論文

© 2018 The trend started in 1997 with the introduction of common rail injection and after some protocols set new targets for overall CO2 emissions. As the diesel engine emits less CO2 than its gasoline counterpart, it kept conquering more and more market shares. Conventional diesel engine control design is mainly based on the maps techniques which required too much time, money and human resources under the number of experiments under various environmental conditions. This makes increasing system complexity. In our authors group, we have proposed that the control structure has the feedback error learning, two-degree-of-freedom controller configuration, with advanced neural networks (NNs) as the feedforward controller along the model-based control method. On the other hand, a cerebellar model articulation controller (CMAC) is a non-fully connected perceptron like associative memory network with overlapping receptive fields, which is used to resolve problems that involve rapid growth and the learning difficulty. Then CMACs have the advantages of good generalization capability, fast learning ability, and simple computation. To our best knowledge, this is new introduce the cerebellar model articulation controller (CMAC) for the control diesel engine combustion control. The effectiveness of the proposed method will be confirmed through numerical simulations based on the Tokyo University diesel engine model with triple fuel injections.

© 1991-2012 IEEE. This paper investigates the localization of multiple near-field narrowband sources with a symmetric uniform linear array, and a new linear prediction approach based on the truncated singular value decomposition (LPATS) is proposed by taking an advantage of the anti-diagonal elements of the noiseless array covariance matrix. However, when the number of array snapshots is not sufficiently large enough, the 'saturation behavior' is usually encountered in most of the existing localization methods for the near-field sources, where the estimation errors of the estimated directions-of-arrival (DOAs) and ranges cannot decrease with the signal-to-noise ratio. In this paper, an oblique projection based alternating iterative scheme is presented to improve the accuracy of the estimated location parameters. Furthermore, the statistical analysis of the proposed LPATS is studied, and the asymptotic mean-square-error expressions of the estimation errors are derived for the DOAs and ranges. The effectiveness and the theoretical analysis of the proposed LPATS are verified through numerical examples, and the simulation results show that the LPATS provides good estimation performance for both the DOAs and ranges compared to some existing methods.

© 2018 The Society of Instrument and Control Engineers - SICE. The use of photovoltaics (PV) in electric power networks has increased because of advantages such as power loss reduction, environmental friendliness, voltage profile improvement, and postponement of system upgrades. However, using PVs of an inappropriate size leads to greater power losses due to variations in PV outputs and demand loads. Therefore, it is necessary to determine the optimal size of PVs. When one considers the various weather conditions and demand load profiles, the optimal PV sizing problem (OPSP) becomes a large-scale optimization that is difficult to solve. In this paper, to resolve this computational issue, we formulate the OPSP as a second-order cone programming model and propose a decomposition using the subgradient method for solving the partial Lagrangian dual problem. We implemented the proposed method on a 47-bus distribution feeder model, and the simulation results proved its viability.

© 2019 JSME. In this paper, we propose a model-based diesel engine air path control system using the simple adaptive control scheme. By using this proposed control system, we can design a robust controller with respect to change of environment, disturbances and nonlinearity of the air path system. We confirm the effectiveness of this proposed system by the simulation using an air path model based on physics and the experiment.

現在のディーゼルエンジン燃焼制御は, 静的マップによるフィードフォワード制御を行うことが主流である．本研究では，フィードバック誤差学習（FEL)の制御構造を保存して，フィードフォワード制御器をAI（人工知能）の技術を用いてオンライン適応・学習させ，ドライバーや環境に積極的に適応・学習できる制御系を提案している．設計された制御アルゴリズムの有効性を確認するために，東大離散化ディーゼルエンジンモデルに対する数値シミュレーションと東大エンジンベンチによる実証実験実施する．

This paper proposes a control method of the magnetorheological (MR) damper semi-active suspension controlling system with an L1 adaptive controller. The suspension system can be represented as a non-homogeneous system. Therefore, the proposed controller design method is introduced for avoiding the solving process of the convolution of a transfer matrix in the non-homogeneous system and guaranteeing the performance of the system within an acceptable level. The result, which is simulated by MATLAB/SIMULINK, proves that the proposed control architecture can work well for reducing calculation process. Moreover, this method is compared with the auto-tuned PID function in SIMULINK; as a result, the method shows a better overall performance by adjusting a few parameters.