Published papers

In this study, we propose a complex distributed task allocation algorithm for “multiple agents versus multiple objects” for object transportation missions. This is achieved by combining a method for determining a set of objects that one agent can transport at once with a list for managing the start time of transportation. This reduces the transport start time and the agent's travel distance. We also aim to apply the proposed method to changing situations.

We conduct research on a distributed task assignment algorithm, which is one of the distributed decision-making methods in multi-agent system. We consider a mission in which multi-agents transport multiple packages. In particular, we deal with a problem in which one agent can carry multiple packages depending on its loading capacity, and there are heavy packages which require multiple agents to transport. To achieve the goal, we combine two methods. The first is Grouping, a method of calculating the groups of packages that one agent can carry at a time. The second is two types of lists which record task start time of agents. In consequence, we decrease the task start time and agents' moving distance. We also consider transportation control, by using consensus based control to second-order system.

© Springer Nature Singapore Pte Ltd. 2020. In the future power network, power consumers as well as power generators participate in electricity market trading as selfish market players. Dynamic pricing is one of the useful tools to manage such networks in a distributed manner by changing electricity prices appropriately. In this chapter, we show distributed price decision procedures regarding the dynamic pricing to maximize social welfare in a power grid with information privacy of market players. Specifically, we first deal with an electricity market that covers multiple regional areas in a power grid and propose a market trading algorithm to derive the optimal regional electricity prices based on alternating decision making of market players. Subsequently, we deal with an electricity market in one regional area where an aggregator participates in the market trading as a mediator between the market operator and consumers. For this market, we propose a trading algorithm to adjust power demand of consumers depending on their lifestyles in a day-ahead electricity market. This chapter also shows the convergence properties of the proposed market trading algorithms and illustrates that these methods enable us not only to derive the optimal electricity prices but also to improve their convergence speed through numerical simulation.

In this paper, we propose a novel control method which combine collision avoidance method and RISE(Robust Integral of the Sign of Error) for multiple Quad-rotor formation control methods for cooperative control, and confirm effectiveness of this method by experiment using actual Quad-rotor. First, this work describes a method to suppress nonlinear disturbance using RISE which is a type of sliding mode control. In addition, collision avoidance method and the conditions to achieve accurate formation are explained. Finally, the effectiveness of proposed method is confirmed functioning correctly, by implemented an algorithm in the actual Quad-rotor.

This paper deals with Extended Kalman Filter(EKF)-based　SLAM estimation considering intermittent measurement. The study proposes a method to ensure that a designer is able to secure the state covariance or system uncertainties even though the mobile robot sometimes losses its measurement data during its observations. EKF which is based on Bayesian, is chosen in this work for estimation purposes in determining mobile robot position and the environment conditions. In addition, as most of estimation techniques uses Bayesian method, our propose results is expected to provide a general estimation guidelines especially on the filter statistical behavior. The analysis started from the review of theoretical studies of EKF including the expected condition occurred when mobile robot do not received information from its sensors. Simulation results have consistently supports our theoretical analysis which determines that a designer can determine the state error covariance explicitly whenever measurement data is not available.

In this paper, we consider cooperative control issues for a multiunmanned aerial vehicle (UAV) system. We propose a cooperative formation control strategy with unidirectional network connections between UAVs. Our strategy is to apply a consensus-based algorithm to the UAVs so that they can cooperatively fly in formation. First, we show that UAV models on the horizontal plane and in the vertical direction are expressed as a fourth- and secondorder system, respectively. Then, we show that the stability discriminants of the multi-UAV system on the horizontal plane and in the vertical direction are expressed as polynomials. For a network structure composed of bidirectional or unidirectional network connections under the assumption that the network has a directed spanning tree, we provide conditions for formation control gains such that all roots of the polynomials have negative real parts in order for the UAVs to asymptotically converge to the positions for a desired formation by using the generalized Routh stability criterion. The proposed control algorithms are validated through simulations, and experiments are performed on multiple commercial small UAVs to validate the proposed cont

© 2019 Institute of Advanced Engineering and Science. All rights reserved. This paper analyze the performance of partial observability in simultaneous localization and mapping(SLAM) problem. The study focuses mainly on the effect of having a decorrelation technique known as Covariance Inflation to the estimation. The matrix inversion will be the main element to be investigated through two conditions with respect to some defined environment namely as unstable partially observable SLAM and partially observable SLAM via matrix norm analysis. For assessment purposes, the Extended Kalman Filter estimation is referred as the estimator to understand how the conditions can influence the results. The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.

© 2018 IEEE. In this paper, a coordinated optimal control is designed to simultaneously satisfy multiple power system objectives at the distribution network level. With high penetration of renewables, traditional generation and control may not be enough to compensate for intermittent changes in net load. Dynamic coordination of battery storage devices and demand response at aggregated distributed energy resource (DER) sites becomes necessary to maintain power system operation, however, each dynamic power component operates at different time scales. Frequency stabilization can be achieved by designing optimal controls for traditional generation, storage devices and demand response. The proposed optimal controls naturally render frequency separation of these load balancing mechanisms. Given a 24-hour net load forecast, optimal tracking control inputs are designed subject to constraints of selected system elements to ensure optimal performance of the power system. Simulation results are presented and discussed.

© 2017 IEEE. In this letter, a nonlinear control design is presented for systems whose input and output vectors are saturated. The proposed synthesis includes an optimal tracker as the seed controller and explicitly imposes all the relevant constraints on system input, on the rate of change in the input, and on system output by using the tools of barrier functions, input-output feedback linearization, comparison argument, Lyapunov argument, and a real-Time constrained optimization. The proposed design is applied to microgrid operation with high penetration of renewable generation, where net load and variable generation must be balanced and the microgrid should be operated at an optimal or near-optimal performance level. Since traditional generation has a limited ramping rate, battery storage devices, and demand responses become necessary to effectively deal with variability of renewable generation and to maintain frequency stability, but their capacities are also limited. It is shown that the proposed design is effective for coordinated control of traditional generation, storage and demand response so that the power system frequency is guaranteed to be within the required operational limits and that renewable curtailment is eliminated or minimized.

© 2020 Elsevier Ltd Oil spill, may happen as a result of the offshore well vessel failures. The precise information about the location and real-time situation of the oil spill is essential to perform an efficient treatment of these environmental catastrophes. A novel approach of multi robot's system which can navigate autonomously and track oil spill on the sea surfaces. This system is able to self-adapt and path plan amidst environmental changes, including the temporospatial variation of the oil concentration. The method consists of two main parts, which are the modelling of the oil spill, the tracking system and autonomous control of the robots. The simulated model depicts the morphological complexities of the spatiotemporal changes in the oil spills. The fuzzy controller is designed to control the robots to control nonlinear and non-crisply distributed water surfaces where oil pollution occurs. Meanwhile, the multi-robot path planning system hybridised with artificial potential field approach to avoid the collision. Several numerical simulations with different scenarios are done to show the robustness of the methods, based on the accuracy and precision of tracking. The evaluation with the simulated ground truth demonstrates that accuracy and precision of the tracking system are more than 70 and 80 percent respectively.

© 2019 JSME. At present, UAV is common to use as a single unit, but by operating as multi-agent system, it is possible to possibly be able to carry out more sophisticated tasks. For example, it may be possible to realize long-distance transportation with heavy object which impossible by a single drone. Therefore, we propose a control method to achieve cooperative transport so that the payload follows the virtual leader. We assume the transporting problem connected payload and multiple quad-rotors with cable. In this research, we treated quad-rotor as a model regarded as a linear secondary system and used consensus algorithm to achieve the purpose. Finally we verified its usefulness by simulation.

© 2019 IEEE. For a population of electric vehicles (EVs) we design a data-driven mean-field game and provide analysis of approximated mean-field equilibrium points based on a receding horizon approach. The model involves stochastic disturbances on the data that drive the game. Some numerical studies illustrate the efficacy of the proposed strategies.

This paper presents the estimation of both position and velocity of Stewart Manipulator by means of limbs potentiometer measurements and MEMS inertial sensors. The estimation used the Extended Kalman Filter (EKF) and H-infinity optimal filtering technique based on the combination of these sensors. The two types of filters are used as nonlinear state estimators to the Stewart platform which is modeled as a stochastic differential equations due to measurement noise in case of EKF and as a continuous time system model in case of H-infinity filtering technique. The results of the both filters are compared with each other on the Stewart platform DELTALAB EX800 using MATLAB SimMechanics toolbox. The simulation results show that Kalman filters are not the best choice for parallel manipulator state estimation as they bear from the hypothesis of statistical noise with zero mean as well as known noise covariance, which may reduce its performance. For these reasons, H-infinity filter may be the alternative of Kalman filter.

This paper deals with a passive-decomposition based control of bilateral teleoperation between a single master robot and multiple cooperative slave robots under time varying delay in the communication line. At first, we decompose the dynamics of multiple slave robots into two decoupled dynamics: the Shape-System describing dynamics of the cooperative works, and the Locked-System representing overall behavior of the multiple slave robots. Second, we propose a PD control method for bilateral teleoperation to guarantee the asymptotical stability of the system for time varying delay. Finally, experimental results show the effectiveness of our proposed teleoperation.

This paper proposes a cooperative control of multi-agent system to capture target object. When the agents capture the target object, they may be subjected to reaction force from it. Therefore, we have to consider the physical contact between the agent and target object. First, we introduce the second order model to consider reaction force from target object. The network topology among agents is time-varying and connected, and information of target object is allowed to be available to at least one agent. Second, we show the proposed control law guarantees the agents capture the target object. The control law consists of graph theory and consensus algorithm, and we analyze the convergence property. Finally, simulation results show the effectiveness of the proposed control law.