論文

In this research, we deal with the coordination problem of Connected and Automated Vehicles (CAVs) at signal-free intersections. First, the problem is formulated to find the appropriate merge time for each CAV. Next, an optimal motion planning problem is formulated to find the control input (jerk) necessary to accurately achieve the given optimal merging time while minimizing the jerk and acceleration. We also present an analytical solution for this motion planning problem with inequality constraints on jerk and acceleration, which achieves both passenger comfort and reduced fuel consumption. Finally, the simulation results show the effectiveness of the proposed method.

In this paper, we consider fuel economy optimization problem for mild HEV (Hybrid Electric Vehicle) via hierarchical model predictive control. In our algorithm, we deal with two problems; one is eco-driving, and the other is torque distribution. In the eco-driving problem, we control vehicle speed. Considering the reduction of fuel consumption and NOx emission, we calculate the required torque to follow target speed. Next, in the torque distribution problem, we calculate the distribution between the engine and motor torque. In that phase, engine characteristics are considered. These problems differ in terms of time scales, hence we propose hierarchical model predictive control. Finally, numerical simulation results show the effectiveness of this research.

This paper deals with the problem of gyroscopic effect and unbalance vibration of the Magnetic Bearing system. Using a gain scheduled H∞ control with a free parameter Φ, we design a control system which attenuates the unbalance vibration, and guarantees the stability against the gyroscopic effect in specified rotational speed. Further we implement the controller and evaluate the effectiveness of the proposed approach by experiments. First, our experimental setup is explained, a mathematical model of the magnetic bearing is derived. Then, we introduce the gain scheduled H∞ control with free parameters to a magnetic bearing control, in order to reject the disturbances caused by unbalance of the rotor, and guarantee the stability against gyroscopic effect, even if rotational speed of the rotor changes. At last, several experimental results show the effectiveness of this proposed method.

本論文では３点法における高次成分の良好な再現方法について議論している．計算不可能な設定角の領域を明確にし，この角度を避けて真円度を測定することにより，目標次数までのすべてのフーリエ成分を求めることが出来ることを示した．また設定角の測定誤差を小さくするためには，３台のセンサの出力信号波形の合同性が重要であることを明らかにし，合同性を容易に検査する手法の一例を示した．

本論文は磁気軸受システムの不釣合い振動問題に対してH∞制御の観点からアプローチした．H∞ループ整形法に基づいて，フリーパラメータを持つゲインスケジュールH∞ロバストコントローラを設計し，ロータの不釣合いに起因する外乱の漸近的除去を達成した. 特にロータの回転速度をスケジューリングパラメータとし，ロータの回転速度が変化しても不釣合いに起因する外乱を除去した．最後に提案法の有効性をシミュレーションと制御実験により検証した．

アンビエントセンサネットワーク(ASN)システムでは,知的環境とノード間のネットワークを利用した情報の最適化を行うことが可能であり,単体のセンサによるセンシングと比較して省電力化,耐故障性,多次元情報の収集などの面で優れていることが知られている.最近では大規模センサネットワークに対してアクチュエーション技術(制御技術)を応用することで,エネルギー効率を考慮したシステム構築に関する研究が進んでいる.本稿では,ASNのアクチュエーション,つまり制御システムへの応用を目指し,マルチホップセンサネットワークを介して得られた情報を用いたフィードバック制御系の構成問題を扱う.

In this paper, we study cooperative control problems with trajectory tracking and reconfigurable formation for a multi-vehicle system expressed as a first-order system. Specifically, we describe a problem that cooperatively controlled vehicles keep geometric configuration among the multiple vehicles while the vehicles are moving in formation. In other words, each of the vehicles must change its position individually to keep the geometric configuration while the vehicle is moved by a cooperative control. In order to achieve this objective, we basically extend our previous result which was a control algorithm to surround a moving target by a group of vehicles cooperatively. The algorithm was based on a consensus algorithm which was cooperative control. In addition, we apply a leader-follower structure to our cooperative algorithm so that a leader can individually provide each of the followers with commands. Finally, the proposed approach is validated by some simulations.

In this paper, we propose a novel parameter initial value estimation method of Lithium-ion battery for state of charge (SOC) estimation by using Local Regression Modeling (LRM) and Vector Fitting (VF). To estimate SOC accurately using nonlinear extended Kalman filter, an adequate set of initial parameters of the battery model are required. Therefore, we apply LRM and VF method to derive the initial parameters and the battery model from database of the frequency response. In addition, we show the SOC estimation method using EKF by determined initial value and model. Finally, the effectiveness of the proposed method is shown via several experimental results.

This paper addresses wind power prediction, which is known to be a key technology in energy management systems. In this paper, a 24-h-ahead power prediction method using a filter theory is proposed for wind power generation. The prediction method is a simple algorithm. The procedure of prediction consists of two steps: the data processing and the calculation of the predicted values. In data processing, in order to obtain the correlative data from the database, we employ just-in-time modeling. In the calculation of the predicted values, we propose a regression model for wind speed and wind power, and the unknown parameters are estimated using a constrained Kalman filter. Moreover, in the procedure used to estimate the unknown parameters, reduction and convergence of the variables are also guaranteed. Finally, the advantages of the proposed method over the conventional method are shown through actual prediction evaluations.

In this paper, we discuss the smart parking system in urban traffic network. In recent years, the decline in vehicle speed when searching for parking lots has been caused traffic jam in urban traffic network. Then, in this paper, we aim at shortening the searching time for parking lots, which is one of the causes of traffic jam, by allocating parking lots being available to drivers. Furthermore, we design the dynamical parking fee and redistribute parking lots to level the multiple parking manager's profit in traffic area. Finally, we confirm the effectiveness of proposed algorithm in numerical simulation.

This paper deals with a power demand-supply management based on negawatt trading, and the purpose of this research is to keep demand supply balance and to minimize the social cost of negawatt trading, balancing generator, and power flow. In this research, consumer and Independent System Operator (ISO) participate in the electricity market, and each consumer acts based on Regret Matching which is one of the learning algorithms. Furthermore, as the incentive design of negawatt trading, we consider strategy-proofness and individual rationality using VCG mechanism design. Then, we propose a novel negawatt trading algorithm based on Regret Matching and VCG mechanism design. Finally, simulation results show the effectiveness of the proposed algorithm.

This paper deals with merging control of automated vehicles using distributed model predictive control. Firstly, we consider a road model which has two main lanes. In the distributed control algorithm, vehicles repeat sharing the planned future trajectory with other vehicles traveling in the vicinity of each vehicle by using communication and re-planning its own trajectory considering the future trajectory of other vehicles. Moreover, in order to further improve the stability of model predictive control, terminal constraints are set and feasible conditions for optimization problems under the terminal conditions are derived. At the last part of this paper, we discuss the result of numerical simulations using distributed merging control algorithm and mention the effectiveness of proposed methods and future works.

This paper is intended to analyze benefits of microscopic real-time optimization for traffic signal control. To this end, we first present a microscopic traffic model consisting of signals and vehicles, where each vehicle dynamics is explicitly incorporated in the form of a mixed logical dynamical system (MLDS) differently from many other related publications taking macroscopic flow models. We then formulate a model predictive control (MPC) problem and then reduce the problem to a mixed integer programming problem. Furthermore, we introduce a linear temporal logic (LTL) constraint to reduce the computation burden and to eliminate unrealistic solutions. The proposed control scheme is then applied to a real-time traffic simulator and it is confirmed that: (i) the number of waiting vehicles for traffic lights is reduced with 54.5% as compared to a fixed-time control scheme and (ii) the average computation time is reduced to about one-tenth relative to the case without the LTL constraint.

A novel wide-area control design is presented to mitigate inter-area power frequency oscillations. A large-scale power system is decomposed into a network of passivity-short subsystems whose nonlinear interconnections have a state-dependent affine form, and by utilizing the passivity shortage framework, a two-level design procedure is developed. At the lower level, any generator control can be viewed as one that makes the generator passivity-short and L-2 stable, and the stability impact of the lower-level control on the overall system can be characterized in terms of two parameters. While the system is nonlinear, the impact parameters can be optimized by solving a data-driven matrix inequality (DMI), and the high-level wide-area control is then designed by solving another Lyapunov matrix inequality in terms of the design parameters. The proposed methodology makes the design modular, and the resulting control is adaptive to operating conditions of the power system. Standard test systems are used to illustrate the proposed design, including DMI and the wide-area control, and simulation results demonstrate its effectiveness in damping out inter-area oscillations.

In this paper, a day-ahead electricity market where multiple consumers and suppliers in several regions bid for per unit electricity price and ISO decides power disaptch and inter-regional power flow amount is considered. We show that if ISO maximizes the difference between payment from customers and payment to suppliers, then the social welfare will be maximized. Then, we propose an algorithm to solve the optimal power dispatch problem using dual decomposition. Finally, simulation results show the effectiveness of the proposed algorithm.

In this paper, we propose an algorithm for state estimation and attack detection in distributed observer system and derive the condition for achieving the estimation and detection. In detail, the algorithm using the notion of virtual states can be used in each local observer for distributedly attack detection by oneself. In the perspective of attack, we define, not only measurement attack, but an observer communication attack which is the falsification of the transmitted data between local observers and also define a combination attack which combines the observer communication attack and measurement one. We derive the condition for estimation and detection under the combination attack as the theorem and, finally, verify the effectiveness of the proposed algorithm by numerical simulations.

This paper deals with photovoltaic (PV) power prediction and building energy management system (BEMS) in multiple buildings. First, we consider PV power prediction. We predict PV power by using k-means method and SVR. However, there is an error between prediction and actual generation. In order to solve this issue, we estimate prediction error in advance by markov process and revise the prediction. And, by applying copula to calculated point prediction, calculate interval prediction. Second, we consider 2 level BEMS. At first level, in each building, power consumption is determined considering comfort. At second level, in order to reduce total cost in all buildings, battery scheduling is calculated with robustness by applying scenario robust theory. And, at the end of each section, simulation results show the effectiveness of proposed prediction method and energy management method.

In this paper, we propose virtual distributed observers which are built by virtually excluding some redundant sensors from conventional distributed observers. By using these observers, we can construct not only robust estimators against sensor attacks but detectors of attacks by malicious agents. We also consider the new framework of estimation problem which is more useful to adapt under distributed conditions. This relaxed problem makes it possible to estimate partial state under sensor attacks even if the whole observer system is not detectable. Finally, we confirm the effectiveness of our proposed method through numerical simulations.