Adaptive H∞ Recurrent Fuzzy Neural Network Control for Synchronous Reluctance Motor Drive

An adaptive H-infinity control system which being composed of robust controller with H-infinity attenuation technique, recurrent fuzzy neural network (RFNN) and compensated control with adaptive law is proposed to control the rotor of a synchronous reluctance motor (SynRM) for the position tracking. First, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRM servo drive. Then, the robust performance control problem is formulated as a nonlinear H-infinity problem under the influence of uncertainties. Moreover, the adaptation of the RFNN is to approximate the requirement for the bound of lumped uncertainty, and a compensated controller with adaptive law is investigated to compensate the minimum approximation error. Finally, experimental results are provided to demonstrate the effectiveness of the proposed control schemes.