Cooperative Line Formation Control of Multi-Agent Systems Based on Least Squares Estimation

  Samratul Fuady (1*), Arumjeni Mitayani (2), Ario Birmiawan Widyoutomo (3), Arief Suryadi Satyawan (4), Alexander Christanto Budiman (5), Suyoto Suyoto (6), Mochamad Mardi Marta Dinata (7)

(1) Department of Electrical Engineering, Jambi University - Indonesia orcid
(2) Research Center for Telecommunication, National Research and Innovation Agency - Indonesia orcid
(3)  - Indonesia orcid
(4) Research Center for Telecommunication, National Research and Innovation Agency - Indonesia
(5) Research Center for Transportation Technology, National Research and Innovation Agency - Indonesia
(6) Research Center for Telecommunication, National Research and Innovation Agency - Indonesia
(7) Research Center for Telecommunication, National Research and Innovation Agency - Indonesia
(*) Corresponding Author

Received: August 14, 2022; Revised: September 12, 2022
Accepted: November 15, 2022; Published: December 31, 2022

How to cite (IEEE): S. Fuady, A. Mitayani, A. B. Widyoutomo, A. S. Satyawan, A. C. Budiman, S. Suyoto,  and M. M. Dinata, "Cooperative Line Formation Control of Multi-Agent Systems Based on Least Squares Estimation," Jurnal Elektronika dan Telekomunikasi, vol. 22, no. 2, pp. 72-77, Dec. 2022. doi: 10.55981/jet.490


In this paper, we consider the problem of multi-agent systems where each agent aims to establish a line formation in a distributed manner. In constructing an efficient line formation, finding a line with the closest total distance from every agent is essential. We propose a formation control using least squares estimation (LSE) performed by each agent with only the local information that consists of the corresponding agent’s and neighbors’ positions. Each agent calculates the local cost function, which is the squared distance from the LSE line to the related agent’s and its neighbors’ positions. Our goal is to minimize the global cost function, which is the sum of these local cost functions. To achieve this, we employ distributed optimization to the global cost function of the overall system using the subgradient method performed by each agent locally. We evaluate our proposed method using numerical simulation, and the result complies with our goal of this paper



LSE; formation control; distributed optimization; multi-agent systems;

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