Online H∞ speed control of sensorless induction motors with rotor resistance estimation

This paper proposes an online technique for the H_∞ control of speed and flux norm of current-fed induction motors (IM). Integrals of the speed and flux norm tracking errors are considered as elements of the state variables. A robust H_∞, optimal control strategy for this problem is determined by solving the algebraic Riccati equation (ARE), using multilayer recurrent neural networks. The proposed controller allows for the simultaneous and independent control of both speed and flux norms of the induction motors. The control implementation involves estimating the rotor flux, rotor resistance, and speed of the induction motor, using continuous-time extended Kalman filter (EKF). The simulation results show the effectiveness of the proposed controller even in the presence of rotor resistance and load torque disturbances.