Population Extremal Optimization Based 2-DOF Control Strategy for Field Oriented Control of Induction Motor

Abstract

In this research work, population extremal optimization (PEO) with a hybrid mutation operation was used to optimize the speed loop’s proportional-integral-derivative (PID) controller of the indirect field-oriented control (IFOC) of a three-phase induction motor (IM). A two-degree-of-freedom (2-DOF) structure of the speed control loop for smoothing the electromagnetic torque responses without manipulating the current controllers was proposed. It was formed by considering the q-axis stator current, to which the electromagnetic torque is directly proportional, as a disturbance variable. The sum of integral time absolute error (ITAE) and a chattering penalty function was used as the objective function for controller optimization. The proposed PEO-based 2-DOF control achieved a lower objective function value than designs based on particle swarm optimization (PSO) and a genetic algorithm (GA). Also, appreciably superior performances of the 2-DOF control over the 1-DOF one was observed in terms of torque smoothing as well as speed tracking. The robustness of the proposed controller was examined by simulating a wide range of parameter variations. The modeling and simulation of the system was conducted in a MATLAB/Simulink platform.