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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:本文介绍了一种新的非线性估计程序,该程序用于估计和跟踪非线性飞机的参数。开发了无味卡尔曼滤波器(UKF),并将其与更传统的扩展卡尔曼滤波器(EKF)进行了比较。在F-15上,无论是复杂机动还是有故障机动,都需要估算状态和参数。该算法可以访问非线性动力学方程,但不能访问飞机发动机模型,空气动力学模型或大气模型。在EKF和UKF算法中估计描述这些未知动态的参数。结果显示UKF比EKF更准确,并且即使在稳定器发生50%故障后,也始终可以很好地跟踪所有参数。空气动力和力矩虽然由于不连续的非线性而难以在故障后立即追踪,但确实迅速恢复并保持在预测范围内。

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