# SHUNT CONTROL ON CANTILEVER BEAM BY NEURAL NETWORK : OBJECTIVE FUNCTION

## Palavras-chave:

Shunt Control, Neural Network, Genetic Algorithm, Objective Function, Smart Structures## Resumo

Piezoelectric materials have been extensively studied in recent years for the development of

electromechanical 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 software 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

damping 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.