Synchronization of permanent magnet electric motors: New nonlinear advanced results

The nonlinear learning control techniques, based on Fourier approximation theory and used by Verrelli (2011) [2] to solve the synchronization problem for uncertain permanent magnet synchronous motors (performing repetitive tasks of uncertain repetition period), are considered in this paper. We show that, if the exogenous rotor position reference signal (which is to be globally tracked without assuming its foreknowledge) is restricted to the class of sinusoidal signals with uncertain bias, amplitude, frequency and phase, a stronger result can be derived by resorting to nonlinear advanced identification techniques. In contrast to Verrelli (2011) [2], neither availability of the rotor speed reference signal is required nor infinite memory identification schemes are used. The application to the problem of synchronizing a drumming robotic arm with a drumming human arm is presented: simulation results show satisfactory closed loop performances and confirm the effectiveness of the proposed solution.