# Electric Drive with an Adaptive Controller and Wireless Communication System
## 1 Introduction  
### Problem Formulation  
The problem of precise speed control is still prevalent in industrial electrical drives with a complex mechanical structure [1]. Usually, analysis of control schemes is performed under the assumption that the connection between the motor and the load is rigid and does not negatively affect the drive. The elasticity of the shaft and gears can become a source of disturbance and oscillations [2, 3]. It can lead to poorer product quality, damage to mechanical parts of the drive, or problems with stability. In the literature, two machines coupled with a flexible element are often referred to as a two-mass system and are the subject of numerous publications [4, 5, 6]. Examples of two-mass systems observed in the industry include rolling mills [7], wind turbines [8, 9], and robotic manipulators [10, 11, 12]. Because of the requirement for high dynamics and the presence of oscillations, such applications must be controlled with more intricate control schemes. PID cascade control is one of the most frequently used strategies due to its low computational power demand and the simplicity of practical implementation [13, 14]. The gains of the controller can be adjusted with the pole distribution method or the modulus and symmetry criteria. However, the structure does not take any information about the load machine. It is not suitable for applications with varying speeds between the motor and the load [15]. In such cases, the problem can be solved by providing additional feedback from other variables [16] or using a state feedback controller [17, 18]. In a state controller, all state variables (angular speed of the motor and load, and the torsional torque) are connected back to the speed controller to compensate for the elasticity of the coupling element. Nevertheless, both of these strategies suffer one significant disadvantage--theyare susceptible to changes in plant parameters. The gains of these controllers are set to work correctly for a selected operating point. The error margin is narrow; even slight changes in plant parameters or a sudden appearance of disturbances can lead to loss of control precision (reference speed tracking) [19]. To address the issue of parameter uncertainty, adaptive control schemes are often applied. Among the possible approaches, gain scheduling [20] and self-tuning methods [21] can be listed. One of the most efficient solutions is model reference adaptive control (MRAC) [22; 23]. In MRAC, the difference between the model of the system and the actual output is fed to the adaptation algorithm, then new values of the controller are established. In this way, the controller remains correctly updated. Additionally, only approximate values of the plant parameters are needed to establish the initial value of the gains. There are two main downsides to this strategy [24]. The adaptation algorithm must be carefully chosen to ensure the stable operation of the drive, which is a difficult task. Furthermore, the use of the reference model can nullify the original non-linearities of the plant. This effect can negatively influence the dynamics of the system if the model is selected inappropriately. The model-free approach relies on the use of intelligent structures, such as neural networks, which are widely used in different tasks related to electrical drives. They can be implemented to estimate state variables [25] or the state of charge (SoC) of electric vehicle batteries [26]. Deep learning algorithms are used to facilitate fault detection [27]. Neural structures are also applied to the task of controlling the angular speed of electric motors [28]. In this concept, gradient algorithms are used to adjust the weights of the network during drive operation. Adaptive properties are achieved through the use of online training. The design process includes the selection of the network structure and the learning coefficient. The stability of neural controllers can be proven through the use of the Lyapunov stability theorem [29].