The abilities of neural networks combined with fuzzy logic offer interesting prospects for the active control of structures. By identification, they permit discarding the often delicate modeling step and they also permit the automatic regulation of the controllers that have non-linear characteristics. This study describes the application of neuro-fuzzy control to the dynamic behavior of structures. The study first explains the process chosen, which consists of two parts: • the first part is essential for the adjustment of the associated controller and concerns the neural identification of the structure studied; • the second part describes the controller development and the training stage; the controller is based on the simplest neural network model possible. This network is also able to translate Sugeno’s fuzzy function and optimize its performances according to a reference response. The study then presents two applications: the first deals with the identification and control of a linear mechanical system with two degrees of freedom. The second deals with the identification and control of the non-linear dynamic behavior of active electromagnetic actuators along one acting axis. In both cases, the results show the abilities and the efficiency of this process and underline the main advantage of this type of controller operating even on in a strongly non-linear system.

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