Connectivity preserved nonlinear time-delayed multiagent systems using neural networks and event-based mechanism
- 128 Downloads
This paper studies how to preserve connectivity for nonlinear time-delayed multiagent systems using event-based mechanism. By using the idea of divide-and-conquer, we divide the distributed controller into five parts to deal with different requirements of the time-delayed multiagent systems, such as eliminating the negative effects of time delays, preserving connectivity, learning the unknown dynamics and achieving consensus. To reduce the communication times among the agents, a centralized event-based protocol is introduced and an event-triggered function is devised to control the frequency of the communication without Zeno behavior. The technique of \(\sigma \)-functions is used to exclude the singularity of the established distributed controller. In the simulation example, the results demonstrate the validity of our developed methodology.
KeywordsConnectivity preservation Event-based control Multiagent systems Neural networks
This work was supported in part by the National Natural Science Foundation of China under Grants 61233001, 61273140, 61304086, 61533017, 61503379 and U1501251, in part by China Scholarship Council under the State Scholarship Fund, in part by Beijing Natural Science Foundation under Grant 4162065 and in part by the Early Career Development Award of SKLMCCS.
- 11.Heemels W, Johansson KH, Tabuada P (2012) An introduction to event-triggered and self-triggered control. In: Proceedings of the conference on decision and control, Maui, Hawaii, USA, pp 3270–3285Google Scholar
- 19.Ma H, Liu D, Wang D (2015) Distributed control for nonlinear time-delayed multi-agent systems with connectivity preservation using neural networks. In: Proceedings of the 22nd international conference on neural information processing, Istanbul, Turkey, pp 34–42Google Scholar
- 28.Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178Google Scholar
- 37.Wang D, Mu C, Liu D (2016) Data-driven nonlinear near-optimal regulation based on iterative neural dynamic programming. Acta Autom Sin (accepted)Google Scholar
- 40.Yu H, Antsaklis P (2012) Formation control of multi-agent systems with connectivity preservation by using both event-driven and time-driven communication. In: Proceedings of the IEEE conference on decision control, Maui, Hawaii, USA, pp 7218–7223Google Scholar