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Nonlinear estimation of aircraft models for on-line control customization

机译:用于在线控制定制的飞机模型的非线性估计

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This paper describes a new nonlinear estimation procedure used to estimate and track the parameters of a nonlinear aircraft. The Unscented Kalman Filter (UKF) is developed and compared to the more traditional Extended Kalman Filter (EKF). State and parameters are estimated on the F-15 for both a complex maneuver and a maneuver with failure. The algorithms have access to the nonlinear dynamic equations, but not the aircraft engine models, aerodynamic models, or atmospheric models. Parameters describing these unknown dynamics are estimated in the EKF and UKF algorithms. Results show the UKF to be more accurate than the EKF, and track all parameters very well at all times, even after a 50% failure of the stabilator. The aerodynamic forces and moments, while difficult to track immediately after the failure because of the discontinuous nonlinearity, did recover quickly and stay within the predicted bounds.
机译:本文介绍了一种用于估计和跟踪非线性飞机参数的新的非线性估计过程。未设计的卡尔曼滤波器(UKF)是开发的,并与更传统的扩展卡尔曼滤波器(EKF)进行比较。对于具有故障的复杂机动和机动,估计状态和参数估计在F-15上。该算法可以访问非线性动态方程,但不是飞机发动机模型,空气动力学模型或大气模型。描述这些未知动态的参数估计在EKF和UKF算法中。结果显示UKF比EKF更准确,并且始终始终遵循所有参数,即使在稳定器的50%失败后也是如此。空气动力力和时刻,虽然在失败后难以跟踪由于不连续的非线性,但确实快速恢复并保持在预测范围内。

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