ANFIS CONTROLLER FOR ACTIVE MAGNETIC BEARINGS TO SUSPEND A FLYWHEEL ENERGY STORAGE SYSTEM

Abstract

A flywheel energy storage system (FESS) is an effective energy-saving device. It works by accelerating a rotor flywheel disc at a very high speed and maintaining the energy in the system as rotational energy. Active magnetic bearings (AMBs) are ideally suited for use at high-speed and are so used in FESSs. This work developed a mathematical model of the electromagnet force and rotor dynamics of a flywheel. The systems for controlling the position and velocity of the flywheel were designed based on the emerging approaches of an adaptive neuro-fuzzy inference system (ANFIS). Fuzzy logic has occurred as a mathematical tool to deal with the uncertainties in human perception. It also provides a framework for applying approximate human reasoning capabilities to knowledge-based systems. Additionally, ANFIS has emerged as an intelligent controller with learning and adaptive capabilities. An ANFIS comprises the fuzzy logic controller (FLC) and neural networks (NNs). In the method that is developed herein, the control model uses Takagi-Sugeno fuzzy logic, in which the back-propagation algorithm processes information from neural networks to suitably adjust the parameters of the fuzzy controller, and the output control signal tracks the input signal. This method can improve the control performance of nonlinear systems. The output signal responses transient performance of systems use an ANFIS that must be trained through a learning process to yield suitable membership functions and weightings. The results of the flywheel energy storage system indicated that the system responded with a satisfactory control performance to reduce overshoot, a zero-error steady-state, and short rise time. The proposed controller can be feasibly applied to the flywheel energy storage system with various external disturbances, and the effectiveness of the ANFIS with self-learning and self-improving capacities is proven.