## 2 Materials and Methods
This approach is based on building a matrix of initial indicators for each test object, normalising the data, forming \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(P_{N}\) & 370 W & \(T_{N}\) & 2.59 Nm \\ \(Z_{p}\) & 2 & \(J_{M}\) & 22 \(\times\) 10\({}^{-4}\) kg\(\cdot\)m\({}^{2}\) \\ \(n_{N}\) & 1370 rpm & & \\ \hline \(R_{1}\) & 27.8 \(\Omega\) & \(L_{\sigma}\) & 0.142 H \\ \hline \(R_{2}\) & 20 \(\Omega\) & \(L_{\mu}\) & 0.88 H \\ \hline \(P_{1}\) & \(-\)0.669 & \(P_{4}\) & 4.415 \\ \(P_{2}\) & 3.606 & \(P_{5}\) & \(-\)0.743 \\ \(P_{3}\) & \(-\)6.622 & \(P_{6}\) & 0.754 \\ \hline \(C_{1}\) & 0.0013 & \(C_{2}\) & 0.5778 \\ \hline \(\omega_{2,1}\) & 52.36 rad/s \(\cong\) 500 rpm & \(\omega_{2,2}\) & 157 rad/s \(\cong\) 1500 rpm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Typical technical and functional characteristics of an electric motor with the rated power of 370 W.  
a reference vector, calculating the distance to the reference, and ranking the results based on the deviation from the optimal value using the least squares method [33]. The algorithmic implementation included the following steps:  
1. Formation of the input matrix of indicators for ranking \(N\) objects by \(M\) indicators (accuracy of system operation in accordance with a given speed trajectory--\(N_{s}\) (_Accuracy, in \(\%\)_), calculation time for parameters--\(T_{s}\) (_Calculation time, in seconds_), energy consumption--\(E_{L}\), accuracy of system operation in accordance with the magnetisation current trajectory--\(R^{2}\)), as shown below in the form of a generalised matrix (1). In this case, the indicators were compared with the reference values obtained using the _fmincon_ algorithm [34]. \[X= \begin{bmatrix}x_{11}&\dots&x_{1j}&\dots&x_{1N}\\ \dots&\dots&\dots&\dots&\dots\\ x_{i1}&\dots&x_{ij}&\dots&x_{iN}\\ \dots&\dots&\dots&\dots&\dots\\ x_{M1}&\dots&x_{Mj}&\dots&x_{MN}\end{bmatrix} ,\quad\text{where }i=1\dots M\text{ and }j=1\dots N, \tag{1}\]  
where \(X\) is the matrix of indicators for each object under study; \(i\) is the current indicator number; \(j\) is the current object number; \(M\) is the number of indicators; \(N\) is the number of objects. 2. Normalisation of indicators, which is a mandatory stage due to the fact that the indicators reflect different, incomparable values, which allows for the elimination of the influence of absolute values, as shown below using Formulas (2)-(5):  
\[X_{norm}= \begin{bmatrix}x_{11_{norm}}&\dots&x_{1j_{norm}}&\dots&x_{1N_{norm}} \\ \dots&\dots&\dots&\dots&\dots\\ x_{11_{norm}}&\dots&x_{ij_{norm}}&\dots&x_{iN_{norm}}\\ \dots&\dots&\dots&\dots&\dots\\ x_{M1_{norm}}&\dots&x_{Mj_{norm}}&\dots&x_{MN_{norm}}\end{bmatrix} , \tag{2}\]  
\[x_{ij_{norm}}=\frac{x_{ij}-\overline{x_{i}}}{\sigma_{i}}, \tag{3}\]  
\[\overline{x_{i}}=\frac{1}{N}{\sum_{j=1}^{N}x_{ij}}, \tag{4}\]  
\[\sigma_{i}=\sqrt{\frac{1}{N}{\sum_{j=1}^{N}\bigl{(}x_{ij}-\overline{x_{i}} \bigr{)}^{2}}}, \tag{5}\]  
where \(X_{norm}\) is the matrix of normalised indicators for each object under study; \(\overline{x_{i}}\) is the average value of the \(i\)-th indicator for the corresponding number of objects; \(\sigma_{i}\) is the standard deviation of the \(i\)-th indicator for the corresponding number of objects; \(i\) is the current number of the indicator; \(j\) is the current number of the object; \(M\) is the number of indicators; \(N\) is the number of objects. 3. Generation of a vector of benchmark values based on the optimal values of the indicators for each row of the \(X_{norm}\) matrix. Based on this, the vector (6) will be shaped, as shown in a generalised form below:  
\[X_{norm}^{e}=\left[ \begin{array}{c}x_{n_{norm}}^{e}\\ x_{2n_{norm}}^{e}\\ \dots\\ x_{M_{norm}}^{e}\end{array} \right] \tag{6}\]where \(X^{e}_{norm}\) is a vector of normalised benchmark values for each studied indicator. 4.