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Volume 53, Issue 7
Displaying 1-5 of 5 articles from this issue
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Paper
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Takahiko OSHIGE, Kazuro TSUDA2017 Volume 53 Issue 7 Pages 377-384
Published: 2017
Released on J-STAGE: July 13, 2017
JOURNAL FREE ACCESSSteel making plants have a variety of heating processes. Emissivity compensation is still one of the most difficult problems when radiation thermometers are applied to heated sheet in steel making processes. In previous reports 1), 2), the authors proposed a new technique using spectral information of radiation from targets and multivariate analysis, such as principal analysis (PCA) or partial least squares (PLS). This paper describes the basic concept of the technique and shows the effectiveness by simulations using calculated and measured reflectance data of various thickness films on steel sheet.View full abstractDownload PDF (2528K) -
Tadashi SUMIOKA, Tomoyuki HOSOYA, Yoshihiro MORI2017 Volume 53 Issue 7 Pages 385-397
Published: 2017
Released on J-STAGE: July 13, 2017
JOURNAL FREE ACCESSIn this paper, we propose a vehicle motion control method considering stability at the limit state using nonlinear model predictive control. In order to consider the vehicle limit state, a vehicle model is defined by using a nonlinear tire model that considers combined slip etc. The steering angle and the slip rates of each wheel at the limit lateral acceleration state are calculated on the numerical simulations. In addition, as an application example of the proposed method, results of running a circuit course at the fastest speed using curvature minimum path generation algorithm is demonstrated. Simulation results show that the proposed method can run at the speed at the lateral acceleration limit according to the course shape and it is able to run on the out-in-out trajectory.View full abstractDownload PDF (3230K) -
Yoshiro TAKAMATSU, Noriaki FUJIKI, Yoshitaka DEGUCHI, Taketoshi KAWABE2017 Volume 53 Issue 7 Pages 398-407
Published: 2017
Released on J-STAGE: July 13, 2017
JOURNAL FREE ACCESSReduction of noise in a car cabin due to road irregularities has been a subject of research for many years. For reducing such noise, we proposed H2-based active structural acoustic control method that used a road noise estimation model and we verified its feasibility on a test bench. In this paper, we propose a road noise reduction control method in driving condition. The structure of the control system, the method to design actuator allocation, and the generalized plant and weighting functions for MIMO H2 controller synthesis are proposed. The proposed methods were verified in the computer simulation results and the driving experimental results.View full abstractDownload PDF (3342K) -
Dai KINOSHITA, Kazunobu YOSHIDA, Itaru MATSUMOTO2017 Volume 53 Issue 7 Pages 408-415
Published: 2017
Released on J-STAGE: July 13, 2017
JOURNAL FREE ACCESSFor the problem of stabilizing an inverted pendulum with restricted travel, a saturating control law is developed that satisfies the amplitude constraint on the cart and has a large region of attraction. The analysis and design is performed based on the linearlized model of the system, as in the study of Lin et al. The control law has two design parameters: T and k, which are both positive. As T approaches 0, the region of attraction becomes to close to the maximal one. These parameters are chosen to optimize the transient response of the system considering the input constraint, the bandwidth of the actuator, observation noise, etc. The performance of the control system, the size of the region of attraction, and the robustness for measurement and modeling errors are evaluated, via simulation and experiment, by comparing them with the ones of the control system designed by Lin et al.'s method.View full abstractDownload PDF (1588K) -
Masayuki NAKAMURA2017 Volume 53 Issue 7 Pages 416-423
Published: 2017
Released on J-STAGE: July 13, 2017
JOURNAL FREE ACCESSThis paper presents improved learning and optimal cooling models to minimize the power consumption of computer room air conditioners (CRACs) in large data centers. These models consist of a learning model of large data center's thermal system and a CRAC's optimal cooling model. The learning model uses L1-norm regularization for efficient learning. The optimal cooling model involves feedback control of CRACs, linear programming for CRAC's control and server's workload placement based on simple sorting. The proposed models are expected to result in fast learning of the thermal system and large reduction of CRAC's power consumption. Simulation experiments are conducted to evaluate its learning and energy efficiencies. The simulation results indicate that the proposed models resulted in efficient learning of large data center's thermal system and large increase of energy efficiency compared with that of a baseline control model.View full abstractDownload PDF (3656K)
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