Adaptive backstepping RFNN control for synchronous reluctance motor drive

An adaptive back stepping recurrent fully neural network (ABRFNN) control system is proposed to control the rotor position of a synchronous reluctance motor (SynR) servo drive in this paper. First, the field-oriented mechanism is applied to formulate the dynamic e nation of the SynR servo drive. Then, an adaptive back stepping approach is proposed to compensate the uncertainties in the motion control system. With the proposed adaptive back stepping control system, the rotor position of the SynR drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the SynR drive, a RFNN uncertainty observer is proposed to estimate the required lumped uncertainty in the adaptive back stepping control system. In addition, an on-line parameter training methodology, which is derived using the gradient descent method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by experimental results.