analysis method integrated clamping error and wear out error over working space","authors":"Xiaojian Liu, Chenrui Wu, Le-miao Qiu, Yang Wang, Shuyou Zhang","doi":"10.1109/COASE.2017.8256178","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256178","url":null,"abstract":"Machining accuracy is one of the major parameters of machine tools which is determined by geometric accuracy design to a large extent. In order to improve the comprehensiveness and veracity in geometric accuracy design, this paper proposed a geometric errors analysis method integrated clamping error and wear out error over working space. A multi-rigid-body model which included the cutting tool's wear out error and work-piece's clamping error is established to represent the position relationships of machine tools' working components. The expression of geometric error was converted from matrix form to screw form through a screw mapping method so that geometric error of all the six degree of freedom in global coordinate frame can be straightly expressed. Based on this, the key geometric errors that affecting the machining accuracy were identified through the improved sensitivity analysis in which motion rules through working space were considered. Finally, a case study on geometric accuracy design stage of a horizontal boring machine was carried out which highlights the advantages of the proposed methodology.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128643982","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256342
Keyu Lu, Jian Li, X. An, Hangen He, Xiping Hu
Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). Meanwhile, it also poses to be a demanding task due to the diversity of traffic scenes and resource limitations of the platforms for traffic scene applications. To address these issues, we present a generalized Haar filter based CNN (Convolutional Neural Network) which is suitable for the object detection tasks in traffic scenes. In this approach, we first decompose an object detection task into multiple local regression tasks. Thereafter, we handle these local regression tasks using several light and efficient networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of these deep networks are constrained to the form of generalized Haar filters. Finally, we carry out various experiments to evaluate the performance of our proposed approach in traffic scene datasets. Experimental results demonstrate that our object detection system is light and effective in comparison with the state-of-the-art.
{"title":"Generalized Haar filter based CNN for object detection in traffic scenes","authors":"Keyu Lu, Jian Li, X. An, Hangen He, Xiping Hu","doi":"10.1109/COASE.2017.8256342","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256342","url":null,"abstract":"Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). Meanwhile, it also poses to be a demanding task due to the diversity of traffic scenes and resource limitations of the platforms for traffic scene applications. To address these issues, we present a generalized Haar filter based CNN (Convolutional Neural Network) which is suitable for the object detection tasks in traffic scenes. In this approach, we first decompose an object detection task into multiple local regression tasks. Thereafter, we handle these local regression tasks using several light and efficient networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of these deep networks are constrained to the form of generalized Haar filters. Finally, we carry out various experiments to evaluate the performance of our proposed approach in traffic scene datasets. Experimental results demonstrate that our object detection system is light and effective in comparison with the state-of-the-art.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131123280","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256071
Yunyi Kang, L. Mathesen, Giulia Pedrielli, Feng Ju
Analytical and simulation models are two common types of approaches used to estimate and predict the performance of complex production systems. Typically analytical models are fast to run but can have reduced accuracy. On the other hand simulation models can achieve high accuracy, but only at the cost of large simulation time and number of replications. Traditionally, the research has been focusing on the development of models able to achieve a satisfactory trade off between accuracy and computational effort. Nevertheless, such an approach implies the choice of a single model to approximate the system behavior. There is still lack of a generic model that can deliver high accuracy and low computational cost for production systems. In this paper, we attempt to address this issue and present a multi-fidelity modeling approach, utilizing both analytical models and simulation models at different levels of fidelity, to efficiently and effectively estimate the performance of asynchronous serial lines with exponential machines. Experimental results show that the multi-fidelity model provides better estimation of the production rate of the studied example Such a model has demonstrated potential in evaluating a large number of solutions with limited computational budget.
{"title":"Multi-fidelity modeling for analysis of serial production lines","authors":"Yunyi Kang, L. Mathesen, Giulia Pedrielli, Feng Ju","doi":"10.1109/COASE.2017.8256071","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256071","url":null,"abstract":"Analytical and simulation models are two common types of approaches used to estimate and predict the performance of complex production systems. Typically analytical models are fast to run but can have reduced accuracy. On the other hand simulation models can achieve high accuracy, but only at the cost of large simulation time and number of replications. Traditionally, the research has been focusing on the development of models able to achieve a satisfactory trade off between accuracy and computational effort. Nevertheless, such an approach implies the choice of a single model to approximate the system behavior. There is still lack of a generic model that can deliver high accuracy and low computational cost for production systems. In this paper, we attempt to address this issue and present a multi-fidelity modeling approach, utilizing both analytical models and simulation models at different levels of fidelity, to efficiently and effectively estimate the performance of asynchronous serial lines with exponential machines. Experimental results show that the multi-fidelity model provides better estimation of the production rate of the studied example Such a model has demonstrated potential in evaluating a large number of solutions with limited computational budget.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130012752","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256147
Zhaolong Ning, Weigang Hou, Xiping Hu, Xiaoxue Gong
The Cyber-Physical System (CPS) concept is now attracting attention in systems engineering, and it is being applied to a fully automated factory control in processes such as semiconductor fabrication. In this paper, we propose a novel control decision structure for process manufacturing, designated as the 3D Optical Network-on-Chip (ONoC) multi-core system, based on the cloud-supported CPS concept. We first construct a task graph — which includes interconnected Virtual Machines (VMs)—to represent the interaction between industrial-physical processes and cyber states. Given the task graph, the control decision process becomes into the problem of the on-chip VM placement. We then design a highly reliable on-chip VM placement scheduling to find the optimal control strategy while guaranteeing the reliability of the 3D ONoC structure. The simulation results demonstrate that our scheme achieves a higher reliability of the 3D ONoC structure when we make the control decision for process manufacturing.
{"title":"A cloud-supported cps approach to control decision of process manufacturing: 3D ONoC","authors":"Zhaolong Ning, Weigang Hou, Xiping Hu, Xiaoxue Gong","doi":"10.1109/COASE.2017.8256147","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256147","url":null,"abstract":"The Cyber-Physical System (CPS) concept is now attracting attention in systems engineering, and it is being applied to a fully automated factory control in processes such as semiconductor fabrication. In this paper, we propose a novel control decision structure for process manufacturing, designated as the 3D Optical Network-on-Chip (ONoC) multi-core system, based on the cloud-supported CPS concept. We first construct a task graph — which includes interconnected Virtual Machines (VMs)—to represent the interaction between industrial-physical processes and cyber states. Given the task graph, the control decision process becomes into the problem of the on-chip VM placement. We then design a highly reliable on-chip VM placement scheduling to find the optimal control strategy while guaranteeing the reliability of the 3D ONoC structure. The simulation results demonstrate that our scheme achieves a higher reliability of the 3D ONoC structure when we make the control decision for process manufacturing.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128810019","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256207
Zhiling Liu, Qing-Shan Jia, Hui Zhang
Evacuation plays an important role in the response process towards inevitable disasters and emergencies. Compared with the large number of evacuees, the number of evacuation guiders is much smaller. The evacuation guider location problem is of great practical importance, since the locations of guiders impact evacuation process and evacuation policy optimization. In general, it is difficult to identify the optimal locations of guiders due to the partial information and partial control, the complexity of evacuation process, and the large state and decision spaces. In this paper, we consider this important problem and make the following main contributions. First, we use the event-based optimization (EBO) theory to model the evacuation problem. Second, we develop an evacuation guider location optimization method based on road network centrality measures and use this method to optimize the evacuation process. Third, we evaluate the performance of our method through numerical results. We hope this work brings insight in evacuation guider location optimization problem.
{"title":"An evacuation guider location optimization method based on road network centrality measures","authors":"Zhiling Liu, Qing-Shan Jia, Hui Zhang","doi":"10.1109/COASE.2017.8256207","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256207","url":null,"abstract":"Evacuation plays an important role in the response process towards inevitable disasters and emergencies. Compared with the large number of evacuees, the number of evacuation guiders is much smaller. The evacuation guider location problem is of great practical importance, since the locations of guiders impact evacuation process and evacuation policy optimization. In general, it is difficult to identify the optimal locations of guiders due to the partial information and partial control, the complexity of evacuation process, and the large state and decision spaces. In this paper, we consider this important problem and make the following main contributions. First, we use the event-based optimization (EBO) theory to model the evacuation problem. Second, we develop an evacuation guider location optimization method based on road network centrality measures and use this method to optimize the evacuation process. Third, we evaluate the performance of our method through numerical results. We hope this work brings insight in evacuation guider location optimization problem.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116294288","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256301
Cheng Huang, Y. Lei
In needle insertion procedures, path planning is crucial to the success of the operation. In this paper, a preoperative path planning algorithm is proposed that considers the needle-tissue interactions for flexible needle insertion operations. Vector Form Intrinsic Finite Element (VFIFE) and Finite Element Method (FEM) are used to calculate the deformation of the needle and tissue, respectively. The non-linearity of the needle and the change of boundary conditions during the insertion process can be integrated easily. The Potential Field-guided Rapidly-Exploring Random Trees (PF-RRT) is applied to generate the initial path set, in which the candidate path will be selected. The needle control sequence that is to generate the optimal path is obtained from the selected candidate path by combining Iteration Learning Control (ILC) method with the needle-tissue interaction model. The simulation results show that the proposed method is effective to generate candidate needle insertion paths that consider needle-tissue interactions.
{"title":"A novel model-based path planning method for robot-assisted flexible needle insertion","authors":"Cheng Huang, Y. Lei","doi":"10.1109/COASE.2017.8256301","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256301","url":null,"abstract":"In needle insertion procedures, path planning is crucial to the success of the operation. In this paper, a preoperative path planning algorithm is proposed that considers the needle-tissue interactions for flexible needle insertion operations. Vector Form Intrinsic Finite Element (VFIFE) and Finite Element Method (FEM) are used to calculate the deformation of the needle and tissue, respectively. The non-linearity of the needle and the change of boundary conditions during the insertion process can be integrated easily. The Potential Field-guided Rapidly-Exploring Random Trees (PF-RRT) is applied to generate the initial path set, in which the candidate path will be selected. The needle control sequence that is to generate the optimal path is obtained from the selected candidate path by combining Iteration Learning Control (ILC) method with the needle-tissue interaction model. The simulation results show that the proposed method is effective to generate candidate needle insertion paths that consider needle-tissue interactions.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127228409","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256272
Siyun Chen, F. Gao, Ting Liu
Load identification is important for the tasks such as load forecasting, demand response and energy management in smart buildings. The accuracy of the traditional methods depends on the dimension of load signatures, the sampling frequency and the stability of load profile. In this paper, a Factorial Hidden Markov Model (FHMM)-based method is proposed to analyze the aggregate load profile and identify the individual device. We extend the Viterbi algorithm to solve the FHMM directly, and this process is more efficient than the solution of the equivalent HMM by using the conventional Viterbi algorithm. The proposed method is insensitive to the stability and accuracy of power data, so it is suitable for the devices in buildings, even for the continuously variable loads. Two experiments with real power data are evaluated to illustrate the proposed method. Meanwhile, we focus on the online performance of the Viterbi algorithm. It is found that the states decoded by Viterbi are unreliable when the observed data are inside a confusing zone. Through analyzing the mechanism of the Viterbi algorithm, the judgment conditions the boundary of the confusing zone are given. We hope this work brings insight to the research on load identification and HMM.
{"title":"Load identification based on Factorial Hidden Markov Model and online performance analysis","authors":"Siyun Chen, F. Gao, Ting Liu","doi":"10.1109/COASE.2017.8256272","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256272","url":null,"abstract":"Load identification is important for the tasks such as load forecasting, demand response and energy management in smart buildings. The accuracy of the traditional methods depends on the dimension of load signatures, the sampling frequency and the stability of load profile. In this paper, a Factorial Hidden Markov Model (FHMM)-based method is proposed to analyze the aggregate load profile and identify the individual device. We extend the Viterbi algorithm to solve the FHMM directly, and this process is more efficient than the solution of the equivalent HMM by using the conventional Viterbi algorithm. The proposed method is insensitive to the stability and accuracy of power data, so it is suitable for the devices in buildings, even for the continuously variable loads. Two experiments with real power data are evaluated to illustrate the proposed method. Meanwhile, we focus on the online performance of the Viterbi algorithm. It is found that the states decoded by Viterbi are unreliable when the observed data are inside a confusing zone. Through analyzing the mechanism of the Viterbi algorithm, the judgment conditions the boundary of the confusing zone are given. We hope this work brings insight to the research on load identification and HMM.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126053119","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256197
He Lyu, Xiangbao Song, Dan Dai, Jiangang Li, Zexiang Li
In this paper, tool path interpolation and redundancy optimization algorithms are designed for the industrial manipulator to perform tasks exhibiting 1-DoF redundancy such as the welding, cutting etc. B-spline is applied for the tool path interpolation and then by minimizing the energy consumption while avoiding singularity and respecting joint limits at the same time, the optimal trajectory can be obtained. The problem is formulated and solved by nonlinear optimization method. POE(Product of exponential) model is used for robotic kinematic and dynamic analysis to simplify the problem. Experiments were conducted to illustrate the feasibility of our method.
{"title":"Tool path interpolation and redundancy optimization of manipulator","authors":"He Lyu, Xiangbao Song, Dan Dai, Jiangang Li, Zexiang Li","doi":"10.1109/COASE.2017.8256197","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256197","url":null,"abstract":"In this paper, tool path interpolation and redundancy optimization algorithms are designed for the industrial manipulator to perform tasks exhibiting 1-DoF redundancy such as the welding, cutting etc. B-spline is applied for the tool path interpolation and then by minimizing the energy consumption while avoiding singularity and respecting joint limits at the same time, the optimal trajectory can be obtained. The problem is formulated and solved by nonlinear optimization method. POE(Product of exponential) model is used for robotic kinematic and dynamic analysis to simplify the problem. Experiments were conducted to illustrate the feasibility of our method.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125342282","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 : 2017-08-01DOI: 10.1109/COASE.2017.8256268
Weiyong Yu, Zhenhua Deng, Hongbing Zhou, Yiguang Hong
In this paper, the problem of distributed resource allocation optimization is investigated for continuous-time multi-agent systems with discrete-time communication. A gradient-based continuous-time algorithm is proposed to solve this network resource allocation problem. A sufficient condition on the communication period is given to show that the proposed algorithm can achieve the exact optimization with exponential convergence rate. Finally, an example of economic dispatch in power grids is given to illustrate the effectiveness of the presented algorithm.
{"title":"Distributed resource allocation optimization with discrete-time communication and application to economic dispatch in power systems","authors":"Weiyong Yu, Zhenhua Deng, Hongbing Zhou, Yiguang Hong","doi":"10.1109/COASE.2017.8256268","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256268","url":null,"abstract":"In this paper, the problem of distributed resource allocation optimization is investigated for continuous-time multi-agent systems with discrete-time communication. A gradient-based continuous-time algorithm is proposed to solve this network resource allocation problem. A sufficient condition on the communication period is given to show that the proposed algorithm can achieve the exact optimization with exponential convergence rate. Finally, an example of economic dispatch in power grids is given to illustrate the effectiveness of the presented algorithm.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124004551","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}
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