## 3 Results
### Results of Applying the Taxonomic Approach and Simulation Experiment  
To determine the optimal combination of values for the algorithmic parameters \(N_{horizon}\), \(N_{MaxGradlter}\), \(T_{horizon}\), and _LineSearchMax_, as well as the weight coefficients \(P_{Cost}\) of the cost function, it is necessary to thoroughly analyse the empirical data obtained. Given the large dimension of the option space and the presence of multiple evaluation criteria, such a task is difficult to perform using visual methods alone. According to the proposed methodology given in Section 2, the taxonomic method involves normalising indicators using Formulas (2)-(5), forming a benchmark vector (6), and calculating the distances between the objects of observation and the benchmark, taking into account the weighting coefficients of the criteria. As a result, for each set of parameters, the integral distance \(R_{j}\) was calculated using Formula (7), which serves as a measure of the distance from the conditionally best option. The results are shown in Figure 7.  
The value of \(R_{j}\) as a function of the _LineSearchMax_ parameter is shown in Figure 7a. There is a sharp drop in \(R_{j}\) when the parameter increases from 0.01 to \(\approx\) 40, after which the values stabilise at a minimum level. This indicates the need to use a sufficiently large maximum line search step to ensure effective convergence of the algorithm at low values of \(N_{MaxGandIter}\). The distribution of \(R_{j}\) values for different combinations of \(N_{horizon}\) and \(N_{MaxGandIter}\) parameters is shown in Figure 7b. There is a clear minimisation of the distance \(R_{j}\) at medium or moderately large values of both parameters: in particular, \(N_{horizon}\approx\) 35-50; \(N_{MaxGandIter}\geq 2\). This corresponds to the zone in which a good compromise between accuracy, energy consumption, and current control quality is achieved. At the periphery of the diagram, where the parameters display extreme values (too low or too high), \(R_{j}\) increases, indicating a deterioration in overall performance. The dependence of \(R_{j}\) on the values of the weight coefficients \(P_{Cost}\), i.e., the ratio between loss and speed deviation, is shown in Figure 7c. The lowest values of \(R_{j}\) are observed for sets where \(\text{Pcost(loss)}\approx\) 2 and \(\text{Pcost(speed)}\approx\) 2-3, which demonstrates an optimal balance between energy saving and dynamic quality. At the same time, excessive growth of any of the components (for example, a speed weight up to 100 or more) causes a deterioration in the overall \(R_{j}\) index, i.e., overloading the cost function with one goal leads to degradation in other criteria. Figure 7d shows \(R_{j}\) when \(T_{horizon}\) changes. The curve has a clear minimum at \(T_{horizon}\approx 0.14\) s, which is approximately in 3.18 times greater than the rotor time constant. This confirms the earlier conclusion about the existence of an optimal prediction zone. Values less than 0.1 s or greater than 0.2 s led to an increase in \(R_{j}\), indicating insufficient or excessive consideration of the future dynamics of the system. The application of the taxonomic method made it possible to form an objective rating assessment of the parameters of predictive control (see Table 3). This approach ensured comprehensive multi-factor optimisation, taking into account conflicting criteria such as speed accuracy, energy consumption, calculation time, and current trajectory quality. The method can be extended to other model variants or control configurations and is a promising tool for automated system configuration in multi-criteria optimisation mode. Figure 8 shows the results of the developed model (see Figures 1 and 2), where a comparison of the benchmark (calculation using fmincon [34]) with the indicators from Table 3 based on GRAMPC, before and after optimisation of the algorithmic parameters, is presented.