Latest revision as of 09:46, 15 February 2021

Abstract

Due to the interaction and external interference, the crowds will constantly and dynamically adjust their evacuation path in the evacuation process to achieve the purpose of rapid evacuation. The information from previous process can be used to modify the current evacuation control information to achieve a better evacuation effect, and iterative learning control can achieve an effective prediction of the expected path within a limited running time. In order to depict this process, the social force model is improved based on an iterative extended state observer so that the crowds can move along the optimal evacuation path. First, the objective function of the optimal evacuation path is established in the improved model, and an iterative extended state observer is designed to get the estimated value. Second, the above model is verified through simulation experiments. The results show that, as the number of iterations increases, the evacuation time shows a trend of first decreasing and then increasing.

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• http://dx.doi.org/10.1155/2020/4604187 under the license http://creativecommons.org/licenses/by/4.0/

• https://doi.org/10.1155/2020/4604187

• http://downloads.hindawi.com/journals/jat/2020/4604187.pdf,

http://downloads.hindawi.com/journals/jat/2020/4604187.xml,

http://dx.doi.org/10.1155/2020/4604187 under the license cc-by

• http://dx.doi.org/10.1155/2020/4604187,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2020/4604187,

http://downloads.hindawi.com/journals/jat/2020/4604187.pdf,

[=citjournalarticle_661442_9 https://www.safetylit.org/citations/index.php?fuseaction=citations.viewdetails&citationIds[]=citjournalarticle_661442_9],

https://academic.microsoft.com/#/detail/3046240635