研究発表

This paper deals with a distributed control for load frequency of power networks based on iterative gradient methods. The control objective is to minimize the quadratic cost function ofload frequency control problem, and we apply this distributed control methodology to complicated large scale electrical power networks by using iterative gradient methods. Finally,simulation results of distributed large scale power network systems including distributed generations, batteries, and renewable energies in order to show the effectiveness of the load frequency control compared with decentralized control and centralized control.

This paper deals with cooperative conveyance control by vehicle swarms with dynamic network topology. First, we introduce a dynamic network topology that depends on relative distance between the vehicles. Secondly, we propose a novel conveyance strategy based on consensus seeking with dynamic network topology. The proposed strategy needs at least one vehicle which can acquire the information of the target-object and network topology among vehicles is time-varying but always connected. Furthermore the strategy is composed of Surround and Transport mode. To analyze the convergence of conveyance with dynamic network topology, algebraic graph theory and matrix theory are utilized. Finally, numerical simulation results demonstrate the effectiveness of the proposed method.

再生可能エネルギーを本格的に導入するためには，大規模・複雑化していくシステムをどのような形態で接続するかというネットワークトポロジーの視点から，電力システムの安定性・最適性のための協調分散制御などの理論や構造設計論が必要である． また環境に依存した不安定な属性を持つ再生可能エネルギーから引き出す電力品質（周波数・電圧など）の確保のために，再生可能エネルギー源と蓄電池の組合せに予測制御を施すことにより，安定化する必要がある． 本講演では，これらの課題に対して，最新の分散協調制御理論についてご紹介する．

本論文は自律移動ロボットの自己位置推定と地図作成問題(SLAM)に対して，位置センサの確率的振舞が未知の場合に対して，H∞フィルタを用いて推定誤差の解析を行った．ここでは特に共分散膨張法を用いることにより，位置推定精度を向上させ，誤差共分散行列の漸近的収束性を証明した．更には，小型移動ロボット用いた実時間制御実験により，理論解析の有効性を実験的に示すとともに，SLAM問題に対してH∞フィルタと共分散膨張法の有用性も実証した．

本論文は自律移動ロボットの自己位置推定と地図作成問題(SLAM)に対して，特に通信環境の変動により計測データが断続的に得られる場合において，拡張カルマンフィルタを用いて自己位置推定した場合の位置推定誤差の上下限をフィッシャー情報行列を用いて算出する方法を提案している．具体的には仮に計測データが届かない場合でもFIMを用いることで，誤差共分散の上下限が与えられることを理論的に証明している．最後に数値シミュレーションにっより提案法の有効性を検証している．

This paper deals with a network configuration considering priori estimation error variance and communication energy in sensor networked feedback system. Each sensor transmits information to a fusion center via multi-hop communication network. We propose a novel network configuration algorithm that each sensor node transmits information with a same dimension. The transmitted information is merged in each sensor node and consists of measurement data with time delay. Then the priori estimate can be minimum variance estimation. The proposed algorithm achieves sub-optimal network topology with minimum energy and a desired variance. Experimental results show an effectiveness of the proposed method.

In this paper, we examine the H? ?Filter-based SLAM especially about its convergence properties. In contrast to Kalman Filter approach that considers zero mean gaussian noise, H∞ Filter is more robust and may provide sufficient solutions for SLAM in an environment with unknown statistical behavior. Due to this advantage, H∞ Filter is proposed in this paper, to efficiently estimate the robot and landmarks location under worst case situations. H∞ Filter requires the designer to appropriately choose the noise's covariance with respect to γ to obtain a desired outcome. We show some of the conditions to be satisfy in order to achieve better estimation results than Kalman Filter. From the experimental results, H∞Filter performs better than Kalman Filter for a case of bigger robot initial uncertainties. Subsequently, this proved that H ∞ Filter can provide another available estimation method for especially in SLAM. © 2010 IEEE.

This paper deals with a passive-decomposition based control of bilateral teleoperation between a single master robot and multiple cooperative slave robots under time varying delay in the communication line. At first, we decompose the dynamics of multiple slave robots into two decoupled dynamics: the Shape-System describing dynamics of the cooperative works, and the Locked-System representing overall behavior of the multiple slave robots. Second, we propose a PD control method for bilateral teleoperation to guarantee the asymptotical stability of the system for time varying delay. Finally, experimental results show the effectiveness of our proposed teleoperation. © 2010 IEEE.

This paper deals with a sensor scheduling algrithm considering estimation error variance and communication energy in sensor networked feedback systems. We propose a novel decentralized estimation algorithm with unknown inputs in each sensor node. Most existing works deal with the sensor network systems as sensing systems and it is difficult to apply them to the real physical feedback control systems Then some local estimates are merged and the merged estimates can be optimized in the proposed method and the estimation error covariance has a unique positive definite solution under some assumptions. Next, we propose a novel sensor scheduling algorithm in which each sensor node transmits information. A sensor node uses energy by communication between other sensor node or the plant. The proposed algorithm achieves a sub-optimal network topology with minimum energy and a desired variance. Experimental results show an effectiveness of the proposed method. © 2010 IEEE.

This paper presents H∞ Filter SLAM, which is also known as the minimax filter to estimate the robot and landmarks location with the analysis on partial observability. Some convergence conditions are also presented to aid the analysis. Due to SLAM is a controllable but unobservable problem, it's difficult to estimate the position of robot and landmarks even though the control inputs are given to the system. As a result, Covariance Inflation which is a method of adding a pseudo positive semidefinite(PsD) matrix is proposed as one approach to analyze Partial Observability effects in SLAM and to reduce the computation cost. H∞ Filter is capable of withstand non-gaussian noise characteristics and therefore, may provide another available approach towards SLAM solution. © 2010 AACC.

In this paper, we study cooperative control problems with a multi-UAV system. Specifically, we propose a c consensus-based cooperative formation control strategy with collision-avoidance capability for a group of multiple unmanned aerial vehicles (UAVs). To cooperatively fly in formation, a consensus-based algorithm and a leader-follower structure are applied to the UAVs. Collisions among the UAVs can occur while they are flying with the cooperative control algorithm. Therefore, we describe how the cooperatively controlled UAVs avoid collisions while they are flying to desired position for formation. The collision-avoidance strategy is based on an artificial potential approach. The convergence is guaranteed even when the cooperative formation control algorithm and the collision-avoidance control algorithm are simultaneously applied to the quadrotors. Finally, the proposed approach is validated by some simulations. © 2014 American Automatic Control Council.

This paper deals with the Simultaneous Localization and Mapping (SLAM) problem via the H∞ filter with compensations for intermittent observations. In order to estimate positions of a robot and landmarks accurately under intermittent observations, we propose the method with a novel filter which detects and compensates intermittent observations by comparing the obtained data with their estimations. This paper also shows the convergence of the estimated error covariance matrices. With simulation and experimental results, we confirm that the state of the robot and the environmental conditions are estimated accurately via the proposed filter under intermittent observations and that the derived theorems of the convergence are correct. © 2014 IEEE.

This paper deals with a model of a real-time electricity market for a smart grid, and discusses how to decide wholesale and retail prices of electricity among market participants: power consumers, power suppliers and an independent system operator. In this paper, we consider a linearized AC model of a power grid with a direct current process and show that the wholesale price for suppliers is determined as the optimal Lagrange multiplier corresponding to the nodal power balance constraints. In addition, we propose a decision procedure for the retail pricing of electricity for consumers. In order to improve the convergence speed of the algorithm, we apply the iterative gradient method with line search to a retail pricing decision procedure, in which the optimal step size is determined in each iteration step. Simulation results, finally, show the effectiveness of the proposed retail pricing decision procedure.

In this paper, we propose a fuel consumption optimization technique for a power-split hybrid electric vehicle (HEV) via gain-scheduled model predictive control. The control algorithms for the hybrid powertrain choose the power split between the internal combustion engine and electric motor of the HEV in order to minimize the fuel consumption. The operating conditions of the HEV change during driving in real time by road conditions and driver demand. Therefore, it is necessary to have optimal control in real time and to consider the time-varying motor condition. We model an HEV and propose a gain-scheduled model predictive control for a power-split HEV. Using the proposed technique, the optimal fuel consumption for a power-split HEV is achieved. Finally, the proposed approach is validated by numerical simulations.

This study deals with decentralized and hierarchical control for electric power networks by using overlapping information. The control objective is to minimize the cost function of the load frequency control problem. The control law consists of three steps. We apply the decentralized hierarchical control methodology to large-scale electrical power networks consisting of distributed power storage and generation sources such as batteries and wind power generators. Through several numerical simulation results of decentralized power networks, we verified their stability, compared the proposed and the previous decentralized methods, and showed the effectiveness of the proposed method.

In this paper, we propose a fuel consumption optimization technique for a power-split hybrid electric vehicle (HEV) via gain-scheduled model predictive control. The control algorithms for the hybrid powertrain choose the power split between the internal combustion engine and electric motor of the HEV in order to minimize the fuel consumption. The operating conditions of the HEV change during driving in real time by road conditions and driver demand. Therefore, it is necessary to have optimal control in real time and to consider the time-varying motor condition. We model an HEV and propose a gain-scheduled model predictive control for a power-split HEV. Using the proposed technique, the optimal fuel consumption for a power-split HEV is achieved. Finally, the proposed approach is validated by numerical simulations.