×

UAV robust strategy control based on MAS. (English) Zbl 1406.93215

Summary: A novel multiagent system (MAS) has been proposed to integrate individual UAV (unmanned aerial vehicle) to form a UAV team which can accomplish complex missions with better efficiency and effect. The MAS based UAV team control is more able to conquer dynamic situations and enhance the performance of any single UAV. In this paper, the MAS proposed and established combines the reacting and thinking abilities to be an initiative and autonomous hybrid system which can solve missions involving coordinated flight and cooperative operation. The MAS uses BDI model to support its logical perception and to classify the different missions; then the missions will be allocated by utilizing auction mechanism after analyzing dynamic parameters. Prim potential algorithm, particle swarm algorithm, and reallocation mechanism are proposed to realize the rational decomposing and optimal allocation in order to reach the maximum profit. After simulation, the MAS has been proved to be able to promote the success ratio and raise the robustness, while realizing feasibility of coordinated flight and optimality of cooperative mission.

MSC:

93C85 Automated systems (robots, etc.) in control theory
68T42 Agent technology and artificial intelligence
93B35 Sensitivity (robustness)
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Mei, C. S., Research and analysis of agent technology, Electronic Design Engineering, 19, 11, 121-124 (2011)
[2] Qiu, J.; Feng, G.; Yang, J., A new design of delay-dependent robust filtering for discrete-time T-S fuzzy systems with time-varying delay, IEEE Transactions on Fuzzy Systems, 17, 5, 1044-1058 (2009) · doi:10.1109/TFUZZ.2009.2017378
[3] Han, H. D.; Wu, Y. X.; Cen, Y. W., Research progress of multi-robot cooperation and coordination, Computer Engineering and Applications, 44, 24, 238-241 (2008)
[4] Karimi, H. R., A computational method for optimal control problem of time-varying state-delayed systems by Haar wavelets, International Journal of Computer Mathematics, 83, 2, 235-246 (2006) · Zbl 1090.65081 · doi:10.1080/00207160600659257
[5] Yin, S.; Luo, H.; Ding, S., Real-time implementation of fault-tolerant control systems with performance optimization, IEEE Transactions on Industrial Electronics, 64, 5, 2402-2411 (2014)
[6] Yin, S.; Yang, X.; Karimi, H. R., Data-driven adaptive observer for fault diagnosis, Mathematical Problems in Engineering, 2012 (2012) · Zbl 1264.93115 · doi:10.
[7] Lu, W.; Zhiliang, W.; Siquan, H.; Lei, L., Ant colony optimization for task allocation in multi-agent systems, Communications, 10, 3, 125-132 (2012)
[8] Chen, W. D.; Xi, Y. G.; Gu, D. L.; Dong, S. L., Complex task oriented multi-robot distributed coordination system, Control Theory and Applications, 19, 4, 505-510 (2002)
[9] Yi, W. H.; Xia, H. G.; Chen, X. G., Distributed coordination reasoning with BDI and knowledge level, Systems Engineering, 22, 7, 93-98 (2004)
[10] Hui, Z.; Shi, Y.; Liu, M., H step tracking control for networked discrete-time nonlinear systems with integral and predictive actions, IEEE Transactions on Industrial Informatics, 9, 1, 337-345 (2013)
[11] Tang, G. F.; Jiang, D. L., Study on task allocation and negotiation of military virtual warehouse multi-agent system based on auction theory, Logistics Technology, 29, 12, 193-196 (2010)
[12] Klabi, H.; Mellouli, K.; Mellouli, S.; Rekik, M., A trust model for a multi-agent negotiation, Proceedings of the International Conference on Communications and Information Technology (ICCIT ’12)
[13] Moon, S. K.; Park, J.; Simpson, T. W.; Kumara, S. R. T., A dynamic multiagent system based on a negotiation mechanism for product family design, IEEE Transactions on Automation Science and Engineering, 5, 2, 234-244 (2008) · doi:10.1109/TASE.2007.896902
[14] Osipov, K.; Sukthankar, G., Forming effective teams from agents with diverse skill sets, Proceedings of the International Conference on Social Informatics (SocialInformatics ’12)
[15] Dasgupta, P., Multi-agent coordination techniques for multi-robot task allocation and multi-robot area coverage, Proceedings of the International Conference on Collaboration Technologies and Systems (CTS ’12)
[16] Liu, X.; Wang, D., The behavior analysis of grouped multi-attribute auction based on multi-agent., Proceedings of the 24th Chinese Control and Decision Conference (CCDC ’12)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.