## 1 Introduction
### Analysis of the Latest Research and Publications  
The results of the analysis of the scientific and technical literature show significant progress in the research of algorithms for the predictive control of electric drives to minimise losses, both on the basis of offline and online control strategies. For example, a scientific article [13] proposes the use of model predictive control by means of dynamic programming methods to design optimal rotor flux and current trajectories based on an approach in which templates are predefined offline and then adapted to a profile specific to each application. The studies in Refs. [14; 15] demonstrate approaches to determine the optimal motor size and duty cycle duration through loss minimisation. However, although offline methods provide significant results in terms of efficiency, they have certain limitations in terms of versatility and adaptability. The well-known online methods, which are described in detail in scientific publications [16; 17; 18; 19], are based on the application of adaptive models using various control laws. For example, the authors of Ref. [17] studied the efficiency of the ANFIS controller under variable load conditions. The authors of Ref. [18] considered the combination of direct torque control (DTC), fuzzy logic, and teamwork optimisation (TOA) algorithms, which provides an increase in the efficiency of an asynchronous motor by 10-15% under dynamic operating conditions. The study described in Ref. [19] focuses on the practical increase in the mileage of an electric vehicle through the development and implementation of energy-efficient control methods and tools. A significant step forward is the development of model predictive control (MPC) strategies that take into account a number of nonlinear constraints [20]. To date, a significant number of high-quality research results are available regarding various approaches to control electromechanical systems using MPC algorithms with a fixed optimisation horizon, sliding window, adaptive strategies, etc. [21; 22; 23]. The authors of these scientific publications demonstrated that adaptive schemes allow better adaptation to changing conditions, but require flexible mechanisms for recalculating constraints and loss models in real time, which under practical conditions, requires significant computing resources using specialised DSP and FPGA modules. Also, the authors of Ref. [24] studied approaches and models for the probabilistic analysis of dynamical systems, which at random moments in time, transit from one state to another, taking into account limited computing resources [25]. Today, the interest of the scientific and engineering community in using computer modelling methods to accelerate online optimisation is growing rapidly. In particular, the latest approaches use various methods of fast optimisation, which allow for implementing predictive control using even computationally limited platforms [26]. However, the accuracy of such simplified models largely depends on the correct identification of model parameters and the consideration of constraints, which negatively affects their universality. In addition, recent studies of the use of predictive control for a permanent magnet synchronous motor (PMSM) drives contribute valuable insights that are potentially transferable to asynchronous drives. In Ref. [27], a nonlinear extended state observer was used to enhance the robustness of model predictive current control against model uncertainties and disturbances. Another work [28] proposed a motor-parameter-free model predictive voltage control based on an incremental model, which significantly reduces sensitivity to motor parameters and simplifies implementation. These developments highlight the ongoing trend toward increasing robustness and reducing model dependency in MPC strategies. In addition, studies that combine MPC methods with neural network predictive models are of particular interest [29; 30].