Pub Date : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238122
The Hung Pham, D. Ichalal, S. Mammar
The contribution of this work focuses on generating the best path for an UAV to distribute medicine to all the infected areas of an agriculture environment which contains non-convex obstacles, pest-free areas and pests-ridden areas. The algorithm for generating this trajectory can save the working time and the amount of medicine to be distributed to the whole agriculture infected areas. From the information on the map regarding the coordinates of the obstacles, non-infected areas, and infected areas, the infected areas are divided into several non-overlapping regions by using a clustering technique. There is a trade-off between the number of classes generated and the area of all the pests-ridden areas. After that, a polygon will be found to cover each of these infected regions. However, obstacles may occupy part of the area of these polygons that have been created previously. Each polygon that is occupied in part by obstacles can be further divided into a minimum number of obstacle-free convex polygons. Then, an optimal path length of boustrophedon trajectory will be created for each convex polygon that has been created for the UAV to follow. Finally, this paper deals with the process of creating a minimal path for the UAV to move between all the constructed convex polygons and generate the final trajectory for the UAV which ensures that all the infected agriculture areas will be covered by the medicine. The algorithm of the proposed method has been tested on MATLAB and can be used in precision agriculture.
{"title":"Complete coverage path planning for pests-ridden in precision agriculture using UAV","authors":"The Hung Pham, D. Ichalal, S. Mammar","doi":"10.1109/ICNSC48988.2020.9238122","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238122","url":null,"abstract":"The contribution of this work focuses on generating the best path for an UAV to distribute medicine to all the infected areas of an agriculture environment which contains non-convex obstacles, pest-free areas and pests-ridden areas. The algorithm for generating this trajectory can save the working time and the amount of medicine to be distributed to the whole agriculture infected areas. From the information on the map regarding the coordinates of the obstacles, non-infected areas, and infected areas, the infected areas are divided into several non-overlapping regions by using a clustering technique. There is a trade-off between the number of classes generated and the area of all the pests-ridden areas. After that, a polygon will be found to cover each of these infected regions. However, obstacles may occupy part of the area of these polygons that have been created previously. Each polygon that is occupied in part by obstacles can be further divided into a minimum number of obstacle-free convex polygons. Then, an optimal path length of boustrophedon trajectory will be created for each convex polygon that has been created for the UAV to follow. Finally, this paper deals with the process of creating a minimal path for the UAV to move between all the constructed convex polygons and generate the final trajectory for the UAV which ensures that all the infected agriculture areas will be covered by the medicine. The algorithm of the proposed method has been tested on MATLAB and can be used in precision agriculture.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131467310","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238057
Jiajie Huang, Qinghua Zhu, Yan Hou
With the rapid growth of cloud computing, its energy waste and excessive energy consumption have become a big issue. Cloud infrastructure is built on a large number of servers and devices. In the execution processes of computing tasks, faults of different components may occur in server hardware/software at any time. We propose a task scheduling method for high performance computing considering failures of servers and the transmission of task datasets in data centers. This approach optimizes two conflicting objectives: minimizing energy consumption during computation and transmission, and reducing application rejections or violations due to failures. The proposed method can also improve resource utilization. The experimental simulations via large scale parallel working datasets show that this method can obtain good energy saving benefit and high quality of service.
{"title":"Energy-sensitive Scheduling for Cloud Data Centers Prone to Failures*","authors":"Jiajie Huang, Qinghua Zhu, Yan Hou","doi":"10.1109/ICNSC48988.2020.9238057","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238057","url":null,"abstract":"With the rapid growth of cloud computing, its energy waste and excessive energy consumption have become a big issue. Cloud infrastructure is built on a large number of servers and devices. In the execution processes of computing tasks, faults of different components may occur in server hardware/software at any time. We propose a task scheduling method for high performance computing considering failures of servers and the transmission of task datasets in data centers. This approach optimizes two conflicting objectives: minimizing energy consumption during computation and transmission, and reducing application rejections or violations due to failures. The proposed method can also improve resource utilization. The experimental simulations via large scale parallel working datasets show that this method can obtain good energy saving benefit and high quality of service.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133486287","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238066
Xi Zhang, Li Li, Chao-Bo Yan
To further improve Software Defined networks performance, robustness and load balancing, it is valuable to determine how to optimally deploy controllers against links failure. In this paper, we model the robust controller placement (RCP) optimization problem with an integer linear programming, termed RCP_ILP, which optimally places the least controllers to meet the robustness and load balancing. The novelty of our model is taking into controller coverage probability, network transmission efficiency and Gini coefficient of controller loading at the cost of least controllers. To reduce the computational complexity for the optimal configuration, we propose a heuristic RCP algorithm. The extensive simulations conducted with real network topologies show that the heuristic RCP algorithm improves the robustness and load balancing of SDNs against links failure.
{"title":"Robust Controller Placement Based on Load Balancing in Software Defined Networks","authors":"Xi Zhang, Li Li, Chao-Bo Yan","doi":"10.1109/ICNSC48988.2020.9238066","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238066","url":null,"abstract":"To further improve Software Defined networks performance, robustness and load balancing, it is valuable to determine how to optimally deploy controllers against links failure. In this paper, we model the robust controller placement (RCP) optimization problem with an integer linear programming, termed RCP_ILP, which optimally places the least controllers to meet the robustness and load balancing. The novelty of our model is taking into controller coverage probability, network transmission efficiency and Gini coefficient of controller loading at the cost of least controllers. To reduce the computational complexity for the optimal configuration, we propose a heuristic RCP algorithm. The extensive simulations conducted with real network topologies show that the heuristic RCP algorithm improves the robustness and load balancing of SDNs against links failure.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133417688","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238095
Fangyuan Tian, Haifeng Zhu, Chen Zhou, Yibin Tao, Yan Li, J. Xue
In the distribution network with high penetration rate of photovoltaic power generation, the phenomenon of photovoltaic discarding can be reduced and the power reverse feeding can be prevented to some extent by configuring the energy storage system reasonably. This paper analyzes the influence of time-of-use (TOU) electric pricing on user load reduction and transfer characteristics, defines the self-elasticity coefficient and cross-elasticity coefficient of electricity price, and establishes the user load demand response model. On this basis, this paper comprehensively considers the loss of photovoltaic discarding and the cost of energy storage, and then adopts the net present value method to realize the optimal configuration of energy storage capacity. Finally, an example is given to verify that the strategy proposed in this paper can reduce the redundancy of rated capacity of energy storage by properly abandoning solar energy in the distribution network when the demand response is considered.
{"title":"Optimal Configuration Strategy of Energy Storage Accessing to Distribution Network","authors":"Fangyuan Tian, Haifeng Zhu, Chen Zhou, Yibin Tao, Yan Li, J. Xue","doi":"10.1109/ICNSC48988.2020.9238095","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238095","url":null,"abstract":"In the distribution network with high penetration rate of photovoltaic power generation, the phenomenon of photovoltaic discarding can be reduced and the power reverse feeding can be prevented to some extent by configuring the energy storage system reasonably. This paper analyzes the influence of time-of-use (TOU) electric pricing on user load reduction and transfer characteristics, defines the self-elasticity coefficient and cross-elasticity coefficient of electricity price, and establishes the user load demand response model. On this basis, this paper comprehensively considers the loss of photovoltaic discarding and the cost of energy storage, and then adopts the net present value method to realize the optimal configuration of energy storage capacity. Finally, an example is given to verify that the strategy proposed in this paper can reduce the redundancy of rated capacity of energy storage by properly abandoning solar energy in the distribution network when the demand response is considered.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115432172","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}
The human-cyber-physical system (HCPS) is a composite intelligent system comprising humans, cyber systems, and physical systems with the aim of achieving specific manufacturing goals at an optimized level. Smart factory is an important carrier of a new-generation intelligent manufacturing. In order to achieve the comprehensive collaboration of human-machine-thing and other elements in the smart factory, the HCPS is introduced to the smart factory in this paper. Firstly, a smart factory model is constructed based on human-cyber-physical (HCPS). Then, according to the characteristics of big data, Internet-of-Things(IoT) and artificial intelligence(AI), the management methods of smart factory is proposed, including production design, resource intelligent management and knowledge discovery. Finally, a guiding technology architecture of human-centered smart factory production and operation management is given. The smart factory based on HCPS is of great significance to realize the full use of various resources, and agile management. Index Terms-Human-cyber-physical system, Smart Factory, Production and Operation, Management Methods
{"title":"Smart Factory Production and Operation Management Methods based on HCPS","authors":"Jiahui Yu, Yuxiang Sun, Wanwen Zheng, Xianzhong Zhou","doi":"10.1109/ICNSC48988.2020.9238110","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238110","url":null,"abstract":"The human-cyber-physical system (HCPS) is a composite intelligent system comprising humans, cyber systems, and physical systems with the aim of achieving specific manufacturing goals at an optimized level. Smart factory is an important carrier of a new-generation intelligent manufacturing. In order to achieve the comprehensive collaboration of human-machine-thing and other elements in the smart factory, the HCPS is introduced to the smart factory in this paper. Firstly, a smart factory model is constructed based on human-cyber-physical (HCPS). Then, according to the characteristics of big data, Internet-of-Things(IoT) and artificial intelligence(AI), the management methods of smart factory is proposed, including production design, resource intelligent management and knowledge discovery. Finally, a guiding technology architecture of human-centered smart factory production and operation management is given. The smart factory based on HCPS is of great significance to realize the full use of various resources, and agile management. Index Terms-Human-cyber-physical system, Smart Factory, Production and Operation, Management Methods","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115464987","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238121
Chao Yang, Guanjun Liu, Chungang Yan
AdaBoost is a well-known effective boosting algorithm for classification and has achieved successful applications in many fields. The existing studies show that it is very sensitive to noisy points, resulting in a decline of classification performance. We have proposed an improved algorithm called CAdaBoost in order to overcome the weakness. However, our CAdaBoost uses a set of super-parameters. In this paper, we propose a no-super-parametric improvement to CAdaBoost and it is applied to the problem of detecting credit card fraud. Although the performance of this CAdaBoost without super-parameters is a little worse than the original CAdaBoost, it still outperforms others including the original AdaBoost and several existing improvements of AdaBoost. Our design without super-parameters provides a helpful idea for other similar problems.
{"title":"A k-means-based and no-super-parametric Improvement of AdaBoost and its Application to Transaction Fraud Detection","authors":"Chao Yang, Guanjun Liu, Chungang Yan","doi":"10.1109/ICNSC48988.2020.9238121","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238121","url":null,"abstract":"AdaBoost is a well-known effective boosting algorithm for classification and has achieved successful applications in many fields. The existing studies show that it is very sensitive to noisy points, resulting in a decline of classification performance. We have proposed an improved algorithm called CAdaBoost in order to overcome the weakness. However, our CAdaBoost uses a set of super-parameters. In this paper, we propose a no-super-parametric improvement to CAdaBoost and it is applied to the problem of detecting credit card fraud. Although the performance of this CAdaBoost without super-parameters is a little worse than the original CAdaBoost, it still outperforms others including the original AdaBoost and several existing improvements of AdaBoost. Our design without super-parameters provides a helpful idea for other similar problems.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117046742","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238124
Shuaiqi Huang, Zhuangzhuang He, Xiang Li
In this work, an estimation model of state of charge (SOC) based on machine learning algorithm is proposed for the real-time back cloud driving data of electric vehicle (EV). The features of driving data transmitted to the cloud is too few to use traditional SOC estimation methods based on power battery models. We process and reconstruct the online-data combined with the characteristics of EV and power battery before training the model. Subsequently, two kinds of methods summarized for processing such cloud data, namely SOC-Interpolation based Regression Algorithm and Driving-Accumulate based Classification Algorithm. Experimental results of various machine learning algorithms show that the model is able to accurately predict SOC stocks. Experiments using various machine learning algorithms show that the model is able to accurately estimate the SOC Stock of electric vehicles in motion. Among the trained models, the SOC-Interpolation based LGB model achieves the best performance.
{"title":"A Method of SOC Estimation for Electric Vehicle Based on Limited Information","authors":"Shuaiqi Huang, Zhuangzhuang He, Xiang Li","doi":"10.1109/ICNSC48988.2020.9238124","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238124","url":null,"abstract":"In this work, an estimation model of state of charge (SOC) based on machine learning algorithm is proposed for the real-time back cloud driving data of electric vehicle (EV). The features of driving data transmitted to the cloud is too few to use traditional SOC estimation methods based on power battery models. We process and reconstruct the online-data combined with the characteristics of EV and power battery before training the model. Subsequently, two kinds of methods summarized for processing such cloud data, namely SOC-Interpolation based Regression Algorithm and Driving-Accumulate based Classification Algorithm. Experimental results of various machine learning algorithms show that the model is able to accurately predict SOC stocks. Experiments using various machine learning algorithms show that the model is able to accurately estimate the SOC Stock of electric vehicles in motion. Among the trained models, the SOC-Interpolation based LGB model achieves the best performance.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129134798","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238102
Xiaojun Zhu, Yan Li, Yibin Tao, Qun Zhang, J. Xue, Chen Zhou, Zhihao Yang
New energy with increasing permeability has increased the unstable factors of power system. Large-scale energy storage system compensating for the fluctuating power of new energy power generation has a high practical significance. To make full use of the regulating ability of the energy storage system, a power decoupling control model of 35kV cascaded H-bridge energy storage system is proposed with the capacity of the energy storage system. Firstly, the topology of 35kV energy storage system is given. Secondly, according to the related technical standards and requirements, the model of cascaded unit equipment of the system is determined. Based on 35kV cascaded H-bridge energy storage system, power regulation model of energy storage power conversion system (PCS) is built and the active power and reactive power decoupling control strategy for energy storage system is obtained. Finally, the control strategy is simulated and validated by using MATLAB/Simulink simulation system. The stability of the control strategy can be verified by the results.
{"title":"Power Decoupling Control Strategy of 35 kV Cascaded H-Bridge Energy Storage System","authors":"Xiaojun Zhu, Yan Li, Yibin Tao, Qun Zhang, J. Xue, Chen Zhou, Zhihao Yang","doi":"10.1109/ICNSC48988.2020.9238102","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238102","url":null,"abstract":"New energy with increasing permeability has increased the unstable factors of power system. Large-scale energy storage system compensating for the fluctuating power of new energy power generation has a high practical significance. To make full use of the regulating ability of the energy storage system, a power decoupling control model of 35kV cascaded H-bridge energy storage system is proposed with the capacity of the energy storage system. Firstly, the topology of 35kV energy storage system is given. Secondly, according to the related technical standards and requirements, the model of cascaded unit equipment of the system is determined. Based on 35kV cascaded H-bridge energy storage system, power regulation model of energy storage power conversion system (PCS) is built and the active power and reactive power decoupling control strategy for energy storage system is obtained. Finally, the control strategy is simulated and validated by using MATLAB/Simulink simulation system. The stability of the control strategy can be verified by the results.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130724864","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238085
Dazi Li, Mingjie Yan, Zhiwen Miao, Yue-cheng Fang, Jun Liu
To explore the effective information contained in mass data and improve the accuracy of stress prediction under low strain rate, a stress prediction method based on a hybrid model of convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed for the temporal characteristics and non-linearity of stress data. Massive historical stress data and strain data are constructed as continuous features according to the time sliding window as input. Firstly, feature vectors are extracted by CNN, constructed in the manner of sequence and used as input data of LSTM network. Then the LSTM network is employed to predict the stress. Stress data obtained in the process of rubber stretching are divided into two parts: training data and test data. The model is trained by training data and test data are used for validation of the proposed model. Experimental results show that the proposed prediction method has higher prediction accuracy than the standard LSTM network.
{"title":"LSTM Neural Network based Tensile Stress Prediction of Rubber Streching","authors":"Dazi Li, Mingjie Yan, Zhiwen Miao, Yue-cheng Fang, Jun Liu","doi":"10.1109/ICNSC48988.2020.9238085","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238085","url":null,"abstract":"To explore the effective information contained in mass data and improve the accuracy of stress prediction under low strain rate, a stress prediction method based on a hybrid model of convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed for the temporal characteristics and non-linearity of stress data. Massive historical stress data and strain data are constructed as continuous features according to the time sliding window as input. Firstly, feature vectors are extracted by CNN, constructed in the manner of sequence and used as input data of LSTM network. Then the LSTM network is employed to predict the stress. Stress data obtained in the process of rubber stretching are divided into two parts: training data and test data. The model is trained by training data and test data are used for validation of the proposed model. Experimental results show that the proposed prediction method has higher prediction accuracy than the standard LSTM network.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131335853","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238114
Wataru Hirayanagi, M. Deng, Y. Noge
In recent years, the weight of the robotic arm has been reduced to achieve high-speed operation in robotic industry. On the other hand, the rigidity of the arm is reduced and the vibration is increased. Its vibration must be suppressed to prevent accuracy in work to be decreased. In this paper, the piezoelectric actuator is used as an actuator to control the vibration of the L-shaped arm. However, hysteresis nonlinearity exists in the piezoelectric actuator. The operator based control system of the L-shaped arm is designed and its stability is confirmed by simulation. Moreover, CPG corresponding to hysteresis nonlinearity is considered and is added to the control system. The effect of vibration suppression is verified by simulation.
{"title":"Nonlinear Control for L-Shaped Arm Using Central Pattern Generator","authors":"Wataru Hirayanagi, M. Deng, Y. Noge","doi":"10.1109/ICNSC48988.2020.9238114","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238114","url":null,"abstract":"In recent years, the weight of the robotic arm has been reduced to achieve high-speed operation in robotic industry. On the other hand, the rigidity of the arm is reduced and the vibration is increased. Its vibration must be suppressed to prevent accuracy in work to be decreased. In this paper, the piezoelectric actuator is used as an actuator to control the vibration of the L-shaped arm. However, hysteresis nonlinearity exists in the piezoelectric actuator. The operator based control system of the L-shaped arm is designed and its stability is confirmed by simulation. Moreover, CPG corresponding to hysteresis nonlinearity is considered and is added to the control system. The effect of vibration suppression is verified by simulation.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128865649","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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