Browsing by Author "Song, Yeongho"
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Item Open Access Distributed estimation of stochastic multiagent systems for cooperative control with a virtual network(IEEE, 2022-10-19) Song, Yeongho; Lee, Hojin; Kwon, Cheolhyeon; Shin, Hyo-Sang; Oh, HyondongThis article proposes a distributed estimation algorithm that uses local information about the neighbors through sensing or communication to design an estimation-based cooperative control of the stochastic multiagent system (MAS). The proposed distributed estimation algorithm solely relies on local sensing information rather than exchanging estimated state information from other agents, as is commonly required in conventional distributed estimation methods, reducing communication overhead. Furthermore, the proposed method allows interactions between all agents, including non-neighboring agents, by establishing a virtual fully connected network with the MAS state information independently estimated by each agent. The stability of the proposed distributed estimation algorithm is theoretically verified. Numerical simulations demonstrate the enhanced performance of the estimation-based linear and nonlinear control. In particular, using the virtual fully connected network concept in the MAS with the sensing/communication range, the flock configuration can be tightly controlled within the desired boundary, which cannot be achieved through the conventional flocking methods.Item Open Access Using lazy agents to improve the flocking efficiency of multiple UAVs(Springer, 2021-10-27) Song, Yeongho; Gu, Myeonggeun; Choi, Joonwon; Oh, Hyondong; Lim, Seunghan; Shin, Hyo-Sang; Tsourdos, AntoniosA group of agents can form a flock using the augmented Cucker-Smale (C-S) model. The model autonomously aligns them to a common velocity and maintains a relative distance among the agents in a distributed manner by sharing the information among neighbors. This paper introduces the concept of inactiveness to the augmented C-S model for improving the flocking performance. It involves controlling the energy and convergence time required to form a stable flock. Inspired by the natural world where a few lazy (or inactive) workers are helpful to the group performance in social insect colonies. In this study, we analyzed different levels of inactiveness as a degree of control input effectiveness for multiple fixed-wing UAVs in the flocking algorithm. To find the appropriate inactiveness level for each flock member, the particle swarm optimization-based approach is used as the first step, based on the initial condition of the flock. However, as the significant computational burden may cause difficulties in implementing the optimization-based approach in real time, we also propose a heuristic adaptive inactiveness approach, which changes the inactivity level of selected agents adaptively according to their position and heading relative to the flock center. The performance of the proposed approaches using the concept of lazy (or inactive) agents is verified with numerical simulations by comparing them with the conventional flocking algorithm in various scenarios.