Implementation of Adaptive Neuro Fuzzy Inference System controller on magneto rheological damper suspension
- 1 July 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1399-1403
- https://doi.org/10.1109/aim.2013.6584290
Abstract
In this paper, we consider the implementation of an Adaptive Neuro Fuzzy Inference System (ANFIS) technique to control a quarter vehicle suspension systems with a semi-active magneto rheological (MR) damper. A quarter-car suspension model together with the MR damper is set up, and a semi-active controller composed of the Skyhook-Groundhook controller and the ANFIS model is designed. The design of ANFIS method use Fuzzy model with Gaussian membership function configuration and back propagation method as learning Fuzzy's parameter. This control strategy is proposed to compensate the uncertainties problem of MR damper. Finally, in the simulation of the comparison of semi active suspension ANFIS controller, Skyhook-Groundhook controller and Passive Suspension is shown using MATLAB. The simulation results demonstrate that the ANFIS method provides better isolation than that via Passive Suspension.Keywords
This publication has 7 references indexed in Scilit:
- Analysis and Experimental Study of Magnetorheological-Based Damper for Semiactive Suspension System Using Fuzzy HybridsIEEE Transactions on Industry Applications, 2010
- Comparative research on semi-active control strategies for magneto-rheological suspensionNonlinear Dynamics, 2009
- Optimal design of MR shock absorber and application to vehicle suspensionSmart Materials and Structures, 2009
- Modeling and Control for a Semi-active Suspension with a Magnetorheological Damper Including the Actuator DynamicsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- A novel neuro-fuzzy controller to enhance the performance of vehicle semi-active suspension systemsVehicle System Dynamics, 2008
- Transient Dynamics of Semiactive Suspensions with Hybrid ControlJournal of Intelligent Material Systems and Structures, 2006
- Vibration Control Using Semi-Active Force GeneratorsJournal of Engineering for Industry, 1974