Details of a Researcher - Namerikawa, Toru

Papers - Namerikawa, Toru

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Non-Cooperative Optimization Algorithm of Charging Scheduling for Electric Vehicle

M Yoshihara, MHM NOR, A Kono, T Namerikawa, Z Qu

SICE Journal of Control, Measurement, and System Integration (The Society of Instrument and Control Engineers ) 13 ( 6 ) 265 - 273 2020.11

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 1882-4889

In this paper, we aim to propose a charging scheduling algorithm for electric vehicles on highways. While the number of electric vehicles has been increasing recently, charging stations are not becoming widespread compared to gas stations. The distance that an electric vehicle can run on one charge is only around 120km to 400km. Therefore, it is necessary to plan to recharge in advance when driving long distances. Problems related to planning algorithms are called charging scheduling problems of electric vehicles. In this paper, we assume that there is no difference in the power of the electric vehicle and the charging station, and consider the situation where each acts to maximize its profit. First, since the electric vehicle can select the charging station freely, it motivates us to solve the optimal allocation problem of the electric vehicle to the charging station using matching theory. Then, non-cooperative game theory is utilized to obtain the energy demand and energy price for the electric vehicles and charging stations, respectively. In addition, the convergence condition of the non-cooperative game is theoretically derived. Finally, the effectiveness of the proposed non-cooperative charging scheduling algorithm is confirmed by numerical simulation.

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Optimal Electric Vehicle Charging Scheduling by Matching Theory and Repeated Game

KONO Akari, NAKAZATO Takuya, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 56 ( 11 ) 495 - 503 2020.11

Research paper (scientific journal), Accepted, ISSN 0453-4654

In this paper, the driver chooses the optimal charging station and determines the optimal energy demand when the highway enters, with the charging station and the electric vehicle controlled. The purpose of this study is to propose optimal charging scheduling. In previous studies, we considered that the charging price was not included in the driver's preference and also the price of the charging station was not optimal. In this paper, the price is included in the preference when the driver selects the charging station, and the optimal price for maximizing the profit is determined by a repeated game by designing the new utility function of the charging station. We theoretically prove the conditions under which repeated game converges, and finally perform numerical simulations to confirm the effectiveness of the proposed algorithm in a more realistic situation than in previous studies.

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Event-triggered and self-triggered formation control of a multi-agent system

T Namerikawa, R Toyota, K Kotani, M Akiyama

Artificial Life and Robotics 25 (4), 513-522 (Springer) 25 ( 4 ) 513 - 522 2020.10

Research paper (scientific journal), Joint Work, Lead author, Accepted, ISSN 1433-5298

In this paper, a distributed event-triggered formation control algorithm for a multi-agent system which consists of linear discrete-time agents is proposed based on control theory. For agents to achieve a formation, it is necessary to communicate with each other to feedback their information. For communication, wireless communications are desirable for various tasks since wired networks can constrain their movement. Also, each agent have to observe their states from sensors. Those energy consumption due to unnecessary calculation and communication of agents may shorten battery life of agents. By using the event-triggered protocol, which updates the control input aperiodically only when certain triggering condition is satisfied, we aim to reduce those calculation and communication by reducing the input update frequency. Also the multi-agent system is guaranteed to achieve a formation by determining the triggering condition of the control input based on Lyapunov’s stability theorem. At the end of the paper, the effectiveness of the proposed algorithm is verified by numerical simulation.

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Detection of Attacks in Smart Grids via Extended Kalman Filter and Correlation Analysis

J Sawodny, O Riedel, T Namerikawa

2020 59th Annual Conference of the Society of Instrument and Control … (2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020) 663 - 669 2020.09

Research paper (international conference proceedings), Joint Work, Last author, Accepted

This paper discusses the detection of attacks on decentralized and intelligent power networks, so-called smart grids. The approach used here, estimates the states of the power network, which is modelled by its power flow, via an Extended Kalman Filter. The use of this filter allows the consideration of nonlinearities, thus enabling the modelling of the losses of the systems. The final detection of attacks on a generator in a power network is realized by a correlation analysis. The correlation analysis provides the result of the inner correlation of sequential signals. Based on this, an assessment can be made as to whether or not the network is affected by an attack.

Autonomous multi-robot tracking system for oil spills on sea surface based on hybrid fuzzy distribution and potential field approach

M Pashna, R Yusof, ZH Ismail, T Namerikawa, S Yazdani

Ocean Engineering 207, 107238 (Ocean Engineering) 207 2020.07

Research paper (scientific journal), Joint Work, Accepted, ISSN 0029-8018

© 2020 Elsevier Ltd Oil spill, may happen as a result of the offshore well vessel failures. The precise information about the location and real-time situation of the oil spill is essential to perform an efficient treatment of these environmental catastrophes. A novel approach of multi robot's system which can navigate autonomously and track oil spill on the sea surfaces. This system is able to self-adapt and path plan amidst environmental changes, including the temporospatial variation of the oil concentration. The method consists of two main parts, which are the modelling of the oil spill, the tracking system and autonomous control of the robots. The simulated model depicts the morphological complexities of the spatiotemporal changes in the oil spills. The fuzzy controller is designed to control the robots to control nonlinear and non-crisply distributed water surfaces where oil pollution occurs. Meanwhile, the multi-robot path planning system hybridised with artificial potential field approach to avoid the collision. Several numerical simulations with different scenarios are done to show the robustness of the methods, based on the accuracy and precision of tracking. The evaluation with the simulated ground truth demonstrates that accuracy and precision of the tracking system are more than 70 and 80 percent respectively.

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Load Frequency Control Using Iterative Gradient Method for Connected Power System Including Distributed Energy Resources

SEKINE Tetsuya, MIZUNO Keisuke, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 56 ( 7 ) 361 - 369 2020.07

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 0453-4654

This paper deals with load frequency control problem of power system. In recent years, due to the large introduction of generators using renewable energy, the relative load fluctuation increases, making it difficult to maintain power balance. Therefore, this paper proposes frequency control method using Moving Horizon Estimation and iterative gradient method for connected power system. In this paper, since system handles frequency control problems in real time, the system manager needs to grasp all states of power system every moment, and estimates the state of power system by using Moving Horizon Estimation. Next, control inputs are determined such that each area minimizes evaluation function including barrier function considering constraint conditions for generator and battery by using iterative gradient method. At this time, since the control target is connencted power system, it is represented as a model including the state of adjacent area, so that each area shares information with each other and performs cooperative control between areas. Finally, simulation of load frequency control using Moving Horizon Estimation and iterative gradient method is carried out and the effectiveness is confirmed.

Frequency Response-Based Initial Parameter Estimation for SOC of Lithium-Ion Battery

K Natori, K Mizuno, T Namerikawa, S Sartori, F Eliassen

IFAC-PapersOnLine 53 (2), 12695-12700 (IFAC-PapersOnLine) 53 ( 2 ) 12695 - 12700 2020.07

Research paper (international conference proceedings), Accepted, ISSN 2405-8963

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(EKF), 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 a constructed database of frequency response data. In addition, we demonstrate the SOC estimation method using EKF by the determined initial value and model. Finally, the effectiveness of the proposed method is shown via several experimental results.

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Non-cooperative Optimization of Charging Scheduling for Electric Vehicle via Stackelberg Game and Matching Theory

M Yoshihara, T Namerikawa

IFAC-PapersOnLine 53 (2), 17029-17034 (IFAC-PapersOnLine) 53 ( 2 ) 17029 - 17034 2020.07

Research paper (international conference proceedings), Accepted, ISSN 2405-8963

In this paper, we deal with the charging scheduling optimization problem of electric vehicle in highway via Stackelberg game and matching theory. At first, we propose an algorithm for electric vehicle allocation to charging station which aims to uniformize waiting time of each charging station. In addition, we present energy demand and price decision problem and solve it using Stackelberg game. We compare Stackelberg equilibrium point and Nash equilibrium point and confirm Stackelberg game provide bigger benefit to charging station. Finally, we show validation of proposed algorithm via numerical simulations.

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Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework

T Rahman, Z Qu, T Namerikawa

IEEE Access 8, 80480-80489 (IEEE Access) 8 80480 - 80489 2020.05

Research paper (scientific journal), Joint Work, Last author, ISSN 2169-3536

© 2013 IEEE. In this paper, the Alternating Direction Method of Multipliers (ADMM) is investigated for distributed optimization problems in a networked multi-agent system. In particular, a new adaptive-gain ADMM algorithm is derived in a closed form and under the standard convex property in order to greatly speed up convergence of ADMM-based distributed optimization. Using Lyapunov direct approach, the proposed solution embeds control gains into weighted network matrix among the agents and uses those weights as adaptive penalty gains in the augmented Lagrangian. It is shown that the proposed closed loop gain adaptation scheme significantly improves the convergence time of underlying ADMM optimization. Convergence analysis is provided and simulation results are included to demonstrate the effectiveness of the proposed scheme.

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Local Attack Detection Algorithm under Combination Attacks in the Distributed Observers

SATO Shotaro, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 56 ( 5 ) 284 - 292 2020.05

Research paper (scientific journal), Accepted, ISSN 0453-4654

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.

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An optimized input/output-constrained control design with application to microgrid operation

R Harvey, Z Qu, T Namerikawa

IEEE Control Systems Letters 4 (2), 367-372 (IEEE Control Systems Letters) 4 ( 2 ) 367 - 372 2020.04

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 2475-1456

© 2017 IEEE. In this letter, a nonlinear control design is presented for systems whose input and output vectors are saturated. The proposed synthesis includes an optimal tracker as the seed controller and explicitly imposes all the relevant constraints on system input, on the rate of change in the input, and on system output by using the tools of barrier functions, input-output feedback linearization, comparison argument, Lyapunov argument, and a real-Time constrained optimization. The proposed design is applied to microgrid operation with high penetration of renewable generation, where net load and variable generation must be balanced and the microgrid should be operated at an optimal or near-optimal performance level. Since traditional generation has a limited ramping rate, battery storage devices, and demand responses become necessary to effectively deal with variability of renewable generation and to maintain frequency stability, but their capacities are also limited. It is shown that the proposed design is effective for coordinated control of traditional generation, storage and demand response so that the power system frequency is guaranteed to be within the required operational limits and that renewable curtailment is eliminated or minimized.

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Optimization of Bidding Price and Power Dispatch in Electricity Market by Iterative Bidding

KATO Yusuke, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 56 ( 4 ) 249 - 258 2020.04

Research paper (scientific journal), Last author, Accepted, ISSN 0453-4654

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.

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PV Power Prediction via Residual Correction and Hierarchical Multiple Buildings Energy Management

YAMAUCHI Ken, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 56 ( 4 ) 208 - 216 2020.04

Research paper (scientific journal), Last author, Accepted, ISSN 0453-4654

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.

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Event-triggered and Self-triggered Formation Control of a Linear Multi-agent System

豊田凌，小谷健人，滑川徹

IEEJ Transactions on Electronics, Information and Systems 140 ( 3 ) 303 - 311 2020.03

Research paper (scientific journal), Last author, Accepted

Optimal Motion Planning of Connected and Automated Vehicles at Signal-Free Intersections with State and Control Constraints

MHM NOR, T Namerikawa

SICE Journal of Control, Measurement, and System Integration (公益社団法人計測自動制御学会) 13 ( 2 ) 30 - 39 2020.03

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 1882-4889

This paper presents the optimal motion planning problem for connected and automated vehicles (CAVs) to cross a conflict area at an intersection with state and control constraints. First, we formulate the scheduled merging (or crossing) time for all CAVs as a mixed integer linear programming (MILP) problem where the merging time is solved frequently. Second, we formulate the optimal motion planning problem so that the CAVs can achieve their scheduled merging time as well as minimizing the energy consumption. Since we solve the motion planning problem analytically, not all the solutions are feasible to comply with the frequently updated merging time. To solve this problem, we propose a feasibility enforcement period (FEP). Then, we validate the proposed solution through simulation, and the results show that even the merging time is frequently updated, the CAVs can still achieve the merging time with a minimal deviation. Besides, our proposed framework also shows a significant improvement in terms of travel time as compared to the conventional one.

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Resilient Reinforcement in Secure State Estimation against Sensor Attacks with a priori Information

T Shinohara, T Namerikawa, Z Qu

IEEE Transactions on Automatic Control (IEEE Transactions on Automatic Control) 64 ( 12 ) 5024 - 5038 2019.12

Research paper (scientific journal), Joint Work, Accepted, ISSN 00189286

© 1963-2012 IEEE. Recent control systems severe depend on information technology infrastructures, especially the Internet of things (IoT) devices, which create many opportunities for the interaction between the physical world and cyberspace. Due to the tight connection, however, cyber attacks have the potential to generate evil consequences for the physical entities, and therefore, securing control systems is a vital issue to be addressed for building smart societies. To this end, this paper especially deals with the state estimation problem in the presence of malicious sensor attacks. Unlike the existing work, in this paper, we consider the problem with a priori information of the state to be estimated. Specifically, we address three prior knowledge - the sparsity information, (\alpha, \bar{n}_0)-sparsity information, and side information, and in each scenario, we show that the state can be reconstructed even if more sensors are compromised. This implies that the prior information reinforces the system resilience against malicious sensor attacks. Then, an estimator under sensor attacks considering the information is developed and, under a certain condition, the estimator can be relaxed into a tractable convex optimization problem. Further, we extend this analysis to systems in the presence of measurement noises, and it is shown that the prior information reduces the state-estimation error caused by the noise. The numerical simulations in a diffusion process finally illustrate the reinforcement and error-reduction results with the information.

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Optimal coordination and control of connected and automated vehicles at intersections via mixed integer linear programming

MHM NOR, T Namerikawa

SICE Journal of Control, Measurement, and System Integration (公益社団法人計測自動制御学会) 12 ( 6 ) 215 - 222 2019.11

Research paper (scientific journal), Joint Work, Accepted, ISSN 1882-4889

This paper addresses the crossing order problem for connected and automated vehicles at an intersection where the problem can lead to higher traffic congestion, especially in high traffic density. First, we formulated the problem with mixed integer linear programming where the solution yields optimal crossing time for each vehicle to cross the intersection. Then, we present an optimization framework to drive each vehicle towards the assigned crossing time while conserving the fuel consumption. Finally, we simulate the intersection scenario with the proposed solution, where it is shown that both congestion and fuel consumption can be reduced significantly.

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Integrating Demand Response Aggregators with Negawatt Trading Mechanisms in Electricity Markets

A Algarni, T Namerikawa

2019 North American Power Symposium (NAPS), 1-5 (51st North American Power Symposium, NAPS 2019) 2019.10

Research paper (international conference proceedings), Joint Work

© 2019 IEEE. In this paper, we propose a distributed power supply-demand management mechanism in a real-time market by utilizing various resources including demand reduction, balancing generators, and power flow changes. The proposed method aims to minimize the cost of balancing the supply with the demand. We simulate the results on the IEEJ EAST 30-machine system with four connected areas. The results show the feasibility of the proposed method and the establishment of a relationship between the demand response (DR) aggregator and the independent system operator (ISO).

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Traffic Signal Control Considering Switching Timing via Distributed Model Predictive Control

S Sasaki, T Namerikawa

2019 19th International Conference on Control, Automation and Systems (ICCAS … (International Conference on Control, Automation and Systems) 2019-October 286 - 291 2019.10

Research paper (international conference proceedings), Joint Work, Accepted

© 2019 Institute of Control, Robotics and Systems - ICROS. In this paper, we aim to alleviate traffic congestion on each link online by traffic signal control for local road transport network to be controlled. We first propose a transportation model with time delay system considering the travel time of the vehicle and calculate the optimal signal phases with variable cycle length and offset using distributed model predictive control for that model to reduce congestion rate on local roads. We then explain algorithms for distributed control that each intersection solves the optimization problem in parallel sharing information only with adjacent intersection. Finally, the effectiveness of the proposed method is confirmed by numerical simulation.

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Parking Lot Allocation Based on Matching Theory using Prediction-based Optimal Vehicle Routing

T Nakazato, T Namerikawa

2019 19th International Conference on Control, Automation and Systems (ICCAS … (International Conference on Control, Automation and Systems) 2019-October 1004 - 1009 2019.10

Research paper (international conference proceedings), Joint Work, Accepted

© 2019 Institute of Control, Robotics and Systems - ICROS. The purpose of this paper is to relax traffic congestion caused by the decrease in vehicle speed in case of a driver searching for a parking lot in traffic network, by using Smart Parking System. Smart Parking System is a system that allocates parking lot considering both the driver's preference and the parking lot manager's preference in advance. At that time, driver should know the optimal route and its time from currrent location to each parking lot. In this paper, Smart Parking System consider the road condition in traffic network and present the driver time by the optimal route from the driver's current location to each parking lot. Then, the parking lot allocation is determined by using matching theory considering both the driver's preference including information of optimal route and its time and the parking lot manager's preference. Finally, the effectiveness is confirmed by numerical simulations and results of parking lot allocation using optimal vehicle routing.

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