基于Luenberger状态估计和稳定性增强系统的高超声速运输机传感器和过程噪声降噪

Z. Zaludin
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引用次数: 0

摘要

本文首先提出了一种基于Luenberger估计的稳定增强系统的高超声速运输机(HST)自动驾驶策略。SAS采用线性二次调节器(LQR)理论设计,对于HST,只要满足三个理论要求,就可以保证其鲁棒动态稳定性。Luenberger估计器被整合到自动驾驶仪设计中,用于SAS估计飞机的状态变量。在动态响应仿真中,将传感器噪声和过程噪声引入到数学模型中。然而,到目前为止,在飞机飞行的速度和高度下,传感器和过程噪声的知识是有限的。仿真结果表明,Luenberger估计器能较好地滤除噪声。这对于HST来说是一个优势,因为在设计Luenberger估计器时不需要先验的噪声知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor and Process Noises Reduction using a Luenberger State Estimator with a Stability Augmentation System for a Hypersonic Transport Aircraft
This paper firstly, presents an autopilot strategy for a Hypersonic Transport Aircraft (HST) using a Stability Augmentation System (SAS) with a Luenberger estimator. The SAS is designed using Linear Quadratic Regulator (LQR) theory which, for HST, benefits the guaranteed robust dynamic stability provided three theoretical requirements are met. The Luenberger estimator is incorporated into the autopilot design to estimate the state variables of the aircraft for the SAS. In the dynamic response simulation, sensor and process noises are inserted into the mathematical model. However, to date, knowledge of the sensor and process noises at the speeds and heights where the aircraft will be flying is limited. The simulation shows that the Luenberger estimator significantly filters the noise. This is an advantage for the HST as prior knowledge of the noises is not necessary when designing the Luenberger estimator.
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