2020 IEEE Conference on Control Technology and Applications (CCTA)最新文献_第5页

Active damping of drivetrain vibrations with compensation for time-varying long control cycle due to actuator constraint 动器约束下时变长控制周期的动力传动系统振动补偿主动阻尼

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206370

Heisei Yonezawa, I. Kajiwara, C. Nishidome, Takashi Hatano, Masato Sakata, Shigeki Hiramatsu

引用次数: 0

Route-optimized Drive Mode Switching Control for Plug-in Hybrid Vehicles: Controller Design and Experimental Validation 插电式混合动力汽车路径优化驱动模式切换控制:控制器设计与实验验证

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206374

Ryunosuke Watanabe, Shoji Hirate, Tatsuya Ibuki, Yoshihiro Sakayanagi, M. Sampei

{"title":"Route-optimized Drive Mode Switching Control for Plug-in Hybrid Vehicles: Controller Design and Experimental Validation","authors":"Ryunosuke Watanabe, Shoji Hirate, Tatsuya Ibuki, Yoshihiro Sakayanagi, M. Sampei","doi":"10.1109/CCTA41146.2020.9206374","DOIUrl":"https://doi.org/10.1109/CCTA41146.2020.9206374","url":null,"abstract":"This paper presents a route-optimized drive mode switching control method for plug-in hybrid vehicles (PHVs) and its experimental validation. The research objective is to reduce fuel usage in situations where energy consumption along a route is probabilistically estimated from historical driving data. To address this, this study develops a driving-route model based on road grades and vehicle speed distributions with terrain maps and driving profiles. The driving-route model allows us to predict fuel and electricity demands on the planned route by applying energy consumption maps relating to drive modes of a PHV on the market. Moreover, the driving-route model can be shared among any PHVs, even if the powertrain properties differ. These models are leading to formulate an integer linear programming problem deciding the drive modes for reducing fuel consumption. In addition, this paper introduces an experimental system to show the proposed approach is applicable to the actual PHV. The experimental validation on the PHV indicates that this method can improve fuel efficiency compared with that of a conventional method.","PeriodicalId":241335,"journal":{"name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121360580","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}

引用次数: 3

A state observer for the calibration of the Hazen-Williams equation 用于校准Hazen-Williams方程的状态观测器

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206329

L. Torres, Martín Jiménez, C. Verde, Laurent Reyes

引用次数: 1

Vehicle-to-Grid Charging Optimization of Electric Vehicle 电动汽车车对网充电优化

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206325

Hyunsup Kim, Hanseung Myeong, I. Park, J. Choi, Kyoungjoo Kim

引用次数: 0

A Data-driven Hierarchical Control Structure for Systems with Uncertainty 不确定系统的数据驱动层次控制结构

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206349

Lu Shi, Hanzhe Teng, Xinyue Kan, Konstantinos Karydis

{"title":"A Data-driven Hierarchical Control Structure for Systems with Uncertainty","authors":"Lu Shi, Hanzhe Teng, Xinyue Kan, Konstantinos Karydis","doi":"10.1109/CCTA41146.2020.9206349","DOIUrl":"https://doi.org/10.1109/CCTA41146.2020.9206349","url":null,"abstract":"The paper introduces a Data-driven Hierarchical Control (DHC) structure to improve performance of systems operating under the effect of system and/or environment uncertainty. The proposed hierarchical approach consists of two parts: 1) A data-driven model identification component to learn a linear approximation between reference signals given to an existing lower-level controller and uncertain time-varying plant outputs. 2) A higher-level controller component that utilizes the identified approximation and wraps around the existing controller for the system to handle modeling errors and environment uncertainties during system deployment. We derive loose and tight bounds for the identified approximation's sensitivity to noisy data. Further, we show that adding the higher-level controller maintains the original system's stability. A benefit of the proposed approach is that it requires only a small amount of observations on states and inputs, and it thus works online; that feature makes our approach appealing to robotics applications where real-time operation is critical. The efficacy of the DHC structure is demonstrated in simulation and is validated experimentally using aerial robots with approximately-known mass and moment of inertia parameters and that operate under the influence of ground effect.","PeriodicalId":241335,"journal":{"name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","volume":"35 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116488016","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}

引用次数: 6

Model Transformation for Enhanced Parameter Identification of Linear Dynamic Systems 线性动态系统增强参数辨识的模型变换

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206281

Leonam S. D. Pecly, K. Hashtrudi-Zaad

引用次数: 3

Stochastic modelled grid outage effect on home Energy Management 随机模型电网中断对家庭能源管理的影响

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206157

Jesse-James Prince Agbodjan, Pierre Haessig, R. Bourdais, Herve Gheguen

引用次数: 5

Impacts of Distributed Speed Harmonization and Optimal Maneuver Planning on Multi-Lane Roads 多车道道路分布式速度协调与最优机动规划的影响

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206166

Nathan Goulet, B. Ayalew

引用次数: 2

Self-Tuning of Individual Control Components for Systems with Parameter Uncertainties and Disturbances 具有参数不确定性和扰动的系统的单个控制元件的自整定

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206259

A. Wache, H. Aschemann

{"title":"Self-Tuning of Individual Control Components for Systems with Parameter Uncertainties and Disturbances","authors":"A. Wache, H. Aschemann","doi":"10.1109/CCTA41146.2020.9206259","DOIUrl":"https://doi.org/10.1109/CCTA41146.2020.9206259","url":null,"abstract":"In this paper, a self-tuning algorithm for the parametrization of a complete feedback control structure or individual components is presented. This approach allows for automatic performance optimization of a motion task w.r.t. a chosen evaluation cost function (ECF). The proposed algorithm is designed especially for systems with only approximate knowledge about the system parameters that are, moreover, affected by external disturbances. Using optimal control techniques and knowledge of the nominal system only, an initial control structure is designed that involves feedback and feedforward control as well as a state and disturbance observer. The proposed algorithm is capable of determining an optimal control parametrization – ensuring closed-loop stability and close-to-optimal tracking behavior. The self-tuning algorithm is applied to an elastic machine tool axis in simulations and the potential of tuning the individual control components w.r.t. the tracking accuracy is investigated in detail. Finally, the obtained simulation results are compared against each other – showing the efficiency and benefits of the overall approach.","PeriodicalId":241335,"journal":{"name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237100","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}

引用次数: 0

A Performance Comparison of LSTM and Recursive SID Methods in Thermal Modeling of Implantable Medical Devices LSTM与递归SID方法在植入式医疗器械热建模中的性能比较

2020 IEEE Conference on Control Technology and Applications (CCTA) Pub Date : 2020-08-01 DOI: 10.1109/CCTA41146.2020.9206293

Ayca Ermis, Mi Zhou, Yen-Pang Lai, Ying Zhang

{"title":"A Performance Comparison of LSTM and Recursive SID Methods in Thermal Modeling of Implantable Medical Devices","authors":"Ayca Ermis, Mi Zhou, Yen-Pang Lai, Ying Zhang","doi":"10.1109/CCTA41146.2020.9206293","DOIUrl":"https://doi.org/10.1109/CCTA41146.2020.9206293","url":null,"abstract":"This paper investigates application of long short-term memory (LSTM) and recursive system identification (RSID) algorithms to predict the thermal dynamics of bio-implants, e.g. UEA under certain assumptions. Both algorithms implemented in this paper predict the temperature readings of heat sensors using a window size of 10 data points. Simulations in COMSOL software as well as experiments using an in vitro experimental systems are utilized for validation and comparison of algorithm performances. Mean squared error (MSE) of prediction results based on the LSTM algorithm is compared against that of the competitive RSID algorithm for evaluation. Both simulation and experimental results indicate that the LSTM can accurately predict the thermal dynamics of the system and outperforms the RSID algorithm when certain conditions for inputs hold. According to COMSOL simulations and in vitro experiments, the LSTM algorithm returns more reliable predictions for the time period in which the convergence of the adaptive filters in the RSID algorithm is not yet achieved. Alternatively, once the adaptive filters converge, the performance of the RSID algorithm is significantly better than the LSTM for some cases due to its adaptive learning capabilities.","PeriodicalId":241335,"journal":{"name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134287150","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}

引用次数: 0