Details of a Researcher - Namerikawa, Toru

Papers - Namerikawa, Toru

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Safe cooperative control of CAVs at signal-free intersection under realistic scenarios

Y Nakai, T Namerikawa

SICE Journal of Control, Measurement, and System Integration (The Society of Instrument and Control Engineers ) 17 ( 1 ) 106 - 121 2024.04

Research paper (scientific journal), Joint Work, Last author, Accepted

This research deals with the cooperative control of CAVs (Connected and Automated Vehicles) at signal-free intersection. CAVs communicate with each other and adjust their speeds to safely pass through the intersection without having to stop. This is expected to improve intersection throughput and reduce fuel consumption. In order to execute cooperative control, a two-stage control structure is considered. In the first stage, the merging time, defined for each CAV as the time at which the CAV reaches the intersection, is obtained by solving mixed integer linear programming (MILP). In the second stage, each CAV solves an optimal control problem to determine the control input that allows it to reach the intersection at the merging time obtained in the first stage. In this research, a nonlinear model that considers air resistance, rolling resistance, and slope resistance is used to take into account the real environment. We propose a method using a control barrier function to guarantee the safety of CAVs even with nonlinear models, and an input correction method using a disturbance observer to suppress modelling errors caused by complex dynamics. We also propose a method that consists of establishing a speed regulation zone to avoid the possibility of passing through an intersection at an unsafe speed to improve throughput. Finally, a comparison with previous studies shows the superiority of the proposed method in reducing fuel consumption.

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Passivity-Short-Based Conditions of System-Wide Stability and L2 gain Performance for Heterogeneous Nonlinear Power System

H Kang, T Namerikawa

2024 SICE International Symposium on Control Systems (SICE ISCS), 94-98 2024.03

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

Optimal Resilient Sensor Placement Problem for Secure State Estimation

Shinohara Takumi, Namerikawa Toru

Proceedings of the Japan Joint Automatic Control Conference (The Japan Joint Automatic Control Conference) 160 ( 0 ) 140 - 142 2024.02

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 00051098

In this paper, we consider a sensor placement problem for secure state estimation in an adversarial environment. Specifically, this paper deals with a system consisting of n agents where up to l sensor measurements are potentially compromised by malicious adversaries and tackles the problem of sensor placement such that the system state can be reconstructed even if the measurements are compromised. However, since it is costly to deploy sensors for all agents, we also aim to place sensors at the lowest possible cost. We call this problem the optimal resilient sensor placement problem (ORSPP) and show that this problem is coNP-hard. Therefore, it is, in general, impossible to compute the ORSPP in polynomial time unless P=coNP. On the positive side, however, we also provide that this problem can be solved in polynomial time if the network structure of the agents satisfies certain conditions. The concrete algorithm for the ORSPP is provided, and further, in the process of providing the coNP-hardness, we also derive a new form of a necessary and sufficient condition for secure state estimation. Finally, we show the results of numerical simulations using a random graph and a diffusion process.

Consistent event-triggered consensus on complete graphs

DJ Antunes, D Meister, T Namerikawa, F Allgöwer, W Heemels

2023 62nd IEEE Conference on Decision and Control (CDC), 3911-3916 (Proceedings of the IEEE Conference on Decision and Control) 3911 - 3916 2023.12

Research paper (international conference proceedings), Joint Work, Accepted, ISSN 07431546

This paper starts by considering an optimal control formulation of the consensus problem on complete graphs with a cost capturing disagreement and agents modeled by integrators. An optimal control policy for this problem is shown to be the well-known consensus algorithm by which each agent resets its state to the average of its and other agents' state values received at every time step. The framework is extended to the case where agents can only exchange information periodically, with a period larger than one. Then an event-triggered control strategy is proposed that results in a better cost than that of the optimal periodic one with the same average transmission rate, that is, it is consistent. According to this strategy, each agent distributedly transmits its state if the error between its current state and a common consensus estimate based on previously transmitted agents' data exceeds a threshold. Simulation results are presented to illustrate the proposed strategy.

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Decentralized Optimal Parking Lot Allocation via Dynamic Parking Fee

Y Fujimaki, T Namerikawa

2023 28th Asia Pacific Conference on Communications (APCC), 261-266 (Proceedings - 2023 28th Asia Pacific Conference on Communications, APCC 2023) 261 - 266 2023.11

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

This paper proposes a novel decentralized optimal parking lot allocation algorithm by using dynamic parking fee. Information about drivers and parking lots used to be collected by a system manager in smart parking systems, and the allocation is determined centrally. However, the computational load increases exponentially as the target area expands. Therefore, a decentralized system is needed that can flexibly respond to the expansion of the area size, in which the allocation is determined by communicating the results of the calculations performed by each participant. In building a decentralized system, it is desirable for the system to be fair and efficient. Therefore, we propose a decentralized parking system that satisfies this property by using matching theory, a field that studies how people and things should be matched in a market. We propose a unique matching method that takes into account the difference between drivers as consumers and parking service providers. We also propose a reallocation method based on dynamic parking fee to reduce the number of unallocated drivers.

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Battery Management of Microgrids Based on Scenario Optimization

S Wakabayashi, T Namerikawa

2023 62nd Annual Conference of the Society of Instrument and Control … (2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023) 1248 - 1253 2023.09

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

This paper proposes an optimal battery management methodology in microgrids. Every microgrid gives other microgrids a fair deal of electric power based on Generalized Nash Bargaining Solution, which is a kind of optimal solutions of a fair trade in game theory. In this paper, we use a method of scenario optimization in order to take optimal and safe estimates for generations from renewable energy sources. First, many scenarios of the amount of generation are prepared. Then, we calculate the total cost of microgrids for each scenario and find the highest cost. We determine the strategy which corresponds to the highest-cost-scenario as each microgrid's final strategy. Through this method, the number of violations of the limitations of the batteries and the extra cost caused by those violations has been reduced. Power flow in networks which connect each microgrid and an electric power company is also considered.

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Robust Energy Management Using Tube-Based Model Predictive Control for a Building with Solar Power and Batteries

K Takano, T Namerikawa

2023 62nd Annual Conference of the Society of Instrument and Control … (2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023) 1344 - 1349 2023.09

Research paper (international conference proceedings), Accepted

The power generation from renewable energy sources, which is being promoted for mass adoption due to environmental issues, is affected by weather conditions, and therefore, it is necessary to predict the amount of power generation. In this study, we propose an algorithm for BEMS that takes into account the uncertainty of solar power generation using Tube-Based MPC, which is a type of robust MPC. We confirm through simulations that the algorithm is more robust against forecast errors by comparing it with a conventional method [3].

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Passivity-Short-based Stability Analysis of Load Frequency Control System with Demand Response

Y Shibata, T Namerikawa

2023 62nd Annual Conference of the Society of Instrument and Control … (2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023) 1232 - 1237 2023.09

Research paper (international conference proceedings), Accepted

This paper deal with a stability analysis for a load frequency control (LFC) system including Demand Response (DR). To analyze system stability, LFC system is formulated to facilitate the application of passivity theory, and the passivity and passivity-short of the system are analyzed. At first, we express LFC system decomposing into a network system and area systems. Specifically, area systems include DR that occurs time-varying communication delay. Then, passivity analysis is applied to each system. As a result, we can clarify the area systems are output strictly passive if certain inequalities are satisfied, and the network system is passive. Simulation results show that the frequency deviation converges to a constant value under the derived inequality condition.

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Cooperative Control of CAVs Using Control Barrier Function at Signal-free Intersection Considering Real Environment

Y Nakai, T Namerikawa

2023 62nd Annual Conference of the Society of Instrument and Control … (2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023) 1001 - 1006 2023.09

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

This study addresses the cooperative control of CAVs(Connected and Automated Vehicles) at Signal-free Intersection. The purpose is to reduce fuel consumption by crossing intersection without stopping as much as possible while ensuring safety. For this problem, We consider a two-stage control structure.The first stage is to calculate the merging time of each CAV by solving mixed integer linear programming (MILP). In the second stage, each CAV solves the optimal control problem and determines the input in time for the merging time. In this study, We propose an optimal control method using control barrier function considering more realistic scenario, and a speed regulation zone to cross the intersection at a pre-determined terminal speed. By using input corrections and trajectory tracking of speed and position, simulation results show that fuel consumption can be reduced while ensuring safety. Finally, compared to conventional research, the system achieves low fuel consumption, and demonstrating its superiority.

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Robust Model Predictive Control of Large-Scale Power Network with Moving Horizon Estimation

T Namerikawa

2023 IEEE Conference on Control Technology and Applications (CCTA), 823-828 (2023 IEEE Conference on Control Technology and Applications, CCTA 2023) 823 - 828 2023.08

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

In this paper, we discuss the load frequency control problem for large-scale power network systems. The control is conducted to preserve the demand-supply power balance by controlling the output of generators in a power network. However, it becomes difficult to retain the balance because of the injection of a large amount of renewable energy whose output is uncertain. This paper proposes a new control application framework, especially focused on a large-scale connected power network system that contains a large-volume wind turbine generator, thermal turbine generator, and battery. More concretely, to satisfy the constraints of state and input control, we propose a method of load frequency control using moving horizon estimation and robust model predictive control.

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Command Value Determination Considering Generation Cost and Control Performance Using Model Predictive Control in Load Frequency Control

D Sano, T Namerikawa

IFAC-PapersOnLine 56 (2), 1534-1539 (IFAC-PapersOnLine) 56 ( 2 ) 1534 - 1539 2023.07

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

This paper deals with load frequency control (LFC) problem for power system. The control is conducted to retain power demand supply balance by controlling output of generators in power network. In order to realize LFC with low power generation cost while maintaining control performance, this paper proposes frequency control method using Moving Horizon Estimation (MHE) and Model Predictive Control (MPC). First, MHE is used to estimate the state of the system. Next, optimization calculation is performed using MPC to determine the command values for each generator. Finally, simulation of load frequency control is carried out to verify the effectiveness of the proposed method.

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Reducing Air Pollutant Emissions and Optimizing Fuel Economy by Controlling Torque and Catalyst warm-up in mild HEV via Cascaded MPC

Y Umezawa, H Seto, T Imamura, T Namerikawa

IFAC-PapersOnLine 56 (2), 670-675 (IFAC-PapersOnLine) 56 ( 2 ) 670 - 675 2023.07

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

In this paper, Cascaded MPC (Model Predictive Control) including Supervisory MPC, Heating MPC and Torque MPC is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between Heaitng MPC and Torque MPC, which are connected via the battery. Supervisory MPC treats the power and temperature systems comprehensively and calculates reference values for NOx emissions to be tracked in Heating MPC. In Heating MPC, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At Torque MPC, the optimal torque distribution for the engine and motor is determined. Finally, the effectiveness of our proposed method is demonstrated by simulations.

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Distributed Route Optimization for Mixed Package-Passenger System Incorporating Ridesharing Passenger Matching

Yamamoto Eri, Itoh Yutaro, Ikemoto Noriaki, Namerikawa Toru

Proceedings of the Japan Joint Automatic Control Conference (The Japan Joint Automatic Control Conference) 56 ( 2 ) 2365 - 2370 2023.07

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

This paper proposes an algorithm for optimizing the route in a mixed package-passenger system where taxicab drivers, who transport passengers, are assigned the task of delivering packages. The main aim is to address the shortage of package delivery drivers and to reduce the amount of CO2 emitted by delivery vehicles. The paper also proposes a stable ridesharing matching scheme, which reduces fares for taxicab passengers. Furthermore, the problem is formulated as a mixed integer linear programming problem to identify the optimal route and driver task assignments. A distributed method based on Lagrangian relaxation is proposed to reduce the computational effort. The paper concludes by demonstrating the effectiveness of the proposed algorithm through numerical simulations.

Decentralized optimal parking lot allocation by static and dynamic two-stage matching

Y Fujimaki, Y Shibata, R Namerikawa, T Namerikawa

IFAC-PapersOnLine 56 (2), 689-694 (IFAC-PapersOnLine) 56 ( 2 ) 689 - 694 2023.07

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

In this paper, we propose an optimal parking lot allocation method based on matching theory that takes into account the desires of both drivers searching for parking lots and parking lot managers. In the conventional method, the allocation calculation is performed centrally, making it difficult to expand the area. We propose a decentralized allocation method that combines static and dynamic matching. In this method, individual drivers and parking lots perform calculations, and the allocation is determined in a decentralized manner through information communication, making it possible to expand the area. Finally, we confirm the effectiveness of the proposed method through numerical simulations.

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Secure State Estimation for Multi-Agent Systems: On the Relationship between the Number of Agents and System Resilience

T Shinohara, T Namerikawa

2023 American Control Conference (ACC), 1006-1011 (Proceedings of the American Control Conference) 2023-May 1006 - 1011 2023.06

Research paper (international conference proceedings), Joint Work, Accepted, ISSN 07431619

This paper addresses the problem of secure state estimation in multi-agent systems consisting of n agents. For the problem, the sparse observability index is a well-known measure of the system resilience. This index is defined as the largest integer δ for which the system observability is preserved even if any δ sensors are eliminated. The larger δ is, the more the system state can be reconstructed even if the system is subjected to more sensor attacks. In this paper, we analyze the relationship between the number of agents n and the sparse observability index δ especially when the network structure is path and cycle graphs. Intuitively, it is assumed that, as the number of agents n increases, the system resilience, i.e., δ, increases accordingly. However, as this study provides, δ does not monotonically increase with an increase in n. In path graphs, δ depends on whether n+1 is prime or composite, and the system resilience is enhanced when n+1 is prime. In cycle graphs, δ obeys whether n is prime or composite, and the system is more resilient when n is prime.

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Safe exploration method for reinforcement learning under existence of disturbance

Yoshihiro Okawa, Tomotake Sasaki, Hitoshi Yanami, Toru Namerikawa

Joint European Conference on Machine Learning and Knowledge Discovery in … (Springer Nature) 13716 LNAI 132 - 147 2023.03

Research paper (international conference proceedings), Joint Work, Accepted, ISSN 03029743

Recent rapid developments in reinforcement learning algorithms have been giving us novel possibilities in many fields. However, due to their exploring property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal with a safe exploration problem in reinforcement learning under the existence of disturbance. We define the safety during learning as satisfaction of the constraint conditions explicitly defined in terms of the state and propose a safe exploration method that uses partial prior knowledge of a controlled object and disturbance. The proposed method assures the satisfaction of the explicit state constraints with a pre-specified probability even if the controlled object is exposed to a stochastic disturbance following a normal distribution. As theoretical results, we introduce sufficient conditions to construct conservative inputs not containing an exploring aspect used in the proposed method and prove that the safety in the above explained sense is guaranteed with the proposed method. Furthermore, we illustrate the validity and effectiveness of the proposed method through numerical simulations of an inverted pendulum and a four-bar parallel link robot manipulator.

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Optimal Parking Lot Allocation Considering Demand Situation

FUJIMAKI Yuto, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 59 ( 3 ) 176 - 182 2023.03

Research paper (scientific journal), Joint Work, Corresponding author, Accepted, ISSN 04534654

<p>In this paper, we propose a new smart parking system that determines the optimal parking allocation in the long run. In this system, the demand situation is acquired earlier than the respective parking start time by motivating drivers to apply for early parking, and the system performs long-term optimization instead of optimization only for the current time. We propose three optimization methods focusing on the time axis. The first one ignores the time axis constraint, the second one has the time axis constraint but lacks driver motivation, and the third one is the proposed method that solves those problems. Finally, numerical simulations will be used to confirm the effectiveness of the proposed method.</p>

Mild HEVにおけるトルクおよび触媒暖機制御によるNOxの排出低減と燃費最適化

UMEZAWA Yuka, SETO Hiroki, IMAMURA Toshiro, NAMERIKAWA Toru

Transactions of the Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 59 ( 3 ) 110 - 120 2023.03

Research paper (scientific journal), Joint Work, Corresponding author, Accepted, ISSN 04534654

<p>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.</p>

Construction of One-Way Car Sharing System Considering Coalition Rationality based on Mechanism Design

Shun Sasaki, Eri Yamamoto, and Toru Namerikawa

IEEE Transactions on Control of Network Systems (IEEE Transactions on Control of Network Systems) 9 ( 4 ) 1655 - 1665 2022.12

Research paper (scientific journal), Joint Work, Corresponding author, Accepted, ISSN 2325-5870

This paper proposes a control algorithm for one-way car sharing. The problem with one-way car sharing is that the origin and the destination parking lots, tend to be biased because user traffic demands, and the cost of relocating the vehicle to increase the operating rate is high. To solve this problem, we propose providing incentives to users in the form of discounts and also changing the parking lots and departure times to prevent bias in the number of vehicles in the parking lots. In this study, based on the Vickrey-Clarke-Groves (VCG) mechanism design, an incentive design was created to satisfy desirable properties, such as strategy-proofness, individual rationality, and coalition rationality. In the proposed one-way car sharing system, the system manager decides the proposal based on the information reported by the users. Therefore, satisfying these properties not only guarantees not only that the users will benefit from agreeing to the proposal, but also that even in case of a false declaration or shill bidding, the discount will not be unreasonably profitable. Finally, the increase in demand acceptance by applying the proposed method was confirmed by numerical simulation.

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Parking Lot Allocation Using Rematching and Dynamic Parking Fee Design

Takuya Nakazato, Yuto Fujimaki and Toru Namerikawa

IEEE Transactions on Control of Network Systems (IEEE Transactions on Control of Network Systems) 9 ( 4 ) 1692 - 1703 2022.12

Research paper (scientific journal), Joint Work, Corresponding author, Accepted, ISSN 2325-5870

In this study, we propose a novel smart-parking system that allocates parking lots based on the matching theory, considering both the drivers and parking managers preferences. Some drivers may be unable to be allocated to parking lots because of the high traffic volume in urban areas that makes many parking lots full. Therefore, we propose a prior reservation system that can reallocate parking lots considering the waiting time until vacant parking spaces appear. The drivers preference is redetermined considering the waiting time obtained by both the parked drivers and reallocated drivers information. We also present a parking lot allocation algorithm, including rematching, and discuss stability and strategy-proofness, which are important properties of matching. Furthermore, we propose a dynamic parking fee design, considering different parking statuses. By combining the price increase with the price decrease based on parking status, the proposed dynamic parking fee aims not only to level out parking utilization, but also to secure the parking managers profit. Finally, numerical simulations confirm the effectiveness of the proposed method.

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