Dynamic modeling of magnetic suspension isolator using artificial neural network: a modified genetic approach

Abstract

Active vibration isolation technology has been widely used to reduce vibration transmission in many different engineering systems. Magnetic suspension isolator (MSI), as an active isolation actuator, has shown advantages including non‐contact, high response frequency, high reliability and long life‐span. However, its potential has not been fully explored due to the nonlinear and hysteretic behavior in a dynamic environment, and there is limited research work in the area. This paper proposes a new artificial neural network (ANN)‐based approach to model the dynamics of MSI. A modified genetic algorithm (MGA) is developed to train the ANN to improve the model accuracy. Results clearly show that the ANN model with the MGA approach outperforms the back propagation (BP) approach and the analytic method based on the least squares fitting method.