Parameter Tuning of Sliding Mode Speed Controller of Induction Motor Drive Using Teaching-Learning Based Optimization Algorithm

Abstract

The optimal settings of the Sliding Mode Controller for three-phase Induction Motor speed regulation are proposed in this paper using a Teaching-Learning Based Optimization Algorithm. It is a straightforward metaheuristic algorithm that simulates the teaching-learning processes in a classroom. Indirect Field Oriented Control is used in this induction motor drive, which allows the motor torque and the flux to be separated, similar to a DC motor. The inner loop uses proportional-integral current controllers, whereas the outside loop uses a sliding mode speed controller. In this paper, the sliding mode controller uses a tanh function as a switching function. Integral Time Absolute Error and Zwe-Lee Gaing's parameters are used as objective functions for tuning sliding mode controller settings. Performances of the proposed scheme in terms of the transient response analysis are compared with the swarm intelligence-based algorithms, namely the modified Particle Swarm Optimization and Grey Wolf Optimization. MATLAB/ SIMULINK software is used to build the three algorithms and sliding mode controller-based induction motor drive models, which are then analysed using simulation experiments. In comparison to the modified Particle Swarm Optimization and the Grey Wolf Optimization algorithms, Teaching-Learning Based Optimization yields the lowest value of the objective function Zwe-Lee Gaing's parameter and it converges quickly.