A New Hybrid Ant Colony Optimization Based PID of the Direct Torque Control for a Doubly Fed Induction Motor

Abstract

Due to its advantages, the Proportional Integral Derivative (PID) controller has been the most widely used controller in the industrial sector. It allows linear systems to have good performance, but if the system is subjected to physical variation conditions, the system’s behavior becomes non-linear, in which case the PID controller is insufficient. The use of the PID controller for speed control in rotating machines, such as the doubly fed induction motor (DFIM) is widely used, but the non-linearity of the machine parameters allows for undesirable behaviors, resulting in overshoots and torque ripples. For this reason, several techniques have been adopted to increase the DTC’s robustness. One finds the integration of artificial intelligence as optimization algorithms. These algorithms are used to generate gains close to the optimum, converging the behavior of the DFIM to its optimum. In this work, an Ant Colony Optimization (ACO) algorithm was proposed to adjust the PID controller gains of the DTC control to control the DFIM, using a combined weighting cost function, to obtain efficient torque and speed control. This paper presents a new hybrid structure resulting from the intelligent ACO-DTC control implemented on Matlab-Simulink. The performance results extracted from the simulation showed the effectiveness of the intelligent ACO-DTC control, which provides satisfactory performance in terms of rapidity, stability, precision, and torque ripples compared to the conventional DTC.