Optimal positioning of piezoelectric actuators on a smart fin using bio-inspired algorithms

In this paper a novel approach is developed for optimization of piezoelectric actuators in vibration suppression. A scaled model of a vertical tail of F/A-18 is developed in which piezoelectric actuators are bounded to the surface. The frequency response function (FRF) of the system is then recorded and maximization of the FRF peaks is considered as the objective function of the optimization algorithm to enhance the actuator authority on the mode, which assigns the optimal placement of the pair of piezoelectric actuators on the smart fin. Six multi-layer perceptron neural networks are employed to perform surface fitting to the discrete data generated by the finite element method (FEM). Invasive weed optimization (IWO), a novel numerical stochastic optimization algorithm, is then employed to maximize the FRF peak which in due reduces the vibration of the smart fin. Results indicated an accurate surface fitting for the FRF peak data as well as the optimal placement of the piezoelectric actuators for vibration suppression.