List of English Publication

@inproceedings{nokey,

title = {Multi-agent Target Position Estimation Using Bearing-only Measurments via Spatial Excitation},

author = {Soojeong Hyeon and Iman Shames and Hyungbo Shim},

url = {https://ieeexplore.ieee.org/abstract/document/10644598},

doi = {10.23919/ACC60939.2024.10644598},

isbn = {979-8-3503-8265-5},

year  = {2024},

date = {2024-07-10},

urldate = {2024-07-10},

booktitle = {2024 American Control Conference},

pages = {548-553},

publisher = {IEEE},

abstract = {This paper solves the problem of estimating a single target position by a group of agents in two-dimensional space. Each agent is assumed to know its own position and to measure only the bearing to the target. We develop a distributed estimation scheme that processes individual bearing measurements by utilizing continuous-time dynamics with communication among the agents. Under the assumption of connectivity on the communication graph, it is shown that, even if each bearing measurement does not satisfy the persistency of excitation condition, each agent is able to estimate the target position provided that the agents are properly positioned. We examine this condition in terms of spatial excitation and its relationship with geometric configuration of the agents. Additionally, we analyze the convergence rate of the estimation error. Simulation results are provided to illustrate the performance of the proposed estimator.},

keywords = {Multi-agent system, Networked control system},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {A Reinforcement Learning Approach for Safe Control Using Multi-Agent Q-Learning},

author = {Deuksun Hong and Hyungbo Shim},

year  = {2024},

date = {2024-07-06},

booktitle = {2024 14th Asian Control Conference},

abstract = {Reinforcement learning techniques typically optimize a controller only for the trained environment. This characteristic leads to trouble when applying the learned models to other settings, particularly in real control scenarios where control needs to be performed under different conditions than those during the learning phase due to modeling errors or aging. This paper introduces a multi-agent version of Q-learning known as QD-learning. We also present how this algorithm operates when applied to the various environment settings, and experimentally demonstrate its effectiveness for designing a safe controller.},

keywords = {Consensus, Multi-agent system, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {Initialization-free Algorithm for Discrete-time Dynamic Average Consensus and Its Application to Distributed Optimization},

author = {Jiyeon Nam and Jinwook Heo and Jeongwoo Kim and Hyungbo Shim and Jae Sung Bang and Jinsung Kim},

url = {https://ieeexplore.ieee.org/abstract/document/10003797?casa_token=DuxZUEtvKl0AAAAA:NZLDmrslBnSuOUXJQEDoFIKpq7S_j8tpp1_m-NkuQr1uVTaA1zu7O6qXQoTxi_makBT6rgsLRQ},

doi = {https://doi.org/10.23919/ICCAS55662.2022.10003797},

isbn = {978-89-93215-24-3},

year  = {2022},

date = {2022-11-28},

urldate = {2022-11-28},

booktitle = {Proc. of 22th International Conference on Control, Automation and Systems (ICCAS)},

pages = {424-429},

publisher = {IEEE},

address = {Busan, Korea},

abstract = {In this paper, we address the problem of dynamic average consensus in a discrete-time setting. The objective of dynamic average consensus problem is for each state of agent to track the average of reference input signals of each agent in a distributed manner. Without initialization process, the proposed algorithm renders the state of each agent converge practically to the average of time-varying reference signals with any given bound. It is a simple time-domain algorithm based on two-time scale. Finally, by applying our algorithm to an distributed optimization problem, we provide two simulation results that converge correctly when the initial conditions are perturbed and that converge within certain amount of small error when numerical errors are injected every time step.},

keywords = {Dynamic average consensus, Initialization-free, Multi-agent system},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{YunLeeShim17,

title = {Consensus-based Distributed Coordination for Optimal Energy Generation of Hierarchical Systems},

author = {Hyeonjun Yun and Seungjoon Lee and Hyungbo Shim},

doi = {10.23919/SICE.2017.8105477},

year  = {2017},

date = {2017-09-20},

booktitle = {Proc. of 56th Annual Conference of the Society of Instrument and Control Engineers (SICE)},

pages = {871-876},

address = {Kanazawa, Japan},

abstract = {This paper considers the optimal energy generation problem for hierarchical system, which consists of multi-cluster power system. In particular, consensus-based distributed hierarchical coordination algorithm is proposed to meet the power generation/demand balance. By using Lagrangian-based approach, we show that the optimization problem for the hierarchical system can be separated into each layer's consensus problem with communication between each layer's leader agent. The convergence is proved in the sense of global practical stability by the singular perturbation theory. Simulation result shows that the solution obtained by the proposed algorithm converges to the optimal solution.},

keywords = {Distributed optimization, Multi-agent system, Power grids, Synchronization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{KimShim15b,

title = {Robust Synchronization for High-Order Heterogeneous Multi-Agent Systems},

author = {Jaeyong Kim and Hyungbo Shim},

year  = {2015},

date = {2015-10-28},

booktitle = {Proc. of SWARM 2015: The First International Symposium on Swarm Behavior and Bio-Inspired Robotics},

journal = {Proc. of SWARM 2015: The First International Symposium on Swarm Behavior and Bio-Inspired Robotics},

pages = {293-294},

address = {Kyoto, Japan},

abstract = {This paper studies robustness of synchronization for high-order heterogeneous multi-agent systems. Using the

concept of the averaged dynamics, it is claimed that two sources enhance the robustness of synchronized behavior against

heterogeneity of agents’ dynamics. First, strongly coupled network makes each behavior of heterogeneous agent close to

that of the averaged dynamics. Second, we observe that the averaged dynamics becomes more robust against individual

variation as the number of agents increases. Simulation result confirms our claim that a large number of agents with strong

coupling have robust synchronization.},

keywords = {Blended dynamics, Multi-agent system, Synchronization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{KimYangShimKim13,

title = {Robustness of Synchronization in Heterogeneous Multi-Agent Systems},

author = {Jaeyong Kim and Jongwook Yang and Hyungbo Shim and Jung-Su Kim},

doi = {10.23919/ECC.2013.6669520},

year  = {2013},

date = {2013-07-17},

booktitle = {Proc. of 2013 European Control Conference (ECC)},

pages = {3821-3826},

publisher = {IEEE},

address = {Zurich, Switzerland},

abstract = {This paper studies robustness of synchronization in heterogeneous multi-agent systems, which is gained by interactions with other agents through the network. In order to effectively deal with the heterogeneous cases, we introduce the concept of the averaged dynamics which is the average of all agents' dynamics, and then claim that two sources enhance the robustness of the group behavior against differences among agents. First, we show that strong coupling among heterogeneous agents makes the trajectories of all agents remain in an arbitrarily small neighborhood of the trajectory of the averaged dynamics. Second, we observe that the amount of individual variation of each agent, that contributes to the averaged dynamics, gets smaller as the number of agents increases, and thus, the averaged dynamics becomes more robust to the differences among agents. Simulation results confirm our claim that a large number of agents with strong couplings have robust synchronization.},

keywords = {Multi-agent system},

pubstate = {published},

tppubtype = {inproceedings}

}