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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2020-10-21 11:05 |
Online | Online |  IBISML2020-20 |
As complexity of robots and environments increases, analytically formulating their kinematics and dynamics becomes diffi... [more] | IBISML2020-20 pp.37-38 |
| PN, NS, OCS (Joint) |
2020-06-18 13:40 |
Online | Online | Optimal VNF Management with Model Predictive Control for Multiple Service Chains Masaya Kumazaki (Univ. Fukui), Masaki Ogura (Osaka Univ.), Takuji Tachibana (Univ. Fukui) NS2020-22 |
Network function virtualization (NFV) provides network functions by implementing virtual network function (VNF) on a com... [more] | NS2020-22 pp.1-6 |
| EE | 2020-01-15 10:50 |
Kagoshima | HOUZAN HALL | A study on influence of prediction horizon of model predictive control to characteristics of digitally controlled dc-dc converter Yuya Noda, Koya Taguchi, Hidenori Maruta (Nagasaki Univ.) EE2019-51 |
In this paper, we evaluate influence of a prediction horizon with combinatorial approach of the model predictive control... [more] | EE2019-51 pp.13-18 |
| RISING (2nd) |
2019-11-26 14:10 |
Tokyo | Fukutake Learning Theater, Hongo Campus, Univ. Tokyo | [Poster Presentation]
Optimal VNF Placement and Route Selection with Model Predictive Control for Service Chains Masaya Kumazaki (Univ. Fukui), Masaki Ogura (Osaka Univ.), Takuji Tachibana (Univ. Fukui) |
Network function virtualization (NFV) provides network functions by implementing virtual network function (VNF) on a com... [more] | |
| EE, IEE-SPC | 2019-07-24 11:30 |
Hiroshima | A Study on Model Predictive Control for DC-DC Converters with Combinatorial Approach Yuya Noda, Hidenori Maruta (Nagasaki Univ.) EE2019-19 |
In this paper, we propose a model predictive control (MPC) method for dc-dc converters with combinatorial approach. One ... [more] | EE2019-19 pp.13-18 |
|
| RCC, MICT | 2019-05-29 13:00 |
Tokyo | TOKYO BIG SIGHT | Dynamic Surveillance over Graphs by Multiple Agents
-- On Feasibility Conditions -- Koichi Kobayashi (Hokkaido Univ.) RCC2019-1 MICT2019-1 |
The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In th... [more] | RCC2019-1 MICT2019-1 pp.1-4 |
| CAS, CS | 2019-03-09 15:00 |
Kanagawa | Shonan Institute of Technology | Calculation of predicted trajectory of power assist device based on human disturbance input Yukinosuke Fujita, Takahiko Mori (Shonan Institute of Tech.) CAS2018-155 CS2018-123 |
The authors have used Maciejowski's model predictive control for the purpose of supporting human's single joint motion s... [more] | CAS2018-155 CS2018-123 pp.93-96 |
| CAS, ICTSSL | 2019-01-25 15:40 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Trajectory Tracking Control of Distributed Parameter Mechanical Systems via a Blending Method of Discrete Mechanics and Nonlinear Optimization Hirotoshi Yasumi, Tatsuya Kai (Tokyo Univ. of Science) CAS2018-137 ICTSSL2018-56 |
The purpose of this study is to develop a new trajectory tracking control method for distributed parameter mechanical sy... [more] | CAS2018-137 ICTSSL2018-56 pp.101-106 |
| MSS, SS | 2019-01-15 10:50 |
Okinawa | Model Predictive Control of Dynamics in Power Consumption for Demand Response Kenta Ohashi, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) MSS2018-55 SS2018-26 |
Demand response is a method of controlling electric equipments on the customer side according to the situation of power ... [more] | MSS2018-55 SS2018-26 pp.7-12 |
|
| MSS, SS | 2019-01-15 11:15 |
Okinawa | Design of Demand Response Based on Event-Triggered Model Predictive Control Kodai Miyazaki, Koichi Kobayashi (Hokkaido Univ.), Shun-ichi Azuma (Nagoya Univ.), Nobuyuki Yamaguchi (Tokyo Univ. of Science), Yuh Yamashita (Hokkaido Univ.) MSS2018-56 SS2018-27 |
In design of energy management systems, aggregators such as retailers play the important role. One of the roles of aggre... [more] | MSS2018-56 SS2018-27 pp.13-18 |
|
| MSS, SS | 2019-01-15 13:30 |
Okinawa | A design method of a self-triggered model predictive controller for linear discrete-time systems with noises Fumito Tagashira, Toshimitsu Ushio (Osaka Univ.) MSS2018-59 SS2018-30 |
In self-triggered control, both the control input and the next update time are determined at every update time. So, the ... [more] | MSS2018-59 SS2018-30 pp.29-32 |
|
| MSS, SS | 2019-01-15 13:55 |
Okinawa | Multi-Agent Monitoring with Fuel Constraints over Graphs Ryo Masuda, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) MSS2018-60 SS2018-31 |
The multi-agent monitoring (surveillance) problem over graphs is to find trajectories of multiple agents that travel eac... [more] | MSS2018-60 SS2018-31 pp.33-36 |
|
| RCC, MICT | 2018-05-24 13:00 |
Tokyo | Tokyo Big Sight | [Poster Presentation]
Model Predictive Control of Multi-Hop Control Networks with Disturbances Dai Satoh, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) RCC2018-14 MICT2018-14 |
In this paper, an approximate solution method of model predictive control (MPC) for a multi-hop control network (MHCN) i... [more] | RCC2018-14 MICT2018-14 pp.67-71 |
| RCC, MICT | 2018-05-24 13:00 |
Tokyo | Tokyo Big Sight | [Poster Presentation]
Multi-hop Control System considering Link Reliabilities Koji Ishii (Kagawa Univ.) RCC2018-15 MICT2018-15 |
Multi-hop control networks, in which multiple controllers control multiple controlled objects through a given multi-hop ... [more] | RCC2018-15 MICT2018-15 pp.73-78 |
| MSS, NLP (Joint) |
2018-03-12 13:35 |
Osaka | A Study on Availability Design of a Building Microgrid with In-vehicle Battery Shoko Kimura, Yoshihiko Susuki, Atsushi Ishigame (Osaka Prefecture Univ.) MSS2017-78 |
We address the so-called availability of power supply in a building microgrid with in-vehicle battery. In order to archi... [more] | MSS2017-78 pp.5-10 |
|
| SIS | 2018-03-08 15:25 |
Aichi | Meijo Univ. Tempaku Campus | DNN:-MPC: A Hardware oriented Deep Neural Networks for Model Predictive Control Kentaro Honda, Naoki Iwaya (Kyutech), Teppei Hirotsu, Toshiaki Nakamura, Tatuya Horiguchi (HITACHI), Hakaru Tamukoh (Kyutech) SIS2017-60 |
Model Predictive Control (MPC) is one of the control systems, where it uses "predictive model" to control objects. Howev... [more] | SIS2017-60 pp.17-22 |
| NS, IN (Joint) |
2018-03-02 09:20 |
Miyazaki | Phoenix Seagaia Resort | A Study of Applying Model Predictive Control to Peak Shaving Using Energy Storage at Campus Buildings Yukio Ogawa (Muroran IT), Go Hasegawa, Masayuki Murata (Osaka Univ.) IN2017-114 |
In smart cities, flattening peak demands in residential buildings is needed for providing stable power supplies. An effe... [more] | IN2017-114 pp.147-152 |
| ITS, WBS, RCC | 2017-12-15 10:55 |
Okinawa | Tiruru/Okinawa Jichikaikan | A Study on Frequency Assignment for Satellite Communications Based on Model Predictive Control Yuma Abe (NICT/Keio Univ.), Hiroyuki Tsuji, Amane Miura (NICT), Shuichi Adachi (Keio Univ.) WBS2017-70 ITS2017-47 RCC2017-86 |
In this paper, a frequency assignment method that is based on Model Predictive Control (MPC). A broadband satellite comm... [more] | WBS2017-70 ITS2017-47 RCC2017-86 pp.197-202 |
| ITS, WBS, RCC | 2017-12-15 11:45 |
Okinawa | Tiruru/Okinawa Jichikaikan | Predictive Pinning Control for Consensus of Multi-Agent Systems Koichi Kobayashi (Hokkaido Univ.) WBS2017-72 ITS2017-49 RCC2017-88 |
In this paper, based on the policy of model predictive control (MPC), a new method of predictive pinning control is prop... [more] | WBS2017-72 ITS2017-49 RCC2017-88 pp.209-213 |
| NS, IN (Joint) |
2017-03-03 10:40 |
Okinawa | OKINAWA ZANPAMISAKI ROYAL HOTEL | Load Balancing based on Model Predictive Control for Edge Computing Satoshi Imai, Toru Katagiri (Fujitsu Lab.) NS2016-200 |
Edge computing has recently been attracting a lot of attention as a technology for reducing network traffic and service ... [more] | NS2016-200 pp.247-252 |
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