Published papers

This paper focuses on a new Force-Reflection (FR) algorithm based on input-to-state stability (ISS) small gain theorem in cooperative work of Single Master-Multiple Slave (SMMS) teleoperation system with time varying communication delay. In this work, we propose a new position tracking control for object grasping and a new FR algorithm based on a PD control to transfer the positions, velocities and force information between both sides of teleoperation. The goals of these methods are to achieve the secure grasping by multiple slave robots and improve the tracking performance of the cooperation SMMS teleoperation system. To analyze stability of the system, the ISS small gain approach is used to show the overall force-reflecting teleoperation to be input-to-state stable. Several experimental results show the effectiveness of our proposed method.

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.

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.

We present a longitudinal electrical conductivity calculation method for disordered alloys applicable from a wide range of density functional theory (DFT) codes based on the first-principles Wannier-based coherent potential approximation (Wannier-CPA). For evaluation of electrical conductivity, we employ two complementary methods; the Kubo-Greenwood formula and numerical analytic continuation of the current-current correlation function. We apply the developed method to Ag-Pd alloys and find that the results obtained by the Wannier-CPA reasonably reproduce previous studies by the well-established CPA implementation based on the Korringa-Kohn-Rostoker Green's function method (KKR-CPA).

To date, multicopters have mainly been used in the entertainment and information-gathering fields, but can potentially be used to transport payloads. Specifically, multicopters are more suited for manoeuvring tough terrains and delivering payloads to areas unaccessible by other means. The authors propose a control method to achieve cooperative transport with a plural multicopter system and address the problem of delivering a payload being suspended via cables from multicopters. In cooperative transport using a multicopter system, disturbance (weight, wind, and inertia force) may cause destabilization. A consensus algorithm is used to coordinate flight of helicopters in a formation and to ensure that the payload follows a virtual leader. Disturbances (such as wind and cable tension) are primary causes of destabilization and, in the multicopter setting, they are suppressed by an adaptive robust control, Robust Integral of Sign of the Error control. It is shown that the proposed control design can achieve not only convergence under the ideal setting but also robust operation under significant disturbances. In addition, additional control objectives such as cable tension, payload attitude, and collision avoidance can be integrated and achieved. As an auxiliary method, cable tension control, maintaining a horizontal payload attitude, and collision avoidance are considered. The usefulness of the proposed method was verified by simulations and laboratory flight testing demonstrations.

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.

This paper discusses cyber attacks and faults diagnosis method for networked electrical power systems. In this paper, a power system is divided into some areas, and then, some parameters are estimated by Kalman filter in each area. Then we propose a diagnosis method by using called state and output fault diagnosis matrices which are composed of the estimated values, output matrix and so on. These diagnosis matrices can immediately judge which nodes of which area is attacked. Finally, some simulation results are shown to validate the proposed approach.

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.

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.

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.

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.

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.

In this study, we propose a distributed search method for multiple targets in In this study, we propose a distributed search method for multiple targets in a two-dimensional space using HEDAC(Heat Equation Driven Area Coverage). HEDAC has been recently used to control multi-agent systems. In contrast to the conventional centralized HEDAC, this paper proposes a decentralized algorithm that uses probability maps to make it more practical. Specifically, each search agent shares the probability of target detection by its own search as a probability map with other agents, and is controlled along the gradient of the potential field created based on this map, enabling it to conduct its search activities in a distributed and efficient way. The contribution of this paper is to improve the flexibility of conventional search methods by decentralizing them. Finally, we confirm the effectiveness of the proposed method through simulation validation.

In this research, we solve the task assignment problem of multiple agents to multiple tasks in a multi-agent system. Regarding the problem of one agent versus multiple tasks, we use the concept of "grouping,"in which each agent seeks a set of tasks that it can handle at once. On the other hand, for the problem of multiple agents versus one task, we use the concept of the "Timetable"with the goal of synchronizing the start time of each agent's task. By combining the two concepts, it is possible to deal with complex assignment problems, such as each agent executing multiple tasks at the same time, or multiple agents executing one task at the same time. Furthermore, we show that this solution can respond if circumstances changes, such as when the number of agents decreases or the number of tasks increases.