Direct prediction-error identification of unstable nonlinear systems applied to flight test data
Direct prediction-error identification of unstable nonlinear systems applied to flight test data
复制标题
直接预测-不稳定非线性系统误差识别应用于飞行试验数据
DOI：
10.3182/20090706-3-fr-2004.00024
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
L. Ljung
中科院分区：
文献类型：
--
作者：
R. Larsson;Zoran Sjanic;M. Enqvist;L. Ljung
先进、高度敏捷的战斗机具有不稳定的非线性气动特性，其控制系统的设计在很大程度上依赖于飞行力学仿真。这使得模拟器中空气动力学模型的准确性变得非常重要。本文给出了控制系统未知的非线性不稳定系统参数估计的两种方法。这两种方法都是直接预测误差方法，要么使用直接参数化的观测器，要么使用扩展卡尔曼滤波作为预测器。这些方法已经在模拟数据和实际飞行试验数据上进行了验证，所有方法都显示了良好的结果。
Control system design for advanced, highly agile fighter aircraft, with unstable nonlinear aerodynamic characteristics, rely heavily on flight mechanical simulations. This makes the accuracy of the aerodynamic model in the simulators very important. Here, two methods for estimating parameters of nonlinear unstable systems where the control system is unknown are presented. Both approaches are direct prediction-error methods, either with a directly parametrized observer or with an Extended Kalman Filter as a predictor. These methods have been validated on simulated data, as well as on real flight test data and all approaches show promising results.