Abstract
The parametric variation of nonlinear systems remains a significant drawback of automatic system controllers. The Proportional-Integral(PI) and Proportional-Integral-Derivative (PID) are the most commonly used controllers in industrial control systems. However, with the evolution of these systems, such controllers have become insufficient to compete with the complexity of the systems. This problem can be solved with the help of artificial intelligence, and especially with the use of optimization algorithms, which allow for variable gains in PID controllers that adapt to parametric variation. This article presents an analytical and experimental study of the Direct Torque Control (DTC) of a Doubly-Fed Induction Motor (DFIM). The speed adaptation of the DFIM is achieved using a PID controller, which is characterized by overshoots in the speed and ripples in the electromagnetic torque. The Genetic Algorithm (GA) within the DTC shows very good robustness in speed and torque by reducing torque ripples and suppressing overshoots. The simulation of the GA-DTC hybrid control in MATLAB/Simulink confirms the improvement offered by this strategy. The validation and implementation of this strategy on the dSPACE DS1104 board are in good agreement with the simulation results and theoretical analysis.