Design and Analysis of MR Damper for Airplane Landing Gear

2018 ◽  
Vol 28 (1) ◽  
pp. 102-109 ◽  
Author(s):  
Bo-Gyu Kim ◽  
Chulhee Han ◽  
Seung-Bok Choi
Keyword(s):  
Mr Damper ◽  
Landing Gear

A Semi-Active Controller for an Aircraft Landing Gear Equipped with Magnetorheological Damper

2019 ◽  
Vol 894 ◽  
pp. 29-33
Author(s):  
Luong Quoc Viet ◽  
Jai Hyuk Hwang
Keyword(s):  
Numerical Simulation ◽  
Force Control ◽  
Hybrid Control ◽  
Mr Damper ◽  
Landing Gear ◽  
Operating Conditions ◽  
Magnetorheological Damper ◽  
Numerical Simulation Result ◽  
Aircraft Landing ◽  
Aircraft Landing Gear

The magnetorheological (MR) damper is the newest approach to replace the traditional passive damper which cannot change their dynamics in response to different operating conditions of the aircraft landing gear. This paper presents the simulation study of a semi-active controller for a landing gear equipped MR damper. Furthermore, a new method combined skyhook control with force control, called hybrid control, is developed to improve the performance of the MR damper landing gear. Finally, the numerical simulation result of the landing gear using SIMSCAPE-Simulink is discussed.

Semi-Active Landing Gear with MR Fluid Damper Based on ADAMS&MATLAB Co-Simulation

2011 ◽  
Vol 228-229 ◽  
pp. 363-367
Author(s):  
S. S. Pawale ◽  
Hai Jun Wang
Keyword(s):  
Fuzzy Control ◽  
Control Strategy ◽  
Ride Comfort ◽  
Mr Damper ◽  
Simulation Method ◽  
Landing Gear ◽  
Design Development ◽  
Mr Fluid ◽  
Matlab Simulink ◽  
Fuzzy Control Strategy

According to MR fluid properties and MR damper structure, a multi-body dynamic model of semi-active landing gear was built by using virtual prototype in ADMAS/Aircraft. Fuzzy control system for the semi-active landing gear with MR damper was designed under the MATLAB/Simulink environment. Co-Simulation of ADAMS/Aircraft and MATLAB/Simulink were carried out. The simulation results show that semi-active landing gear with MR damper based on fuzzy control strategy can reduce the aircraft’s vibration and improve the ride comfort characteristics. Co-simulation method provides one simple feasible method for the process of landing gear design, development and manufacture. It also offers a new way to advance designing quality for complex dynamic system and control strategy simulation.

scholarly journals A Supervised Neural Network Control for Magnetorheological Damper in an Aircraft Landing Gear

2021 ◽  
Vol 12 (1) ◽  
pp. 400
Author(s):  
Quoc-Viet Luong ◽  
Bang-Hyun Jo ◽  
Jai-Hyuk Hwang ◽  
Dae-Sung Jang
Keyword(s):  
Neural Network ◽  
Mr Damper ◽  
Landing Gear ◽  
Network Control ◽  
Neural Network Control ◽  
Hybrid Controller ◽  
Aircraft Landing ◽  
Aircraft Landing Gear ◽  
Drop Tests ◽  
The Impact

This paper adopts an intelligent controller based on supervised neural network control for a magnetorheological (MR) damper in an aircraft landing gear. An MR damper is a device capable of adjusting the damping force by changing the magnetic field generated in electric coils. Applying an MR damper to the landing gears of an aircraft could minimize the impact at landing and increase the impact absorption efficiency. Various techniques proposed for controlling the MR damper in aircraft landing gears require information on the damper force or the mass of the aircraft to determine optimal parameters and control commands. This information is obtained by estimation with a model in a practical operating environment, and the accompanying inaccuracies cause performance degradation. Machine learning-based controllers have also been proposed to address model dependency but require a large number of drop test data. Unlike simulations, which can conduct a large number of virtual drop tests, the cost and time are limited in the actual experimental environment. Therefore, a neural network controller with supervised learning is proposed in this paper to simulate the behavior of a proven controller only with system states. The experimental data generated by applying the hybrid controller with the exact mass and force information, which has demonstrated high performance among the existing techniques, are set as the target for supervised learning. To verify the effectiveness of the proposed controller, drop test experiments using the intelligent controller and the hybrid controller with and without exact information about aircraft mass and force are executed. The experimental results from the drop tests of a landing gear show that the proposed controller maintains superior performance to the hybrid controller without using explicit damper models or any information on the aircraft mass or strut force.

scholarly journals Semi-Active Control for a Helicopter with Multiple Landing Gears Equipped with Magnetorheological Dampers

2021 ◽  
Vol 11 (8) ◽  
pp. 3667
Author(s):  
Quoc Viet Luong ◽  
Dae-Sung Jang ◽  
Jai-Hyuk Hwang
Keyword(s):  
Mr Damper ◽  
Electrical Current ◽  
Center Of Gravity ◽  
Landing Gear ◽  
Gear System ◽  
Damping Force ◽  
Promising Candidate ◽  
Mr Dampers ◽  
Helicopter Landing ◽  
Landing Gears

Due to their extensive use in various applications, helicopters need to be able to land in a variety of conditions. Typically, a helicopter landing gear system with skids or passive wheel-dampers is designed based on only one critical touchdown condition. Thus, this helicopter landing gear system may not perform well in different landing conditions. A landing gear system with magnetorheological (MR) dampers would be a promising candidate to solve this problem. However, a semi-active controller must be designed to determine the electrical current applied to the MR damper to directly manage the damping force. This paper presents a new skyhook controller, called the skyhook extended controller, for a helicopter with multiple landing gears equipped with MR dampers to reduce the helicopter’s acceleration at the center of gravity in off-normal landing attitude conditions. A 9-DOF simulation model of a helicopter with multiple MR landing gears was built using RECURDYN. To verify the effectiveness of the proposed controller, co-simulations were executed with RECURDYN and MATLAB in different initial pitch and roll angles at touchdown. The main simulation results show that the proposed controller can greatly decrease the peak and rms acceleration of the helicopter’s center of gravity compared to a traditional skyhook controller and passive damper.

scholarly journals Intelligent Control Based on a Neural Network for Aircraft Landing Gear with a Magnetorheological Damper in Different Landing Scenarios

2020 ◽  
Vol 10 (17) ◽  
pp. 5962 ◽  
Author(s):  
Quoc Viet Luong ◽  
Dae-Sung Jang ◽  
Jai-Hyuk Hwang
Keyword(s):  
Neural Network ◽  
Mr Damper ◽  
Landing Gear ◽  
Magnetorheological Damper ◽  
Damping Force ◽  
Hybrid Controller ◽  
Aircraft Landing ◽  
Neural Network Controller ◽  
Passive Damper ◽  
Aircraft Landing Gear

A typical oleo-pneumatic shock-absorbing strut (classic traditional passive damper) in aircraft landing gear has a metering pin extending through the orifice, which can vary the orifice area with the compression and extension of the damper strut. Because the metering pin is designed in a single landing condition, the traditional passive damper cannot adjust its damping force in multiple landing conditions. Magnetorheological (MR) dampers have been receiving significant attention as an alternative to traditional passive dampers. An MR damper, which is a typical semi-active suspension system, can control the damping force created by MR fluid under the magnetic field. Thus, it can be controlled by electric current. This paper adopts a neural network controller trained by two different methods, which are genetic algorithm and policy gradient estimation, for aircraft landing gear with an MR damper that considers different landing scenarios. The controller learns from a large number of trials, and accordingly, the main advantage is that it runs autonomously without requiring system knowledge. Moreover, comparative numerical simulations are executed with a passive damper and adaptive hybrid controller under various aircraft masses and sink speeds for verifying the effectiveness of the proposed controller. The main simulation results show that the proposed controller exhibits comparable performance to the adaptive hybrid controller without any needs for the online estimation of landing conditions.

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