Enhancing Formation Control of Multi Agent Systems Using Ann Based Technique
Authors
Akaninyene M. Joshua
Department Electrical and Electronic Engineering, Enugu State University of Science and Technology. (NG)
Chukwuagu M. Ifeanyi
Department of Electrical and Electronic Engineering Caritas University Amorji-Nike, Emene, Enugu State (NG)
Article Information
DOI: 10.51583/IJLTEMAS.2026.150600001
Subject Category: Electronic Engineering
Volume/Issue: 15/6 | Page No: 1-9
Publication Timeline
Submitted: 2026-07-02
Published: 2026-07-02
Abstract
The persistent power failure that paralyzed business activities were caused by these factors that could not attain threshold, Inter-agent spacing error (RMS), Maximum formation shape error, Relative position estimation error ,Formation centroid tracking error , Relative velocity mismatch , Formation convergence time , Communication latency and Energy consumption deviation . This constant power failure observed in the country led to introduction of enhancing formation control of multi agent systems using ANN based technique. To perfectly achieve this, it was done in this manner, formation control of multi agent systems was characterized and the causes of poor formation control of multi agent systems was established and a conventional SIMULINK model for formation control of multi agent systems was designed. Then ANN was trained in the causes of poor formation control of multi agent systems for an effective reduction of the causes of poor formation control of multi agent systems and an algorithm was developed to implement the process. However, a SIMULINK model for enhancing formation control of multi agent systems using ANN based technique was designed and the results obtained were validated and justified. The results obtained were the conventional Inter-agent spacing error (RMS) that causes poor formation control of multi agent systems was 0.4 m. On the other hand, when ANN based technique was integrated into the system, it automatically changed 0.2 m. and the conventional Energy consumption deviation that causes poor formation control of multi agent systems was 27%. Meanwhile, when ANN based technique was integrated into the system, it drastically reduced to 20 %.Finally, with these results obtained the percentage enhancement formation control of multi agent systems when an ANN was integrated in to it was7%.
Keywords
enhancing, formation, control, multi agent, systems, ANN based, technique
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References
1. Fax, J. A., & Murray, R. M. (2004). Information flow and cooperative control of vehicle formations. IEEE Transactions on Automatic Control, 49(9), 1465–1476. [Google Scholar] [Crossref]
2. Haykin, S. (2009). Neural networks and learning machines (3rd ed.). Pearson Education. [Google Scholar] [Crossref]
3. Lewis, F. L., Vrabie, D., & Syrmos, V. L. (2013). Optimal control (3rd ed.). Wiley. [Google Scholar] [Crossref]
4. Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233. [Google Scholar] [Crossref]
5. Ren, W., & Beard, R. W. (2008). Distributed consensus in multi-vehicle cooperative control: Theory and applications. Springer. [Google Scholar] [Crossref]
6. Wang, X., Zhang, Y., & Lewis, F. L. (2020). Adaptive neural-network-based formation control for multi-agent systems with uncertainties. IEEE Transactions on Cybernetics, 50(8), 3508–3519. [Google Scholar] [Crossref]
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