## 1 Introduction  
Modeling of electric drives of machines and vehicles is currently one of the most explored fields of control engineering. Motion control systems are often equipped with permanent magnet induction machines working as driving components. In the electric part of a control system, the terminal voltage or the armature current is available for speed or position estimation, since, for the mechanical system state of driven inertia, the rotational displacement or velocity is measured with the use of additional sensors. Control of the speed of a Direct Current (DC) motor with conventional Proportional Integral Derivative (PID) and non-conventional Artificial Neural Network (ANN) methods is taken into consideration in Reference [1]. Direct current motor model and performance indicators criteria are defined. For non-conventional method inverse model control schema is used, where a 3-layer Neural Network (NN) inverse model is trained by back-propagation algorithm. Considering the application of the motor in robotics, the study shows an interesting technique of improving the closed system's dynamics, where there is no need to know the model parameters when designing the system control. Reference [2] applies an adaptive PID controller dependent on the extra blunder of a turn around control sign to comprehend non-linearity and parameter varieties. In both cases, the PID controller is tuned in MATLAB. Adaptive tracking control taken into account also in this work proved its efficiency in Reference [3]. There can be found an adaptive velocity trajectory tracking control for a nonlinear shunt direct current electric machine subjected to unmeasurable variable loading torque and parametric uncertainty. Again, ANN and dynamical tracking error compensators are combined to avoid dependence on detailed mathematical models of uncertain nonlinear systems and significantly improve the controller robustness and efficiency. Other similar examples include: a sensorless speed tracking using an ANN [4], flatness-based control [5], or an adaptive backstepping control [6]. Similarly, in Reference [7], a comparative studies on different meta-heuristic type adaptive control for the speed regulation of the DC motor with parameters uncertainties are addressed. The work includes an overview with testing of several adaptive controllers based on the differential evolution, Particle Swarm Optimization (PSO), Bat Algorithm (BAT), Firefly Algorithm (FFA), and Wolf Search Algorithm (WSA) algorithms are proposed in the on-line tuning of the parameters used in calculating the motor control signal. Simulations show the efficacy of each control strategy. Fractional-order brushless DC motor system is introduced in Reference [8], wherein there exist unknown dynamics, immeasurable states and chaotic oscillation. The authors propose an anti-oscillation and chaos control aimed at immeasurable states. The high-gain observers with fast convergence allowed to obtain information about system states. The system uncertainties are compensated using a finite-time echo state network with a weight to approximate uncertain dynamics, while its weight is tuned by a fractional-order adaptive law. An FFA-based Fractional-Order PID (FOPID) controller is proposed in Reference [9] for a Brushless DC (BLDC) motor to achieve effective control of torque and speed. In a conventional control, the torque references are converted into current references and phase currents are controlled using current controllers. It is not effective, resulting in a ripple in the torque. The authors propose a direct instantaneous scheme in which reference torques are compared with those estimated directly and the error is given to FOPID controller tuned using the FFA technique. A direct adaptive Fuzzy Logic Controller (FLC) studied in Reference [10] for precise control of the DC motor speed is estimated from two levels, where the lower level uses a Mamdani fuzzy controller and the upper level is an inverse model based on a Takagi-Sugeno approach.