Neural Sliding Mode Block Control of Single-Phase Induction Motors

Single-phase induction motors are widely used for small-scale industries and domestic applications due to their availability, low price, and low maintenance cost. To achieve desired performances of these motors, this paper presents the development of a neural identifier based on a Recurrent High Order Neural Networks (RHONN) on-line trained by an Ex-tended Kalman Filter (EKF) complemented with a discrete-time sliding mode block control strategy to control a Single-Phase Induction Motor (SPIM) mechanical speed and flux. Simulation results illustrate effectiveness of the proposed control scheme to ensure time-varying references adequate tracking. In addition, robustness in presence of parameter variations is achieved.