An Online Trained Adaptive Neural Network Controller for an Active Magnetic Bearing System

Abstract

In this paper, an intelligent control method to position an active magnetic bearing (AMB) system is proposed, using the emergent approaches of fuzzy logic controller (FLC) and online trained adaptive neural network controller (NNC). An AMB system supports a rotating shaft, without physical contact, using electromagnetic forces. In the proposed controller system, an FLC was first designed to identify the parameters of the AMB system. This allowed the initial training data with two inputs, the error and derivate of the error, and one output signal from the FLC, to be obtained. Finally, an NNC with online training features was designed using an S-function in Mat lab software to achieve improved performance. The results of the AMB system indicated that the system exhibited satisfactory control performance without overshoot and obtained improved transient and steady-state responses under various operating conditions.