## 5 Conclusions  
Based on the results of our research conducted to develop and validate a computerised method for the multiparameter optimisation of algorithms for predictive control of asynchronous electric drives, the following results have been obtained:  
1. The effectiveness of applying a multiparameter optimisation method for the parameters of predictive control algorithms, based on a combination of computer modelling and taxonomic analysis methods, has been proposed and proven. This has made it possible to comprehensively take into account the criteria of accuracy of system operation in accordance with a setpoint trajectory, energy consumption, and computational costs.  
2. A computer model of a predictive control system for an asynchronous electric drive has been developed and validated in MATLAB and Simulink based on the GRAMPC strategy, which provides an adequate model of the dynamic behaviour of an asynchronous electric drive under a wide range of operating modes.  
3. During a series of computer experiments, the optimal parameters of the predictive control algorithm have been established: \(N_{horizon}=50\), \(N_{MaxCradIter}=2\), \(T_{horizon}=0.14\) s, and \(LineSearchMax=40\), which ensures stable operation of the algorithm when controlling an asynchronous motor with a power of 370 W in dynamic modes.  
4. It has been demonstrated that the application of a taxonomic approach to effectiveness analysis allows for the automated determination of optimal combinations of control algorithm parameters for different operating modes, which increases the adaptability and practical applicability of the proposed method of predictive control in industrial electric drives.  
5. The results obtained form a methodological and applied basis for the further development of hybrid intellectualised predictive algorithms with increased adaptability to changing operating conditions and integration into real industrial automated control systems.  
6. The study's novelty is highlighted by the integration of a real-time-capable MPC tool (GRAMPC) with a taxonomic MCDM-based optimisation process, providing a structured methodology for selecting algorithm parameters. This approach enables a more precise, energy-efficient, and computationally viable control of asynchronous drives under dynamic conditions, distinguishing the proposed solution from existing approaches.  
Conceptualization, S.S. and I.L.; methodology, G.D. and I.L.; software, G.D.; validation, S.S., G.D. and I.L.; formal analysis, K.M.; investigation, S.S., K.M., G.D. and I.L.; data curation, G.D. and I.L.; writing--original draft preparation, G.D.; writing--review and editing, G.S. and G.D.; visualization, K.M. and G.D.; supervision, S.S., G.S. and G.D. All authors have read and agreed to the published version of the manuscript.  
This research was carried out as part of the joint Ukrainian-German R&D project 'Model Predictive Dynamic Energy Efficient Control in Rotating Field Machines', funded by the Ministry of Education and Science of Ukraine (state registration number 0124U003745) and the German Federal Ministry of Education and Research under grant number 01 DK24020.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.