# Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor
## 1 Introduction  
A direct current (DC) motor has many applications in the industrial sector [1], such as aeronautics, robotics, automation, manufacturing, automotive, etc. Therefore, it is constantly subjected to multiple experiments or investigations in identification, estimation, and parametric control to create solutions in different areas of research that could have applications in real life. For this, the precise control of motor parameters is required to achieve efficient operation [2], and model optimization should help achieve this goal. Sometimes, models are simulated mathematically, drifting in predictions, control monitoring, and diagnosis. Motor parameters vary with features such as misalignments that can lead to poor motor performance. The design of DC motor controllers could have drawbacks, as they are sensitive to high tolerances [3]. Usually, the parameters specified by manufacturers are somewhat robust, and it is necessary to generate more precise models that help us make a more efficient motor. With precise models, diagnostic systems can be developed, as shown in the research by ref. [4], where a diagnostic method based on a rotorslip was designed to detect faults in the current and speed sensors during the induction motor operation. Another example is from ref. [5], where a technique based on current estimation for fault detection and speed sensor failure for an induction motor is developed. The precise knowledge of mathematical motor models allows for the bypassing of sensors and efficient speed control, as shown in the research by ref. [6], where high-performance field-oriented motor control requires the accurate knowledge of the flow of information and the motor speed. These investigations denote the importance of motor modeling and parameterization, since they allow for better fault-tolerant control, reduce the number of sensors, and even improve control precision. For example, an improved loss minimization technique based on fuzzy logic for a brushless DC motor (BLDC) drive system is presented in [7]. In addition, the implementation of a platform for testing an algorithm for the closed-loop speed control of a DC motor was sought in [8]. These have not been the only works that have focused on controlling speed through algorithms. Speed control algorithms have also been designed based on the estimation of dynamic error parameters under uncertainty and load variation [9]. Heuristic algorithms solve optimization problems defined by intuitive approximations, and solutions to these problems are intelligently thought out even if the solution is not the best. In recent years, heuristic procedures have been developed to optimize software design problems and component reuse. Some research shows the usefulness of this type of algorithm and its application in parametric evaluation [10]. Some of the proposed limitations of heuristic algorithms have been overcome with more robust methods based on statistics, such as in ref. [11]. However, it is necessary to continue innovating [12]. Investigations such as [13] propose an algorithm that uses a stochastic method to reach optimal points within simple algorithms. A comparison of heuristic algorithms for the identification of DC motor systems was carried out in [14] through discrete Proportional-Integral (PI) controllers to analyze the system response. Fast and accurate convergence rate results were obtained with the extended Kalman filter (EKF) algorithm. A heuristic method is a well-known set of steps that is used to quickly identify a high-quality solution to a given problem. It consists of intuitive mathematics with a design of static and manual rules that are supposed to give a good solution, although not necessarily the optimal solution, to the problem. For this reason, it is important to compare them with metaheuristic algorithms and observe which offer the highest optimization. Metaheuristic algorithms have acquired an important role in recent years.