Adaptive backstepping control for a PMSM drive using RFNN uncertainty observer

Abstract

In this paper an adaptive backstepping control system is proposed to control the rotor position of a permanent magnet synchronous motor (PMSM) drive using recurrent fuzzy neural network (RFNN). First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, an adaptive backstepping approach is proposed to compensate the uncertainties in the motion control system. With the proposed adaptive backstepping control system, the rotor position of the PMSM 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 PMSM drive, a RFNN uncertainty observer is proposed to estimate the required lumped uncertainty in the adaptive backstepping 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.