Stepper motor trajectory tracking via dynamic block form neural networks

The authors present a novel approach to control this kind of motor. Modifying the published results for nonlinear identification using dynamic neural networks, they propose a new neural network identifier of block form. Based on this model a control law, which combines sliding mode and block control, is derived. This neural identifier and the proposed control law allow trajectory tracking for stepper motors. Applicability of the approach is tested via simulations.