ABSTRACT
Magneto rheological fluids (MRF) are intelligent smart materials whose rheological characteristics change rapidly and are under the control of an applied magnetic field. MRF-based devices are successfully commercialized for industrial purpose. Its application for automotive braking has wide scope, and researchers are extending work to bring it to practical use. It has the ability to replace conventional hydraulic brakes (CHB). This controllable yield stress produces shear friction on the rotating disks, generating the braking torque. Magneto rheological brake (MRB) has advantages over CHB in terms of a faster response time. Hence, these brakes are being investigated for vehicular applications. Artificial intelligence is a way of making a computer software think intelligently, in a manner similar to how intelligent humans think. An effort has been made to implement fuzzy logic control strategy for the estimation of MRB torque. In this work, fuzzy logic theory is applied to assign membership function (MF) for inputs and output. Assigned MFs are used to define a rule-based system for fuzzy logic controller proposed for the existing MRB. MFs are assigned for inputs and 32output. Singular value decomposition method based on Sugeno computation is used for the estimation of brake torque.
