List of English Publication

@inproceedings{nokey,

title = {User-friendly Vehicle-to-Grid Optimal Scheduling Problem and Distributed Implementation for Plug-and-play Operation},

author = {Seungbeom Lee and Jinwook Heo and Soojeong Hyeon and Jiyeon Nam and Hyungbo Shim and Jinsung Kim},

doi = {10.23919/ICCAS59377.2023.10316855},

year  = {2023},

date = {2023-10-19},

urldate = {2023-10-19},

booktitle = {International Conference on Control, Automation and Systems (ICCAS 2023)},

publisher = {IEEE},

address = {Yeosu, Korea, Republic of},

abstract = {In this paper, we propose an optimization method of charging scheduling algorithm based on preference of vehicle owners. To suffice various desires for vehicle charging, we design objective function of optimization problem as weighted sum of some objective function to reflect various desires. To solve the problem without aggregating weight coefficient, distributed optimization based on dynamic average consensus method is implemented. By initialization-free property of dynamic average consensus, plug-and-play operation of electric vehicle is applicable. Finally, we simulate the proposed algorithm to enlighten the meaning of weighted sum for objective function and plug-and-play scenario.},

keywords = {Distributed optimization, Initialization-free, Plug-and-play},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{LeeShim20,

title = {Blended dynamics approach for analysis and construction of distributed optimization algorithms},

author = {Seungjoon Lee and Hyungbo Shim},

url = {https://www.dbpia.co.kr/pdf/pdfView.do?nodeId=NODE10493601&mark=0&useDate=&bookmarkCnt=0&ipRange=N&accessgl=Y&language=ko_KR},

year  = {2020},

date = {2020-10-15},

booktitle = {Proc. of 20th International Conference on Control, Automation and Systems (ICCAS)},

publisher = {IEEE},

address = {Busan, Korea},

abstract = {In this paper, the recently proposed concept of blended dynamics of a heterogeneous multi-agent system is introduced. Although the blended dynamics is originally proposed for the analysis of heterogeneous multi-agent system, it is argued that the same approach provides a flexible framework for the analysis and construction of the distributed algorithms. The utility of the proposed approach is first illustrated by systematically constructing the distributed gradient descent algorithm from the centralized gradient descent method. The proposed approach is further extended to derive two variants of distributed algorithms utilizing the heavy-ball method. Finally, drawback of the proposed approach is identified and integral action is employed to resolve the issue.},

keywords = {Distributed optimization, Multi-agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {Utility of Edge-wise Funnel Coupling for Asymptotically Solving Distributed Consensus Optimization},

author = {Jin Gyu Lee and Thomas Berger and Stephan Trenn and Hyungbo Shim},

url = {https://ieeexplore.ieee.org/abstract/document/9143983},

doi = {10.23919/ECC51009.2020.9143983},

isbn = {978-3-90714-402-2},

year  = {2020},

date = {2020-05-12},

booktitle = {Proc. of 2020 European Control Conference (ECC)},

pages = {911-916},

publisher = {IEEE},

address = {St. Petersburg, Russia},

abstract = {A new approach to distributed consensus optimization is studied in this paper. The cost function to be minimized is a sum of local cost functions which are not necessarily convex as long as their sum is convex. This benefit is obtained from a recent observation that, with a large gain in the diffusive coupling, heterogeneous multi-agent systems behave like a single dynamical system whose vector field is simply the average of all agents' vector fields. However, design of the large coupling gain requires global information such as network structure and individual agent dynamics. In this paper, we employ a nonlinear time-varying coupling of diffusive type, which we call `edge-wise funnel coupling.' This idea is borrowed from adaptive control, which enables decentralized design of distributed optimizers without knowledge of global information. Remarkably, without a common internal model, each agent achieves asymptotic consensus to the optimal solution of the global cost. We illustrate this result by a network that asymptotically finds the least-squares solution of a linear equation in a distributed manner.},

keywords = {Distributed optimization, Funnel control, Multi-agent systems},

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{nokeyk,

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

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

urldate = {2022-11-28},

booktitle = {International Conference on Control, Automation and Systems (ICCAS 2022)},

publisher = {Institute of Control, Robotics and Systems},

keywords = {Consensus, Distributed algorithm, Distributed optimization},

pubstate = {published},

tppubtype = {inproceedings}

}