## 1 Introduction  
Magnetroheological (MR) dampers have been studied in a wide range of industrial fields to effectively control and reduce unwanted vibrations [1]. However, the application of an MR damper as a replacement for a traditional oleo-strut (i.e., oleo-pneumatic damper) is very uncommon in aircraft landing gears [2]. The traditional oleo-strut, which has an orifice area that varies according to the movement of the metering pin, limits the efficiency of the damper, especially for conditions other than where it is designed, because only a slight change in the viscous damping force can be made. A landing gear equipped with an MR damper, which is the most plausible replacement for the oleo-strut, has such advantages as quick response times, reduced weight, and lower costs [3; 4]. To make use of it effectively, a controller must be designed to determine the optimal electric current to be applied to the MR damper [5]. However, an MR damper is a complex system that usually exhibits nonlinear characteristics [6]. Thus, the development of a controller for an MR damper is a challenging problem. Thus far, many control algorithms have been investigated through experiments or theoretical studies. [PERSON] et al. [7] designed and applied the sky-ground hook controller for a landing gear with an MR damper to maximize the landing efficiency of the drop simulation. [PERSON] and [PERSON] [8] developed a controller based on human simulated intelligent control to improve the landing gear's performance in different landing scenarios. [PERSON] [9] applied a robust semi-active control for aircraft landing gear systems with MR dampers based on the linear quadratic regulator (LQR) and H\({}_{\infty}\). [PERSON] et al. [10] examined a buffer control algorithm based on model predictive control. [PERSON] et al. [11] developed a design analysis and control of adaptive MR landing gear dampers to enable adaptive shock mitigation in a lightweight helicopter. [PERSON] et al. [12] designed a new control logic to improve the performance of an aircraft landing gear with an MR damper at touchdown. In all previous studies, understanding the behavior of the MR damper has been very important because this behavior has a significant impact on the effectiveness of the controller. Machine learning, a hot topic nowadays, has been developed for many years and used in various industrial applications [13]. Machine learning has proved its ability to control a system without the knowledge of that system (e.g., self-learning cars [14] and robotics [15; 16]). However, applications for landing gear are relatively rare. [PERSON] et al. [17] applied a back-propagation neural network to adjust three parameters of the PID controller for landing gear. [PERSON] et al. [18] used machine learning algorithms to predict the landing gear loads during touchdown based on drop test experimental data. However, as of yet, there are no studies that have directly applied machine learning to control landing gear with MR dampers. In our previous work [19; 20], a classical controller based on adaptive sliding mode control was applied to improve the landing gear robustness and adaptiveness under parametric uncertainties. The controller estimates landing conditions in real time and then adjusts its parameters according to the estimated landing conditions. Moreover, controller robustness is enhanced by adding a sliding mode control algorithm to manage small variable disturbances. The most crucial factor in the efficiency of this method is the correctness of the landing gear model used in the controller. The controller may not be useful if the model is unable to closely follow the real dynamics of an MR damper. To overcome this pitfall, the present paper adopts an intelligent controller based on a neural network trained using two methods, which are a genetic algorithm, called GA-NN, and policy gradient estimation, called PGE-NN, for aircraft landing gear with an MR damper in differing landing scenarios. The controller is developed through a number of trials.