Jafari, MohammadFehr, RicGarcia Carrillo, Luis RodolfoEspinoza Quesada, Eduardo Steed2022-02-242022-02-242017-07Jafari, M., Xu, H. and Carrillo, L.R.G., 2017, May. Brain emotional learning-based intelligent controller for flocking of multi-agent systems. In 2017 American Control Conference (ACC) (pp. 1996-2001). IEEE.Jafari, M., Xu, H. and Carrillo, L.R.G., 2017, May. Brain emotional learning-based intelligent controller for flocking of multi-agent systems. In 2017 American Control Conference (ACC) (pp. 1996-2001). IEEE.https://hdl.handle.net/1969.6/90202The Brain Emotional Learning Based Intelligent Controller (BELBIC) is a neurobiologically-motivated intelligent controller based on a computational model of emotional learning in mammalian limbic system. The learning capabilities, multi-objective properties, and low computational complexity of BELBIC make it a very promising tool for implementation in real-time applications. Our research combines, in an original way, the BELBIC methodology with a flocking control strategy, in order to perform real-time coordination of multiple Unmanned Aircraft Systems (UAS). The characteristics of BELBIC fit well in this scenario, since almost always the dynamics of the autonomous agents are not fully known, and furthermore, since they operate in close proximity, they are subjected to aggressive external disturbances. Numerical and experimental results based on the coordination of multiple quad rotorcraft UAS platforms demonstrate the applicability and satisfactory performance of the proposed method.The Brain Emotional Learning Based Intelligent Controller (BELBIC) is a neurobiologically-motivated intelligent controller based on a computational model of emotional learning in mammalian limbic system. The learning capabilities, multi-objective properties, and low computational complexity of BELBIC make it a very promising tool for implementation in real-time applications. Our research combines, in an original way, the BELBIC methodology with a flocking control strategy, in order to perform real-time coordination of multiple Unmanned Aircraft Systems (UAS). The characteristics of BELBIC fit well in this scenario, since almost always the dynamics of the autonomous agents are not fully known, and furthermore, since they operate in close proximity, they are subjected to aggressive external disturbances. Numerical and experimental results based on the coordination of multiple quad rotorcraft UAS platforms demonstrate the applicability and satisfactory performance of the proposed method.en-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://creativecommons.org/licenses/by-nc-nd/4.0/reinforcement learning controladaptive neuralfuzzy controlembedded computer control systemsapplicationsknowledge based controlreinforcement learning controladaptive neuralfuzzy controlembedded computer control systemsapplicationsknowledge based controlArticlehttps://doi.org/10.1016/j.ifacol.2017.08.1219