Tools and Methods for the Verification and Validation of Adaptive Aircraft Control Systems

Johann Schumannt, Yan Liut, Nasa Ames, Moffett Field
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引用次数: 11

Abstract

The appeal of adaptive control to the aerospace domain should be attributed to the neural network models adopted in online adaptive systems for their ability to cope with the demands of a changing environment. However, continual changes induce uncertainty that limits the applicability of conventional validation techniques to assure the reliable performance of such systems. In this paper, we present several advanced methods proposed for verification and validation (V&V) of adaptive control systems, including Lyapunov analysis, statistical inference, and comparison to the well-known Kalman filters. We also discuss two monitoring tools for two types of neural networks employed in the NASA F-15 flight control system as adaptive learners: the confidence tool for the outputs of a Sigma-Pi network, and the validity index for the output of a Dynamic Cell Structure (DCS) network.
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自适应飞机控制系统验证和验证的工具和方法
自适应控制在航空航天领域的吸引力应归功于在线自适应系统中采用的神经网络模型,因为它们能够应对不断变化的环境的需求。然而,持续的变化导致不确定性,限制了传统验证技术的适用性,以确保这些系统的可靠性能。在本文中,我们提出了几种用于自适应控制系统的验证和验证(V&V)的先进方法,包括李雅普诺夫分析,统计推断以及与著名的卡尔曼滤波器的比较。我们还讨论了NASA F-15飞行控制系统中采用的两种类型的神经网络作为自适应学习器的两种监测工具:用于Sigma-Pi网络输出的置信工具,以及用于动态单元结构(DCS)网络输出的有效性指标。
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