Adaptive Backstepping Control for SynRel Motor Drive Using FNN Uncertainty Observer

Abstract

An adaptive backstepping control using fuzzy neural network (FNN) uncertainty observer is proposed to control the rotor position of a synchronous reluctance (SynRel) motor drive in this paper. First, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRel motor 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 SynRel motor 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 SynRel motor drive, a FNN 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 FNN. The effectiveness of the proposed control scheme is verified by experimental results.