## 6 Practicality Comparison
### Focus on Different Knowledge Points  
The results obtained from this study should be compared with the literature to showcase the contributions to knowledge. In conventional jitter control, a performance balancing method between worm gear self-locking and the yaw braking mechanism is often used to achieve limited jitter behavior [32].  
To achieve a greater reduction in the jitter, similar yaw system models as those used in the present study were investigated [33; 34; 35] by using the passive flutter suppression (PFS) method, with yaw vibration and low-speed buffeting discussed. The PFS technique was executed through methods, such as the structural stiffness adjustment design, friction torque matching, and driving-torque size adjustment, etc., and analyzed by using the finite element method. Furthermore, the LSJ vibration suppression in the PFS method was only based on the assumption that the yaw motion speed was always a constant value, which was clearly only a conditional qualitative linear analysis (CQLA) approach [33].  
Based on the CQLA approach, the structural parameters, first set of PD parameters, and iterative algorithm in this design were still used. Figure 10 shows the LSJ velocity fluctuations under constant yaw speeds of \(\omega=5,\ 10,\ 50\ \rm{mrad\cdot s}\).  
It can be seen that, under different constant yaw speeds, the LSJ vibration speed can quickly converge, reflecting the effectiveness of the proposed iterative algorithm in this design. Moreover, compared with reference [33], there is no \"negative\" speed and no low-speed-jitter phenomenon of \"back and forth\" motions in the LSJ process that appear in reference [33], reflecting the robustness of the proposed iterative algorithm. It should be noted that the buffeting analysis in the present study is based on a nonlinear analysis method of \"yaw-speed changing\", and its superior performance is obvious compared to linear buffeting analysis methods.  
As the PFS method is based on structural design and optimization, once the structure is finalized, the frequency of each order of the dynamic system is fixed. Moreover, based on the structural parameters and the first set of PD parameters in the present study, Table 3 shows the modal structure of each order of the entire dynamic system, including the control subsystem. Whether it is a tower mode or cabin mode, there is no subdivision here, but it is arranged in numerical order. The comparison is based on the PFS method, using the finite element method in reference [34; 35], and the additional motor drive design, using the iterative algorithm in the present study, by directly analyzing the eigenvalues in Formula (12).  
It can be seen that, compared with the PFS system based on the finite element analysis [35], the dynamic system with active control in this study generally reduces the frequency of each order modal, which means that the vibration becomes gentle and even tends to stabilize. The damping ratios generally increase, which is a manifestation of the effectiveness of the control methods in the dynamic control systems in this study. The increase in the damping ratio is also the direct reason for the decrease in various frequencies, which offsets the elastic force and slows down the system's vibration. This is also an effective method to achieve vibration suppression from the perspective of frequency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modal** & **Maximum** & **Standard Deviation** \\ \hline
1 & 19.4700/16.5495 & 0.2508/0.3010 \\
2 & 61.4020/52.1917 & 0.2387/0.2864 \\
3 & 93.6540/79.6059 & 0.2167/0.2600 \\
4 & 187.198/159.1183 & 0.2057/0.2468 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The system model structures (method in reference [35]/algorithm in the present study).  
Figure 10: The LSJ velocity fluctuations under constant yaw speeds of \(\omega=5,\ 10,\ 50\ \rm{mrad\cdot s}\).