Adaptive H/sub /spl infin// recurrent fuzzy neural network control for synchronous reluctance motor drive

An adaptive H/sub /spl infin// control system which being composed of robust controller with H/sub /spl infin// 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/sub /spl infin// 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.

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