## 5 Results and Discussion  
Figure 8 shows the simulation results of GA-NN with different neurons from 10 to 40 with step 10. As can be seen, the highest maximum fitness evaluation is approximately 0.94 with 30 neurons. Thus, the number of neurons is set to be 30 in the rest of this study. Figure 9 shows the simulation results of PGE-NN with the optimal number of neurons. It can be seen that PGE-NN also converts at 0.94. This indicates that the two methods produce similar results. Table 3 shows the execution time of GA-NN and PGE-NN. All simulations are executed by using MATLAB version 2016b. The computer has an Intel Core i7-7700 processor. It can be seen that the computation time of GA-NN takes approximately three times longer than that of PGE-NN to converge. Thus, the PGE-NN has an advantage in reducing the computation time. Numerical simulations using different control methods are performed for verifying the effectiveness of the controller. A passive damper and an adaptive hybrid controller are tested for comparison to the proposed intelligent controller. The adaptive hybrid controller is taken from the previous work [19]. Figure 8: Simulation results of GA-NN with different neurons. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Total Loops** & **Number Simulation** & **Execution Time per** & **Total Execution** \\  & **for Convergence** & **per Loop** & **Simulations (second)** & **Time (hour)** \\ \hline GA-NN & 10 & 300 & 13.6 & 11.3 \\ PGE-NN & 11 & 100 & 12.9 & 3.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the execution time between GA-NN and PGE-NN with 30 neurons. Figure 9: Simulation results of PGE-NN with 30 neurons. The adaptive hybrid controller is the combination of a skyhook controller and a force controller. In the skyhook controller, the skyhook gain is calculated separately in every single landing scenario. Additionally, the controller then autonomously adjusts the skyhook gain according to the estimated aircraft mass and sink speed by using a lookup table technique. In the force controller, the damping force is estimated based on the mathematical model and then maintained during the first compression stroke. Generally, the adaptive hybrid controller needs to determine the unknown parameters, which are aircraft mass, skyhook gain, and damping force, in different landing conditions. Because the system is assumed to be known exactly, the number of numerical simulations to determine the unknown parameters is less than 100. Thus, the execution time of the adaptive hybrid controller is dramatically smaller than the training time of the neural network. However, the most crucial factor in the efficiency of this method is the correctness of the landing gear model used in the controller. Table 4 and Figure 10 show the performance of the damper using different control methods for a heavy landing case. It can be seen that GA-NN and PGE-NN also maintain the damping force during the first compression stroke by their self-learning processes without requiring system knowledge. They produce slightly higher total energy absorber efficiencies than the adaptive hybrid controller. They also have smaller maximum stokes during landing, and they bring the system into an equilibrium state faster than the other controllers. However, they use a higher electric current than the adaptive hybrid controller. Thus, GA-NN and PGE-NN exhibit slightly greater acceleration in magnitude and also slightly higher damping force than that of the adaptive hybrid control. Generally, GA-NN and PGE-NN achieve marginally better performance than that of the adaptive hybrid controller. Figure 10: Comparative simulation results of the passive damper, adaptive hybrid controller, GA-NN, and PGE-NN control at \(m_{1}=680\) kg, \(v=3\) m/s. Table 5 shows the landing gear's performance using the passive damper and the adaptive hybrid control in fifteen landing cases, which are the twelve standard cases in Table 2 and three randomly generated ones.