Adaptive backstepping control for a permanent magnet synchronous motor drive using RNN uncertainty observer

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 neural network (RNN). 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 RNN 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 RNN. The effectiveness of the proposed control scheme is verified by experimental results.