Published papers

This paper proposes a novel distributed probabilistic pose graph filtering framework on SE(3). It integrates ADMM with Stein Variational Gradient Descent (SVGD) to address multi-modal, non-Gaussian posteriors in multi-robot SLAM. We reformulate pose graph filtering as Bayesian posterior estimation on SE(3) using particles. The consistency requirement, minimizing KL divergence, is cast into an augmented Lagrangian and decomposed via ADMM. Agents perform local SVGD updates and consensus steps, communicating only summary statistics. The proposed method (i) maintains multiple hypotheses for ambiguous loop closures, (ii) reduces communication by exchanging only Lagrange multipliers and particle summaries, and (iii) handles SE(3) nonlinearity. Convergence is proven under standard ADMM conditions, and KL minimization is validated. Simulations demonstrate real-time convergence, outlier robustness, and accurate posterior approximation.

This paper develops and validates a resilient formation control for multi-UAV systems under agent failures. Building on our previous work, we extend resilient formation control that tolerates non-compensable faults and enables leader replacement to second-order UAV dynamics, and provide a theoretical stability analysis of the proposed control law. To address leader failures, we integrate a coordinated leader replacement algorithm with the resilient controller. The proposed scheme is validated through numerical simulations and multi-UAV flight experiments on small quadrotors. The results demonstrate that the system preserves formation even under severe leader failures, while the non-faulty agents asymptotically maintain consensus. These theoretical, simulation, and experimental results confirm the effectiveness of the proposed approach and highlight its practical applicability for resilient multi-agent systems.

This study deals with ride-sharing systems, which are expected to alleviate traffic congestion and reduce carbon dioxide emissions. Although ride-sharing systems have been defined variously in the literature, this study assumes that individual passengers share a vehicle for transportation and split the cost of transportation, such as gasoline, tolls, and parking fees, with other passengers who have similar transportation needs. In this study, the combination of multiple passengers is determined by matching using hypergraph. Then, the amount paid by passengers is determined as the Nash bargaining solution to design a price that takes into account passengers' preferences regarding time and fare. Through simulation verification, it is confirmed that the combination of multiple passengers reduces carbon dioxide emissions and drivers, and that the design of the payment amount using the Nash bargaining solution guarantees the gain for the passengers.

Decarbonization, decentralization, and digitalization of power systems have been rapidly promoted, but these trends also pose challenges in maintaining supply–demand balance and frequency stability under false data injection attacks (FDIAs). This paper proposes a resilient load frequency control (LFC) system for multi-area power systems capable of maintaining stable power system operation under FDIAs. Specifically, the controller is designed based on a distributed consensus framework with auxiliary variables to suppress system frequency deviations in the presence of FDIAs. It is shown that the proposed system achieves supply–demand balance through consensus on power generation commands, and guarantees Lyapunov stability of the closed-loop system even in the presence of FDIAs. Additionally, our scheme is capable of mitigating any uniformly bounded FDIAs without limitations on the number or locations of compromised controllers, and without relying on attack detection or estimation mechanisms. Finally, the effectiveness of the proposed approach is verified through numerical simulations using the IEEJ 30-machine system model.

This paper considers Day-Ahead Market with battery and accumulator to level power generation. First,we model consumer with battery, generator with battery and accumulator to maximize their own profit. Then, the optimal pricing algorithm based on dual decomposition and Steepest Descent Method and proof the stability of the algorithm is proposed. Finally, we show the effectiveness of the algorithm with some numerical simulations which indicate that the proposed algorithm achieves the demand-supply balancing.

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.

© 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.

© 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.

© 2019 American Automatic Control Council. In this paper, we present a discussion on smart parking systems in urban traffic networks. Reduced vehicle speed when drivers search for parking lots contributes to increased traffic jams in recent urban traffic networks. We aim to shorten the searching time for parking lots, which is one of the causes of traffic jams, by allocating available parking lots to drivers. Furthermore, we design a dynamic parking fee system and redistribute parking lots to equalize the profits earned by managers of multiple parking lots in traffic-congested areas. We then finally confirm the effectiveness of the proposed algorithm through numerical simulations.

© 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.

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.

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.

This paper proposes a novel direct load control method to stabilize the system frequency. The proposed method utilizes the consumers' controllable loads for load frequency control. By solving an optimization problem to maximize the social welfare function, the input required for the frequency control is allocated among the controllable loads and the load frequency control generator in an economically rational manner. To introduce the direct control program for smooth operation of the power system, preventing consumers from cheating and guaranteeing their benefits are required. Thus, strategy-proofness and individual rationality based on the Groves and Clarke mechanism were used to design the electricity charge. Each consumer bids on the utility functions truthfully to the independent system operator and gains from participating in the proposed direct load control program. The effectiveness of the proposed method was demonstrated through numerical simulations.

In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NOx emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research.

This study deals with power demand-supply management based on negawatt trading. The aim of this study is to minimize the social cost of negawatt trading and maintain a demand-supply balance. Power consumers and independent system operators participate in the electricity market and each consumer acts based on regret matching, which is one of the learning algorithms. Furthermore, we consider strategy-proofness and individual rationality using VCG mechanism design. In this study, we propose a novel negawatt trading procedure based on regret matching and VCG mechanism design. Finally, the simulation results confirm the effectiveness of the proposed algorithm.

This paper deals with a predictive control and estimation problem with information structured constraints in a constrained finite-time optimal control framework. A large-scale system with information structures is defined as a system in which each subsystem collects spatio-temporally different information, and in general it is necessary to collect and process information in a distributed fashion. We propose a novel predictive control scheme and an estimation law with local information that each distributed subsystem obtains. The effectiveness of the proposed control and estimation law is evaluated through numerical simulations of a simplified micro grid.

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.