## 1 Introduction  
### Relevance of the Topic and Research Motivation  
Improving the energy efficiency of electromechanical devices and systems, specifically with the use of an electric drive, is one of the key tasks of the industrial, energy, and transport sectors in the context of global requirements and sustainable development goals to improve energy efficiency and the rational use of resources during technological and production processes [1]. According to recent research data, the share of electricity consumed by electric drives is up to 46% of the total, with the vast majority of this use coming from dynamic modes of operation (up to 80%) in the context of the industrial and commercial sectors [2]. The most typical example that proves the above statistical indicators is the functioning of electric vehicle drives, where the speed and torque dynamically change throughout the entire operating cycle [3; 4; 5], specifically in response to variations in electrical parameters (e.g., stator current, rotor flux, voltage) and the subsequent impact on energy efficiency and control precision.  
In practical terms, electric drives with model predictive control are widely used in diverse applications where dynamic performance and energy efficiency are critical. For example, in electric vehicle systems, MPC allows for better transient response, torque tracking, and disturbance tolerance [6]. In industrial belt systems, predictive control allows for cost-optimal operation in terms of coordination between the operation status of belt conveyors and the time-of-use tariff [7]. Additionally, water utilities benefit from the MPC of water supply systems to minimize energy costs [8]. These cases demonstrate the relevance of parameter-optimised MPC in modern electromechanical infrastructure.  
In dynamic modes, power losses are not constant and significantly depend on changes in the stator current, rotor flux, switching frequency, and other parameters of electromechanical systems. While steady-state modes are already well researched and in most cases, provided with energy-efficient solutions (maximum torque per ampere (MTPA) strategy, field-oriented control (FOC) methods, direct torque control (DTC) methods), the vast majority of available technological solutions require further development to optimise transient losses, which in most production processes of industrial facilities and electric transport, account for a significant proportion of the operating time [9; 10; 11; 12].  
The above problem is further complicated by the limitations associated with the need to take into account factors as voltage and current limits, as well as nonlinear effects such as magnetic circuit saturation, losses in the core, etc. Most existing models do not allow for the implementation of hardware and software accounting for these parameters and effects in real time, which reduces the overall efficiency and adaptability of control systems. All this proves the relevance and necessity of solving the scientific and applied problem of the development and further elaboration of adaptive methods and model predictive control algorithms of electric drives capable of minimising energy losses in dynamic modes, taking into account destabilising factors and limitations.