Adaptive Backstepping FNN Control for a Permanent Magnet Synchronous Motor Drive

The permanent magnet synchronous motor (PMSM) drive system using adaptive backstepping fuzzy neural network (ABFNN) control is investigated for the tracking of periodic reference inputs. 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 mover 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 FNN uncertainty observer is proposed to estimate the required lumped uncertainty in the adaptive backstepping control system. The effectiveness of the proposed control scheme is verified by the experimental results.