Dynamic modeling of magnetic suspension isolator using artificial neural network: a modified genetic approach
- 7 February 2012
- journal article
- research article
- Published by SAGE Publications in Journal of Vibration and Control
- Vol. 19 (6), 847-856
- https://doi.org/10.1177/1077546311433608
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.Keywords
This publication has 15 references indexed in Scilit:
- A general dynamics and control model of a class of multi-DOF manipulators for active vibration controlMechanism and Machine Theory, 2011
- A Novel Semi-active Magnetorheological Bushing Design for Variable Displacement EnginesJournal of Intelligent Material Systems and Structures, 2008
- Semi-active vibration isolation system with variable stiffness and damping controlJournal of Sound and Vibration, 2008
- Semi-active control of shallow cables with magnetorheological dampers under harmonic axial support motionJournal of Sound and Vibration, 2008
- Neuro-fuzzy Active Control of Rotor Suspended on Active Magnetic BearingJournal of Vibration and Control, 2007
- Active vibration isolation in a “smart spring” mount using a repetitive control approachControl Engineering Practice, 2006
- An Active Micro Vibration Isolator with Zero-Power Controlled Magnetic Suspension TechnologyJSME International Journal Series C, 2006
- Active Vibration Control of a Modular Robot Combining a Back-Propagation Neural Network with a Genetic AlgorithmJournal of Vibration and Control, 2005
- Active vibration control for marine applicationsControl Engineering Practice, 2004
- PERFORMANCE CHARACTERISTICS OF A VIBRATION ISOLATOR WITH ELECTRO-RHEOLOGICAL FLUIDSJournal of Sound and Vibration, 1999