Identification of piezoelectric LuGre model based on particle swarm optimization and real-coded genetic algorithm

This paper deals with the identification of a piezoelectric actuator model parameters considering LuGre model used in friction modelling. The identification is based on two artificial intelligence techniques: the particle swarm optimization (PSO) and the real-coded genetic algorithm (RGA) techniques. The identification procedure is performed using a high precision actuator. Experiments are carried out to optimize algorithms parameters and a comparison between the two techniques is presented.