ANFIS controller for an Active Magnetic Bearing system

This paper proposes an intelligent control method for positioning an Active Magnetic Bearing (AMB) system, using the emerging approaches of the Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS). An AMB system depends on control of the air gap between the stator and the rotor. In practice, no precise mathematical model can be established because the rotor displacement in this AMB system are inherently unstable and the relationship between the current and electromagnetic force is highly nonlinear. Fuzzy logic has emerged as a mathematical tool to deal with the uncertainties in human perception and reasoning. 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. Recently, these two fields have been integrated into the emergent field of fuzzy neural networks. 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 adjust suitably the parameters of the fuzzy controller, and the output control signal tracks the input signal. This method can be applied to improve the control performance of nonlinear systems. The output signal responses transient performance of systems using a fuzzy-neural network that must be trained through a learning process to yield suitable membership functions and weightings. The results of a simulation of the AMB system indicated that the system responds with satisfactory control performance without overshoot, with a zero-error steady-state, and a rise time of 0.12±0.02 seconds. The proposed controller can be feasibly applied to AMB systems with various external disturbances, and the effectiveness of the ANFIS with self-learning and self-improving capacities is proven.