## 4 Discussion and Prospects for Further Research  
The presented results clearly demonstrate that energy efficiency in asynchronous drive systems is not only a function of hardware or basic control strategy, but also of how well the predictive control parameters are tuned. The study proved that the proposed computerised method of multi-criteria optimisation of predictive control parameters for asynchronous electric drives, which includes a formalised description, a simulation model, and algorithmic principles of predictive control, allows for effective coordination of the main performance indicators of electromechanical systems, such as the accuracy of system operation in accordance with a setpoint trajectory, energy consumption, and computational costs. The proposed combination of taxonomic analysis with computer modelling in the MATLAB and Simulink environment provided the possibility for the adaptive and optimised adjustment of the main technical and functional parameters of the predictive algorithm, which is confirmed by the quantitative results obtained.  
The practical value of the presented approach lies in the creation of a computer model, which is a means of configuring algorithmic software for predictive control of asynchronous electric drives for various purposes, allowing automated control systems to be adapted to changes in operating conditions, without significantly complicating the process of deployment and integration into existing technological and production processes. This is particularly relevant for industrial sectors in which electric drives operate under variable load conditions and require flexible control to ensure optimal energy efficiency.  
Although the presented study focuses on standard squirrel-cage induction motors with cylindrical geometry (typical asynchronous electric motor with a rated power of 370 W, which represents standard industrial configurations), the proposed optimisation  
Figure 8: Simulation results, where Fmincon indicates plots obtained using fmincon; Before indicates plots obtained using GRAMPC with initial guess settings; After indicates plots obtained using GRAMPC with optimised settings.  
methodology can, under additional experiments and investigations, be extended to other electric machines such as PMSMs, flat or pancake motors, and others. The proposed parameter tuning approach remains applicable, provided that their specific electromagnetic characteristics are accurately reflected in the model.  
At the same time, the scientific and applied results obtained open up prospects for further research in the following areas:  
* Scaling up the proposed method by introducing hybrid methods, specifically by using neural networks and machine learning methods for automatic real-time adjustment of predictive models;
* Researching the effectiveness of the developed method in regards to electric drives with other types of motors;
* Validating and adjusting the results obtained based on practical experiments in various industry applications;
* Integrating the developed approach into modern automation and digitalisation complexes for production and technological processes.  
A main focus of future work will be given to conducting a real-world case study involving prototype-based validation of the proposed MPC approach in order to demonstrate its practical applicability under realistic operating conditions. Additionally, further research will focus on profiling and optimising the computational load of the control algorithm on embedded platforms, including microcontrollers and FPGA-based systems, to assess the feasibility for low-power or resource-constrained industrial applications.  
Therefore, the results of this study form the technical and technological basis for the further development and improvement of computer technologies for controlling electric drives.