## 1 Introduction  
The development of new signal processing techniques paved the way for the creation of far more complex control structures. The DTC control approach was adopted in several studies [1; 2; 3; 4]. The benefits of the DTC method (dynamics, robustness, high performance, and simplicity of implementation) are negatively influenced by the employment of hysteresis comparators. The comparators, in theory and practice, allow variable frequency as well as finite frequency sampling, allowing a speed response with pseudo-random overshoot [5; 6]. As a result, the machine's behavior is affected, particularly at low speed and during the variation of the machine parameters [7]. The harmonic content of the different output signals is difficult to anticipate due to these variables. In addition to that, applying the traditional DTC control on the DFIM generates significant torque oscillations, causing mechanical resonances and vibrations, as well as strong noise, leading to rapid machine aging [8]. The working principle of flux-oriented control (FOC) is to restore the DFIM's behavior to that of a DC machine in order to maintain torque-flux decoupling [9; 10; 11]. However, under the scenario of direct flux-oriented control (DFOC) [11; 12], this approach necessitates a sensor situated in the air gap for a direct detection of the flux, and the sensors are susceptible to mechanical damage (vibration, temperature, etc.) [12], and the indirect flux-oriented control (IFOC) eliminates the utilization of a flux sensor; however, the main drawback of this approach that the estimation is extremely sensitive to changes in the machine's parameters, specifically the rotor and stator time constants. Thus, the control is regulated by six standard PID regulators, causing all of the system behaviors to be very sensitive to parametric variation. Many academics have recently proposed artificial intelligence-based methods for improving the performance of conventional DTC (e.g., neural networks and fuzzy logic) and combined controls, not mandating information on the mathematical model. For responding to parametric changes, these methods are based on chance. The authors of [13] recommended employing artificial intelligence approaches to enhance the dynamic performance of direct torque control. Direct neural torque control (DTNC), direct torque fuzzy control (DTFC), and direct neural fuzzy torque control (DTNFC) are some of the terms used to describe them, and they utilize artificial neural networks and fuzzy logic to generate a voltage vector that can direct flux and torque toward their set points over a set time, replacing truth tables and hysteresis comparators with controllers based on artificial intelligence; these techniques have been very successful. These approaches, in the case of DTC, allow the adjustment of the switching frequency, which results in faster fluxes and torque responses with less distortion. However, the underlying structure of the DTC-based method is more complicated and requires a powerful computer, which are the major disadvantages of solutions based on artificial intelligence [14]. A modern DFIM control approach developed in [15] was used to reduce torque ripples by applying a genetic algorithm of DTC control to the stator and optimizing the PID speed controller's parameters. A total of 12V and 5 Hz were applied to the rotor as power sources. However, for such circumstances, the DFIM functions as an induction motor (IM). You cannot drive the DFIM at excessive speeds [16]. The analysis in this article focused on the optimization of PID gains by deploying a GA andthe execution of a hybrid GA-DTC control deployed to the DFIM in connection with the two voltage inverters, in order to profit from the whole range of speed fluctuations, reduce the inverters' joule losses, and address the difficulties stated above [17; 18]. The finest advantages indicated at the start of this paragraph are found in this architecture. The GA optimizes the parameters KD, KI, and KP of the DTC's PID speed controller.