Sliding mode control of a feedback linearized induction motor using TS fuzzy based adaptive Iterative Learning Controller

Abstract

This paper presents an application of a Takagi Sugeno (TS) Fuzzy logic based adaptive Iterative Learning Controller (ILC) to reduce chattering, torque ripple and to improve dynamic performance of a feedback linearized induction motor drive with sliding mode controller for periodic speed tracking. This ILC is connected to the forward path of sliding mode speed control loop. At first, the state feedback linearization technique is used for decoupling speed and flux control loop. It uses reference frame transformation and control in a stationary (α-β) frame with rotor flux and stator current components as the state variables. Since the induction motor drive system is sensitive to parameter variation, model uncertainties and load disturbances, a robust control strategy based on sliding mode is designed. In the sliding mode based scheme the chattering of state and control variables and torque ripple are present. To reduce chattering and torque ripple, a TS fuzzy logic based adaptive ILC is designed. Both control schemes are simulated in SIMULINK environment. Simulation results demonstrate that the performance of sliding mode cum TS fuzzy logic based adaptive ILC is better than the scheme with only sliding mode controller. These simulation results are also verified with real time simulator, RT Lab.