Abstract
In‐house prototype model of an active magnetic bearing is built and using its physical parameter values a linearized transfer function of order two, is mathematically obtained for a specific point of operation. In this work, a closed loop active magnetic bearing system is proposed and controller for this closed loop is designed using fuzzy logic control. Two different fuzzy inference systems: Mamdani and Sugeno fuzzy inference system constructed individually with eleven different types of membership functions separately. The considered membership functions are—linear, generalized bell‐shaped, Gaussian distribution based, quadratic, cubic polynomial and sigmoidal curve‐based membership functions. Calculating the transient state and steady state parameter values together with the controller output allows to see how these various membership functions perform on the proposed closed loop active magnetic bearing system. Observed data shows that changing the fuzzy inference system for a specific membership function will result in 67.41% reduction in the overshoot value, 29.70% reduction in settling time value, 10.42% change in rise time value and 2.91% variation is peak time value. Practicality of all membership functions are analysed based on calculated data and their performances with the proposed model.
In‐house prototype model of an active magnetic bearing is built and using its physical parameter values a linearized transfer function of order two, is mathematically obtained for a specific point of operation. In this work, a closed loop active magnetic bearing system is proposed and controller for this closed loop is designed using fuzzy logic control.