論文

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.

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.

This paper deals with Multi-Robot SLAM for large scale map building. Specifically, each robot estimates a local map using EKF, and we merge these local maps into a global map. In this paper, we provide a new RLS based algorithm for map merging. First, we transform local maps into relative information which is considered as measurements for the global map. Then, we update the state estimate by RLS considering the weighting of measurements, which is determined by error propagation from the EKF SLAM. We prove the convergence of the error covariance matrix in this algorithm. In experimental results, we confirm the validity of the proposed algorithm and correctness of derived theorems for the convergence.

Recently, the Aerial Manipulation System becomes very attractive for a wide range of applications due to its unique features. However, control of such system is quite challenging. One of the critical challenge is that this system is very susceptible to actuators' faults. In this paper, a Passive Fault Tolerant Control System is proposed to address this issue with robust and optimal performance. The robustness is achieved using a linear Disturbance Observer (DOb) loop. Based on the linearization capability of DOb, a standard Model Predictive Control (MPC) is then used and the resulting control scheme is characterized by both a low computational load and optimal actuators' efforts with respect to conventional nonlinear robust solutions. This controller is tested to achieve the tracking of a point-to-point task space references under the effect of actuators' faults, picking/releasing a payload, changing the operating region, and measurement noise. Efficacy of the proposed technique is verified via numerical simulations.

This paper presents a novel optimal power demand management method for power consumers with an aggregator considering state and control constraints. The aggregator is an organization who manages some consumers to achieve demand response efficiently. This paper proposes a power demand adjustment method for the aggregator to allocate power reduction among consumers in a distributed manner. Specifically, this method simultaneously derives the optimal power reduction of each consumer using a gradient method and determines the control inputs by solving a quadratic programming problem with state and input constraint conditions. This paper also shows the convergence of the proposed algorithm, and finally, numerical simulation results illustrate the effectiveness of the proposed method.

This paper deals with an optimal control and scheduling algorithm for one-way car sharing systems. One of the most critical problems of one-way car sharing system is that the parking lots, which tends to be the origins and the destinations, are biased due to the traffic demand of the users, and the cost of relocating the vehicles is becoming high. In order to solve this cost problem, we propose a novel mechanism to give incentives to users in the form of discounts and propose changes to parking lots and departure times to prevent bias in the number of vehicles in parking lots. The optimal parking lot is determined by using a queue information in order to consider the traffic demand distribution of users. Furthermore, an incentive is designed so as to satisfy desirable properties based on VCG mechanism. Finally, the effectiveness of the proposed method is confirmed via several numerical simulations.

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.

We consider a set of players who have to make a joint decision under uncertainty and learn via gradient dynamics how to reach the maximal profit for the coalition while the game is running and the revenue gets allocated to the players. The main result is the convergence of the intertwined learning-allocation dynamics to a stable solution which yields the maximal profit for the coalition and a stable allocation in the core of the asymptotic game.

In this paper, we show that Prescribed Performance Control (PPC) is a model-free Control Barrier Function (CBF)-based control approach. Specifically, we establish that a function utilized in the PPC design is a Time-Varying Reciprocal Control Barrier Function (TVRCBF). We demonstrate that PPC satisfies the same gradient condition that is well-known in the CBF literature, ensuring forward invariance. As a result, the control inputs generated by the PPC law belong to the input set characterized by the TVRCBF. Apart from assuming a certain controllability property, no further knowledge on the system dynamics is required. Our theoretical findings improve the understanding of the relationship between PPC and other CBF-based controllers. The theoretical results are validated through numerical simulations.

This study proposes a model predictive control (MPC)-based energy management strategy for fuel cell electric vehicles (FCEVs), formulated using a unified state-space representation that captures the dynamic behavior of both the fuel cell system and the battery. A key feature of the proposed method is the inclusion of a cost function term that encourages operation around the fuel cell's maximum efficiency point. The control framework is designed to concurrently reduce hydrogen consumption, regulate the battery state-of-charge (SOC), suppress abrupt variations in fuel cell power output, and guide the fuel cell stack power toward a reference associated with the maximum efficiency point. Simulation results confirm that the proposed approach achieves a 2.9 % improvement in fuel economy and a 56 % reduction in fuel cell stack output fluctuation compared to a rule-based strategy.

This paper proposes an integrated algorithm for matching and pricing in multi-passenger ride-sharing systems. We construct a framework to solve the group formation problem, ensuring that social welfare is maximized even when passengers act strategically. To achieve this, we develop a matching method using a hypergraph structure that allows for the simultaneous optimization of passenger-driver assignments. Additionally, we design a pricing mechanism based on the Vickrey-Clarke-Groves (VCG) theory to guarantee strategy-proofness and individual rationality. Finally, the proposed algorithm is validated through theoretical analysis and numerical simulations.

This paper deals with ∞ filter-based short-term electric load prediction taking into consideration the characteristics of the load curve. We propose a predictive method to forecast the future electric load demand for 36 h from 12:00 PM, and evaluate the peak and bottom of the load curves on the next day. We propose a load model, estimate the unknown parameters of the model by means of an ∞ filter using the data separated for nonworking days and weekdays, with the same pattern of the previous data chosen and assigned to the model parameters. The simulation results show the effectiveness of the proposed prediction methodology.

In this paper, we study cooperative control problems with a multi-UAV (Unmanned Aerial Vehicle) system expressed as a fourth-order system using a consensus-based algorithm. We first show that a linearized model of UAVs like quadrotors is expressed as a fourth-order system, and then propose a formation control algorithm for the fourth-order system after formulating a problem. The proposed control law is based on a consensus algorithm, and a leader-follower structure is also applied to the control law so that the leader can provide the quadrotors with commands such as their desired states. And then, we show that the proposed control algorithm can guarantee accurate formation keeping when fundamental assumptions about the network composed of the multiple UAVs are satisfied. Finally, the proposed approach is validated by some simulations.

The goal of this paper is to improve tracking performance of a bilateral teleoperation system with communication time delay. The proposed method is a passivity-based PID-type controller. First, using feedback passivation control, we make the master and slave robots passive with respect to the new output including position and velocity signals. Secondly, we design the PI controller for the new output. In the proposed method, position coordination is achieved even with the viscous friction error situation. The stability of the proposed PID teleoperation system and tracking performance are shown via passivity based stability analysis. Finally, several experimental results show the effectiveness of the proposed control methodology for teleoperation systems. Key Words : teleoperation system, communication delay, PID-type controller, tracking performance.