Neural identification and control of a linear induction motor using an /spl alpha/ - /spl beta/ model

We present a new method to control a linear induction motor (LIM) using dynamic neural networks. First, we propose a neural identifier of triangular form; this neural model has the structure of a nonlinear block controllable form (NBC). Then, a reduced order observer is designed in order to estimate the secondary fluxes. Finally, a sliding mode control is developed to track velocity and flux magnitude. Simulations are presented to illustrate the applicability of the proposed scheme.