## 6 Conclusions  
This research has investigated intelligent techniques based on a neural network controller for aircraft landing gear equipped with a magnetorheological damper in differing landing scenarios. In this research, two algorithms, which are the genetic algorithm and policy gradient estimation, were applied to determine the optimal parameters in the neural network controller. The controller learned from a large number of trials. Thus, the main advantage of this method is that it runs autonomously without requiring system knowledge. Both the genetic algorithm and policy gradient estimation have produced  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**GA-NN**} & \multicolumn{3}{c}{**PGE-NN**} \\ \hline  & \(\mathbf{F_{\max}}\) (kN) & \(\mathbf{s_{\max}}\) (m) & \(\mathbf{\eta}\) (\%) & \(\mathbf{F_{\max}}\) (kN) & \(\mathbf{s_{\max}}\) (m) & \(\mathbf{\eta}\) (\%) \\ \hline \multicolumn{6}{l}{\(m_{1}=560\) kg} \\ \hline \(v=2\) m/s & 14.8 & 0.144 & 94.4 & 14.7 & 0.144 & 94.7 \\ \(v=2.5\) m/s & 17.7 & 0.157 & 95.6 & 17.6 & 0.157 & 95.5 \\ \(v=3\) m/s & 20.6 & 0.177 & 94.0 & 20.6 & 0.177 & 94.8 \\ \hline \multicolumn{6}{l}{\(m_{1}=600\) kg} \\ \hline \(v=2\) m/s & 15.5 & 0.150 & 94.4 & 15.3 & 0.150 & 95.4 \\ \(v=2.5\) m/s & 18.1 & 0.164 & 96.8 & 18.1 & 0.164 & 97.3 \\ \(v=3\) m/s & 21.1 & 0.184 & 95.5 & 21.0 & 0.183 & 96.9 \\ \hline \multicolumn{6}{l}{\(m_{1}=640\) kg} \\ \hline \(v=2\) m/s & 16.2 & 0.155 & 94.2 & 16.1 & 0.155 & 94.8 \\ \(v=2.5\) m/s & 18.9 & 0.171 & 95.8 & 18.8 & 0.169 & 97.3 \\ \(v=3\) m/s & 21.7 & 0.191 & 96.1 & 21.7 & 0.189 & 97.6 \\ \hline \multicolumn{6}{l}{\(m_{1}=680\) kg} \\ \hline \(v=2\) m/s & 16.9 & 0.160 & 94.6 & 16.8 & 0.160 & 95.0 \\ \(v=2.5\) m/s & 19.7 & 0.178 & 94.9 & 19.6 & 0.175 & 95.0 \\ \(v=3\) m/s & 22.9 & 0.197 & 94.6 & 22.8 & 0.194 & 94.9 \\ \hline \multicolumn{6}{l}{\(m_{1}=575\) kg (Random Case 1)} \\ \hline \(v=2.25\) m/s & 16.4 & 0.149 & 97.8 & 16.1 & 0.152 & 97.2 \\ \hline \multicolumn{6}{l}{\(m_{2}=625\) kg (Random Case 2)} \\ \hline \(v=2.75\) m/s & 20.0 & 0.179 & 96.1 & 19.6 & 0.182 & 96.3 \\ \hline \multicolumn{6}{l}{\(m_{3}=654\) kg (Random Case 3)} \\ \hline \(v=2.35\) m/s & 18.4 & 0.167 & 95.8 & 18.2 & 0.182 & 95.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Damper performance of the damper using GA-NN and PGE-NN in differing landing cases.  
similar results. Compared to the one of the best existing controllers, i.e., the adaptive hybrid controller, both the genetic algorithm-neural network and policy gradient estimation-neural network controllers delivered similar levels of performance to that of the adaptive hybrid control in different landing scenarios without requiring the landing condition estimation or the system knowledge. In future studies, the effectiveness of the proposed controller will be verified with experimental data from landing gear drop tests.  
Data curation, Q.V.L. and D.-S.J.; Formal analysis, Q.V.L. and D.-S.J.; Investigation, D.-S.J.; Methodology, D.-S.J. and J.-H.H.; Project administration, J.-H.H.; Software, Q.V.L.; Supervision, [PERSON]-S.J. and [PERSON]-H.H., Writing--original draft, Q.V.L.; Writing--review & editing, D.-S.J. and [PERSON]-H.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), grant number 10073291.  
This work was supported by the Technology Innovation Program (intelligent landing gear with variable damping force for 1500 lb class) (10073291).  
The authors declare no conflict of interest.