## 6 Conclusions  
The weight matrices are assigned to subspace algorithm dimensionality reduction of the data by projecting the vector feature on a space of lower dimensions. Since the UPC algorithm use identity weight matrix in its structure, the computation time of UPC is the least among its other counterparts. The process of dimensionality reduction has reduced the time efficiency in PC and CVA but has an enhanced estimation results are achieved in PC. The obtained principal components are relatively robust against noise inclusion resulted more stable pole distribution. UPC has higher estimation accuracy; however, it is relatively weaker than PC in discrimination of poles in the first four orders. Compared to other algorithms CVA had the weakest performance in pole identification and time efficiency.  
**Author Contributions:** Resources, H.S. ([PERSON]), [PERSON] ([PERSON]), N.B., M.P. and S.S.R.K.; investigation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and H.S. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), H.S. ([PERSON]), N.B., M.P. and S.S.R.K.; visualization, H.S. ([PERSON]), H.S. ([PERSON]), N.B., M.P. and S.S.R.K.; supervision, N.B.; project administration, [PERSON] ([PERSON]), [PERSON] ([PERSON]), N.B. and S.S.R.K.; funding acquisition, N.B., M.P. and S.S.R.K.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Higher Education, Malaysia, and Universite Teknologi Malaysia (UTM) for their financial support through the Fundamental Research Grant Scheme (grant number: 4F800) and Higher Institution Centre of Excellent grant (grant number: 4J224) The APC was funded by Ministry of Education, Youth, and Sports of the Czech Republic and the European Union (European Structural and Investment Funds Operational Program Research, Development, and Education) in the framework of the project \"Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation\", Reg. No. CZ.02.1.01/0.0/0.0/16_025/0007293.  
**Acknowledgments:** The authors would like to thank the Ministry of Higher Education, Malaysia, and Universite Teknologi Malaysia (UTM) for their financial support through the Fundamental Research Grant Scheme (4F800) and Higher Institution Centre of Excellent grant (4J224), Ministry of Education, Youth, and Sports of the Czech Republic and the European Union (European Structural and Investment Funds Operational Program Research, Development, and Education) in the framework of the project \"Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation\", Reg. No. CZ.02.1.01/0.0/0.0/16_025/0007293.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case study** & **Indicator** & **PC** & **UPC** & **CVA** \\ \hline \multirow{4}{*}{Numerical model} & VAF & + \({}^{*}\) & +++ & ++ \\  & Pole analysis & +++ & ++ & + \\  & Computation time & ++ & +++ & + \\ \hline Experiential & Pole analysis & +++ & ++ & + \\ \hline \hline \end{tabular} \({}^{*}\)_+_, \({}^{*}\)_+_, \({}^{*+}\)_+_, and “+++_” notion means the “weak”, “medium” and “strong” performance in the associated weight matrices, respectively.  
\end{table}
Table 2: The results of the comparative study for PC, CVA, and UPC algorithms.