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One of the major subsystems of each airplane is landing gear system which must be capable of tolerating extreme forces applied to the airplane during landing. Using conservative techniques to find landing loads result in overestimation and unnecessary extra structural weight. New commercial softwares can simulate real landing conditions with acceptable accuracy if detailed mechanical data about landing gear system subparts are provided. Although these softwares work well but due to lack of detailed information about the subparts at the conceptual design phase, complexity and time consuming of modeling, expensive license price, etc. they do not seem to be the best choice for design purpose. In this study, in order to calculate landing loads more precisely than the estimating conservative methods, flight dynamic differential equations of an airplane during landing phase are derived and through numeric and state space techniques are solved for different initial conditions including, three point landing, two point landing and one wheel landing. Each landing gear of the airplane is modeled as a two-degree of freedom mass-spring-damper set. Time history of the airplane center of gravity, pitch and roll angle, vertical landing loads of each landing gear and their spin-up loads for different landing types (different initial conditions) are obtained to show capabilities of this new, fast and accurate landing simulation code, generated.

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In this paper, an optimal active controller is designed to prevent the shimmy vibrations in aircraft nose landing gear. The controller is designed according to the linearized system while the input is implemented on the real non-linear plant. Shimmy vibration is the lateral and torsional vibrations in the wheel that causes instability in high speed performances. Thus, control and suppressing of this vibration is extremely important. In this paper, using the nonlinear dynamics of the nose landing gear system, the equivalent linearized system is extracted and then its related linearized state space is derived. Stability, controllability and observability of the system are investigated based on the linearized model of the system and damping the shimmy vibrations is performed with the least consumption of energy using Linear Quadratic Regulator (LQR). To estimate the states of the system which are not measurable using ordinary sensors, an observer is designed and implemented using separation principal. To verify the performance of the proposed controller, vibration response of the open loop system is compared with the closed loop response of the designed optimal controller. Considerable improvement can be seen in the performance of the closed loop system since not only the vibrations are effectively damped but also the consumption of energy is minimized. Finally, digital control system is extended in order to implement the proposed controller on the discretized model of the system and the effect of sampling rate on the accuracy of the system is studied.

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In this paper, in order to reduce a landing gear vibration two adaptive control systems are designed considering the landing and taxi phases. For this purpose, 6 degree of freedom equations of motion of the landing gear and the related transfer functions are extracted. A reduced order model of the overall transfer functions are given as a result of complicated dynamic model. A Lyapunov based model reference adaptive control is designed to absorb the vibration of front wheel of the landing gear at touchdown. In addition, a minimum variance adaptive controller is designed and implemented on the system to reject the band level disturbances during the taxi phase. The band disturbances are modeled as a colored Gaussian noise and the system parameters as well as noise characteristics are estimated using extended least square approach. Both control systems are investigated to assess the best performance. Numerical simulations of the system in Matlab/Simulink environment show the preferences and satisfactory performances of the proposed vibration control systems. These results are calculated against various inputs including model reference adaptive control and minimum variance approaches