<![CDATA[Some systems require mechanical power, which can be used in many applications, including rotating vehicle wheels, pulling elevators, and moving robot limbs, etc. Mechanical or kinetic energy can be produced and generated from electrical machines, which can be represented by an electric motor, which is a machine that operates on electrical energy, i.e. input energy, and produces mechanical energy, i.e. output energy. One of the most common and widely used motors is the DC motor, which has features that make it a matter of interest to researchers, producing and manufacturing companies to develop and improve its performance. The motor is characterized by flexibility, low cost, durability, and the ability to control the speed and position of the rotating member using traditional, expert and intelligent control systems to achieve appropriate performance according to the field of application. In linear systems, traditional systems (Proportional-Integral-Derivative Controller (PID) have succeeded, while their performance is weak and unacceptable in nonlinear systems. Therefore, expert and intelligent control systems are relied upon to improve the performance of electric motors. It is proposed to implement and operate an electric motor control system using the genetic algorithm to verify its effectiveness in improving performance compared to the traditional one (PID). The genetic algorithm was chosen to address the optimization challenges because it is commonly used in artificial intelligence applications in various fields that are suitable for real time. Therefore, this study presented improving the performance of the traditional controller using the genetic algorithm. Through comparison, the possibility of improving the system performance with changing operating conditions was verified by adjusting the parameters of the traditional controller. The simulation was performed using Matlab, and the DC motor specifications included a rated voltage of 32.4 V, a rated current of 2 A, a rated speed of 536 rad/s, and a power of 54 watts. The conventional controller is responsible for the basic feedback control, while the GA-PID controller optimizes the control parameters to improve the system performance.]]>
