This paper deals with load frequency control problem of power system. In recent years, due to the large introduction of generators using renewable energy, the relative load fluctuation increases, making it difficult to maintain power balance. Therefore, this paper proposes frequency control method using Moving Horizon Estimation and iterative gradient method for connected power system. In this paper, since system handles frequency control problems in real time, the system manager needs to grasp all states of power system every moment, and estimates the state of power system by using Moving Horizon Estimation. Next, control inputs are determined such that each area minimizes evaluation function including barrier function considering constraint conditions for generator and battery by using iterative gradient method. At this time, since the control target is connencted power system, it is represented as a model including the state of adjacent area, so that each area shares information with each other and performs cooperative control between areas. Finally, simulation of load frequency control using Moving Horizon Estimation and iterative gradient method is carried out and the effectiveness is confirmed.

The authors have researched into a novel localization system for an unmanned aerial vehicle (UAV) for automatic structure visual inspection. In environment around large scale facilities such as under a bridge, automatic flight does not work due to the GNSS signals not reaching the UAV. This paper proposes a localization system using subsidiary UAVs (sub-UAVs) that assist localization of an UAV (main-UAV) working in the vicinity of facilities. Sub-UAVs acquires its own position information by GNSS and relative position information by vision sensor. Main-UAV localizes its own position by integrating the information obtained from measurements of all Sub-UAVs. In this paper, we model two types of measurement, GNSS and vision sensor, by Sub-UAVs to realize the proposed system. Then, the parameters associated with modeling are confirmed through basic experiments, and the effectiveness of the system is confirmed by simulation.

Stimulated by strong needs for enhancing efficiency in transportation systems, various traffic simulators have been developed so far. Meanwhile, a number of control algorithms optimizing traffic signals based on sensor data acquired in real-time have been reported in the literature. Due to difficulties in implementing these algorithms on real traffic systems, it is reasonable to conduct demonstrations on a traffic simulator. However, existing simulators do not always fully equip functions for real-time feedback control and optimization. We thus develop a novel simulator configuration interconnecting a traffic simulator, UC-win/Road, a control simulator, Simulink, and an optimization solver, Gurobi. Moreover, we install a mechanism based on parallel computing in order to avoid losing the real-time execution due to a large amount of computational efforts for solving a large-scale optimization over a traffic network. We finally implement an optimal traffic signal control algorithm presented in our antecessor on the simulator and demonstrate its effectiveness.

This paper proposes a vision-based target pursuit control based on cooperative estimation of target motion. A vision-based observer using visual sensor networks, so called Networked Visual Motion Observer, is presented in one of the authors' previous work to estimate three dimensional target motion, i.e., translational and rotational motion. We first introduce rigid body motion and camera model, and formulate the problem to be considered. Then, we propose a novel observer-based pursuit control law based on motion estimation. We next show that all rigid bodies with a visual sensor converge to each desired relative pose with respect to the target when the target is static. Furthermore, the pursuit performance for a moving target is evaluated based on L₂ stability. Finally, we demonstrate the effectiveness of the proposed control law through simulations and experiments.