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Evolutionary based BEL controller applied to a magneto-rheological actuator
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Orientador(es)
Resumo(s)
This paper addresses the problem of finding the best Brain Emotional Learning (BEL) controller parameters in order to improve the response of a single degree-of-freedom (SDOF) structural system under an earthquake excitation. The control paradigm considered is based on a semi-active system to control the dynamics of a lumped mass-damper-spring model, being carried out by changing the damping force of a magneto-rheological
(MR) damper. A typical BEL based controller requires the definition of several parameters which can be proved difficult and nonintuitive to obtain. For this reason, an evolutionary based search technique has been added to the current problem framework in order to automate the controller design. In particular, the
particle swarm optimization (PSO) method was chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary based algorithm. Moreover, simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.
Descrição
Palavras-chave
BEL Controlo
Contexto Educativo
Citação
Braz-Cesar, M.T.; Coelho, J.P.; Gonçalves, José (2018). Evolutionary based BEL controller applied to a magneto-rheological actuator. In 13Th Apca International Conference on Control and Soft Computing (Controlo 2018). Ponta Delgada, Sao Miguel. p.384-388