Details of a Researcher - Namerikawa, Toru

Papers - Namerikawa, Toru

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Merging of Connected and Automated Vehicles at Roundabout Using Model Predictive Control

MHBM Nor, T Namerikawa

2018 57th Annual Conference of the Society of Instrument and Control … (2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2018) 272 - 277 2018.09

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

© 2018 The Society of Instrument and Control Enginners - SICE. This paper addresses a merging problem of connected and automated vehicles at single lane with two different freeways roundabout. First, we assume that the merging vehicle already knew the environment of the roadway beforehand. After that, we formulate the optimization problem based on model predictive control framework which includes lane keeping and collision avoidance. Finally, we show in the simulation results that the proposed solution is capable of merging the vehicle at the roundabout effectively.

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Event-Triggered Formation Control of a Generalized Multi-Agent System

R Toyota, T Namerikawa

2018 57th Annual Conference of the Society of Instrument and Control … (2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2018) 940 - 945 2018.09

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

© 2018 The Society of Instrument and Control Enginners - SICE. In this paper, we propose an event-triggered formation control algorithm for a multi-agent system consists of linear discrete agents in order to reduce energy consumption due to unnecessary calculation and communication of agents. By using the event-triggered protocol, which updates the control input aperiodically only when certain triggering condition is satisfied, calculation and communication frequency are reduced. Also the multi-agent system is guaranteed an almost sure stability by determining the control input update timing by the triggering condition which is based on Lyapunov's stability theorem. Finally we verify the effectiveness of the proposed algorithm by numerical simulation.

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Passivity-Short-based Stability Analysis on Electricity Market Trading System Considering Negative Price

K Muto, T Namerikawa, Z Qu

2018 IEEE Conference on Control Technology and Applications (CCTA), 418-423 (2018 IEEE Conference on Control Technology and Applications, CCTA 2018) 418 - 423 2018.08

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

© 2018 IEEE. This paper deals with a distributed decision method for determining the electricity price considering the negative price in the market trading. In addition, we analyze the stability and convergence properties for the market trading system using the concept of passivity-short. At first, mathematical models of market participants' behavior which maximize their own profits are discussed. Then, we construct a trading system based on dynamic pricing considering plug and play operation that each market participant intermittently participates in market trading. As for the optimal electricity price, even if the optimal price might be negative, we indicate the stability of the system can be guaranteed by using passivity-short which is a relaxed concept of passivity. Finally, numerical simulation results illustrate that each value decided by the market trading converges to the optimal value.

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Coordinated Optimal Control of Constrained DERs

R Harvey, Z Qu, T Namerikawa

2018 IEEE Conference on Control Technology and Applications (CCTA), 224-229 (2018 IEEE Conference on Control Technology and Applications, CCTA 2018) 224 - 229 2018.08

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

© 2018 IEEE. In this paper, a coordinated optimal control is designed to simultaneously satisfy multiple power system objectives at the distribution network level. With high penetration of renewables, traditional generation and control may not be enough to compensate for intermittent changes in net load. Dynamic coordination of battery storage devices and demand response at aggregated distributed energy resource (DER) sites becomes necessary to maintain power system operation, however, each dynamic power component operates at different time scales. Frequency stabilization can be achieved by designing optimal controls for traditional generation, storage devices and demand response. The proposed optimal controls naturally render frequency separation of these load balancing mechanisms. Given a 24-hour net load forecast, optimal tracking control inputs are designed subject to constraints of selected system elements to ensure optimal performance of the power system. Simulation results are presented and discussed.

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Load Frequency Control for Power System Using Moving Horizon Estimation and Robust Model Predictive Control

HIRANAKA Takuto, SEKINE Tetsuya, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 54 ( 7 ) 606 - 616 2018.07

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

In this paper, load frequency control problem for power system is discussed. The control is conducted to retain power demand supply balance by controlling output of generators in power network. However, it becomes harder to retain the balance because of large volume injection of renewable energy whose output is uncertain. To resolve the problem, some approaches are considered in this research field. One is to use dispersion type power sources like battery in addition to conventional generators like thermal power generator and hydroelectric generator. Another is to apply new control theories and methods. The other is the load frequency control considering demand response by using a controllable load on the consumer side. Especially among them, in this paper, power system which holds large volume wind turbine generator, turbine generator and battery is focused on and new control method is proposed. Concretely, load frequency control based moving horizon estimation and robust model predictive control is proposed in order to satisfy the constraints of state and control input.

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Power Demand-supply Adjustment in Multi-area Power Grid Based on Negawatt Trading via Regret Matching and VCG Mechanism Design

NAGAMI Kentaro, MUTO Keita, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 54 ( 7 ) 617 - 625 2018.07

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

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.

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Scenario-Based Robust MPC for Energy Management Systems with Renewable Generators

S Sato, T Namerikawa

2018 37th Chinese Control Conference (CCC), 2304-2309 (Chinese Control Conference, CCC) 2018-July 2304 - 2309 2018.07

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

© 2018 Technical Committee on Control Theory, Chinese Association of Automation. This paper addresses photovoltaics (PV) power prediction and energy storage problem which are known to be a key technology in energy management systems (EMS). Extending results of the point prediction of PV power, we first describe a prediction interval (PI) method using a copula, which can express the relation between a multivariable joint distribution and each marginal distribution. Then, resorting to the PI method, the energy storage optimization problem in a building is developed. A scenario robust (SR) optimization theorem, which calculates the robustness of the optimal solution, is applied to the proposed PI method, and hence we obtain an optimal energy storage solution taking the robustness of the solution into account. Additionally, we propose a method which combines a model predictive control (MPC) technique and SR to reduce the total electricity costs. The simulation results finally illustrate the cost reduction and robustness of the proposed method.

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Merging control for automated vehicles using decentralized model predictive control

Y Hayashi, T Namerikawa

2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics … (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM) 2018-July 268 - 273 2018.07

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

© 2018 IEEE. Ahstract-This paper deals with merging control of automated vehicles using distributed model predictive control. 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 replanning 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 confirm the effectiveness of proposed methods through simulations.

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Reach Set-based Attack Resilient State Estimation against Omniscient Adversaries

T Shinohara, T Namerikawa

2018 Annual American Control Conference (ACC), 5813-5818 (Proceedings of the American Control Conference) 2018-June 5813 - 5818 2018.06

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

© 2018 AACC. We consider the problem of secure state estimation in an adversarial environment with the presence of bounded noises. We assume the adversary has the knowledge of the healthy measurements and system parameters. To countervail the dangerous attacker, the problem is given as a min-max optimization, that is, the system operator seeks an estimator which minimizes the worst-case estimation error due to the manipulation by the attacker. On the proposed estimator, the estimation error is bounded at all times even if the system removing an arbitrary set of 2l sensors is not observable, where l is the number of the compromised sensors. To this end, taking the reach set of the system into account, we first show the feasible set of the state can be represented as a union of polytopes, and the optimal estimate is given as the Chebyshev center of the union. Then, for calculating the optimal state estimate, we provide a convex optimization problem that utilizes the vertices of the union. Additionally, the upper bound of the worst-case estimation error is derived theoretically, and we also show a rigorous analytical bound under a certain condition. The attacked sensor identification

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Optimal power demand management by aggregators using matching theory

H Ikegami, T Namerikawa

2018 Annual American Control Conference (ACC), 4584-4589 (Proceedings of the American Control Conference) 2018-June 4584 - 4589 2018.06

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

© 2018 AACC. This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.

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Design considerations for river floating sensors

ZH Ismail, A Omairi, T Namerikawa, AI Cahyadi

ASEAN Engineering Journal 8 (1), 1-13 (ASEAN Engineering Journal) 8 ( 1 ) 1 - 13 2018.06

Research paper (scientific journal), Joint Work

© 2018 ASEAN University Network/Southeast Asia Engineering Education Development Network. All rights reserved. In order to utilize the flow of a river to generate power, it is crucial to develop a system that is able to obtain and record the river's characteristics in various aspects. A simple but effective method has to be created to allow researchers to collect and study the parameters of a river in order to predetermine the final build of the power generation model. A floating sensor that is able to collect and give a real-time data has been developed using readily available hardware as well as custom made parts. This paper describes a design methodology for river floating sensors and results from a case study performed in the actual controlled channel are presented to demonstrate the effectiveness of the design decisions.

Optimal Demand Adjustment of Consumers with Various Appliances Using Dynamic Pricing

K Muto, Y Okawa, T Namerikawa

SICE Journal of Control, Measurement, and System Integration 11 (3), 146-153 (公益社団法人計測自動制御学会) 11 ( 3 ) 146 - 153 2018.05

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

This paper deals with a demand adjustment problem of each consumer having appliances by an aggregator based on optimal pricing in a day-ahead electricity market. In this paper, we model consumers, generators, and aggregators and design a market mechanism, where they act to maximize their own profit based on the power price and decide the electricity supply and demand. The dual decomposition is applied to the market problem to maximize the social welfare, where the proposed algorithm decides an electricity price based on the exchange of information among market participants to solve each distributed problem for improving convergence. In addition, the convergence of this proposed method is proven using Lyapunov's stability theorem. Finally, simulation results show that matching of supply and demand is achieved, while satisfying constraints on the consumption amount and the use time of each appliance, and the price determined using proposed price update algorithm converges to a certain price.

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Consensus-Based Cooperative Formation Control for Multiquadcopter System With Unidirectional Network Connections

Namerikawa, Toru, Kuriki, Yasuhiro, Khalifa, Ahmed

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME (Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME) 140 ( 4 ) 2018.04

Research paper (scientific journal), Single Work, ISSN 0022-0434

In this paper, we consider cooperative control issues for a multiunmanned aerial vehicle (UAV) system. We propose a cooperative formation control strategy with unidirectional network connections between UAVs. Our strategy is to apply a consensus-based algorithm to the UAVs so that they can cooperatively fly in formation. First, we show that UAV models on the horizontal plane and in the vertical direction are expressed as a fourth- and secondorder system, respectively. Then, we show that the stability discriminants of the multi-UAV system on the horizontal plane and in the vertical direction are expressed as polynomials. For a network structure composed of bidirectional or unidirectional network connections under the assumption that the network has a directed spanning tree, we provide conditions for formation control gains such that all roots of the polynomials have negative real parts in order for the UAVs to asymptotically converge to the positions for a desired formation by using the generalized Routh stability criterion. The proposed control algorithms are validated through simulations, and experiments are performed on multiple commercial small UAVs to validate the proposed cont

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Perfect Stealthy Attacks in Cyber-physical Systems

Shinohara Takumi, NAMERIKAWA Toru

Transaction of SICE (The Society of Instrument and Control Engineers) 54 ( 3 ) 309 - 319 2018.03

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

Security of Cyber-Physical Systems (CPS) against cyber attacks is one of the most critical issues that should be investigated. In this paper, we deal with the vulnerabilities due to novel integrity cyber attacks named perfect stealthy attacks and the countermeasure against them in CPS which are modeled as a stochastic Linear Time-Invariant (LTI) system equipped with a Kalman filter, an LQG controller, and a χ2 failure detector. The attacks are designed by a sophisticated attacker so that the measurement residual of the compromised system coincides with the healthy one, and can ruin the system definitely. First, from an attacker's standpoint, we show an existence condition and a design procedure of the attacks using invariant subspace property. Then, on the other hand from a system supervisor's standpoint, the countermeasure against the malicious attacks using an aperiodic transmission scheme based on the Nash bargaining game is discussed. Finally, the dangerousness of the attacks and the effectiveness of the proposed detection method are verified through numerical simulations.

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Passivity-based stability analysis of dynamic electricity pricing with power flow

Okawa Y., Namerikawa T., Qu Z.

2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017) 2018-January 813 - 818 2017.12

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

© 2017 IEEE. This paper deals with passivity-based stability analysis of dynamic electricity pricing considering power flow. In the near future electricity market, power consumers and generators will participate in electricity market trading as market players. For such a market trading system, the dynamic pricing procedure is required to consider the intermittent participation of power consumers and generators. Then, this paper discusses the stability of the electricity market trading system including power flow using passivity analysis. This paper also shows that the optimal power demand, supply and electricity prices are asymptotically stable and these values are derived in a distributed manner through market trading.

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On the Vulnerabilities Due to Manipulative Zero-Stealthy Attacks in Cyber-Physical Systems

Takumi SHINOHARA, Toru NAMERIKAWA

SICE Journal of Control, Measurement, and System Integration (The Society of Instrument and Control Engineers) 10 ( 6 ) 563 - 570 2017.11

Research paper (scientific journal), Joint Work, Accepted

In this paper, we analyze the vulnerabilities due to integrity cyber attacks named zero-stealthy attacks in cyber-physical systems, which are modeled as a stochastic linear time invariant (LTI) system equipped with a Kalman filter, an LQG controller, and a χ2 failure detector. The attacks are designed by a sophisticated attacker so that the measurement residual of the compromised system coincides with the healthy one, and thus it is impossible to detect the attacks. First, we characterize and analyze an existence condition of the attacks from an attacker's standpoint. Then, we extend the attacks into an attacker's goal: The scenario when the adversary wishes to manipulate the systems to an objective designed by him/her. Our results provide that the attacker can manipulate the compromised system to the objective without accessing the networks of real-time sensor or actuator data. Finally, we verify the dangerousness of the attacks through a simple numerical example.

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Distributed Optimal Power Management via Negawatt Trading in Real-Time Electricity Market

Okawa Yoshihiro, Namerikawa Toru

IEEE Transactions on Smart Grid (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC) 8 ( 6 ) 3009 - 3019 2017.11

Research paper (scientific journal), Accepted, ISSN 1949-3053

This paper presents a distributed optimal power supply-demand management method by using negawatt trading in a real-time electricity market. In the negawatt trading, power consumers reduce their own power demand and receive monetary reward from the market operator. This paper discusses a power adjustment method via negawatt trading by including charge or discharge power from energy storage systems. In order to derive appropriate electricity incentive prices for consumers and to use energy storage systems in power grids efficiently, we deal with a power adjustment problem by using some future power supply information from generators. We then show a novel market algorithm to solve this problem in a distributed manner through market trading. Finally, numerical simulation results illustrate the effectiveness of our proposed method.

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Optimal Pricing of Electricity by Aggregator for Demand Adjustment of Each Consumer Having Appliances

Keita Muto, Yoshihiro Okawa, Toru Namerikawa

2017 56TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) (IEEE) 1246 - 1251 2017.09

Research paper (international conference proceedings), Accepted

This paper deals with demand adjustment problem of each consumer having appliances by an aggregator based on the optimal pricing problem in a day-ahead electricity market. In this paper, we model consumer, generator, aggregator and design a market mechanism and they act to maximize their own profit based on the power price and decide the electricity supply and demand. The dual decomposition is applied to market problem to maximize the social welfare and the proposed algorithm decides electricity price based on the exchange of information among market participants to solve each distributed problem for improving convergence. In addition, the convergence of this proposed method is proven using Lyapunov stability theorem. Finally, simulation results show that matching of supply and demand is sufficiently achieved.

Short-Term Wind Power Prediction for Wind Turbine via Kalman Filter Based on JIT Modeling

TOMOKI ISHIKAWA,1 TAKAAKI KOJIMA,1 and NAMERIKAWA Toru

Electrical Engineering in Japan 198 ( 3 ) 86 - 96 2017.02

Research paper (scientific journal), Joint Work, Accepted

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.

リチウムイオンバッテリ充電率推定のための周波数応答データを用いたパラメータ初期値推定

NAMERIKAWA Toru

計測自動制御学会論文集 53 ( 1 ) 112 - 120 2017.01

Research paper (scientific journal), Joint Work, Accepted

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.

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