SHUNT CONTROL ON CANTILEVER BEAM BY NEURAL NETWORK: OBJECTIVE FUNCTION

Resumo

Piezoelectric materials have been extensively studied in recent years for the development of electrome-
chanical harvesting devices. Usually connected to a structure, these kinds of materials convert kinetic energy into

electric energy, and your electronic parameters interact directly to the vibrations of the system they are coupled
on. Therefore, this work aims at comparing the use of genetic algorithms and artificial neural network techniques
in the implement of shunt control in a structural set of a cantilever beam coupled to a piezoelectric layer in the

piezo-beam configuration. For the architecture of the genetic algorithm and the neural network, was used a soft-
ware with finite element model implemented and the comparisons were made analyzing the computational demand

of the algorithms and their respective responses when both of them were defined on the task of finding the best
combination between the parameters of resistance and inductance of the piezoelectric patch that result in the best
damping to the structure. The comparison between the techniques had a focus on the use of the objective function

of the system by them, parameter used as a metric to gauge the aggregate computational demand, and the damp-
ing provided with the respective configurations suggested by the two techniques.The results show that the neural

network after training completes your execution in order of 102 seconds, much faster than the genetic algorithm,
presenting a response with an average gain in damping of 23,24dB, but, even though faster, this technique demands
much more iterations than the genetic algorithm, due to its nature of parallel computations, and additional care for
the input data, that need a pre-processing not seen in the genetic algorithm technique.