Fourier series expansion for synchronization of permanent magnet electric motors

Learning control techniques, based on Fourier approximation theory, are used to solve the tracking control problem via state (rotor position and speed and stator currents) feedback for permanent magnet synchronous motors performing repetitive tasks: smooth rotor position and speed reference signals, whose foreknowledge is not assumed and which are periodic of uncertain period, are required to be tracked for any motor initial condition. The effectiveness of the proposed solution is illustrated by its successfully application to a master–slave synchronization problem. The presented result illustrates the high potentiality of merging together functional approximation theory and advanced nonlinear control techniques.