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Nonlinear filtering and system identification algorithms for autonomous systems.

机译：自治系统的非线性滤波和系统识别算法。

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Autonomy is a key technology for future high-performance and remote operation applications. In essence, autonomy will extend automatic control to broader operating conditions, through more highly uncertain environments, and to a greater tolerance for system faults. An important component for achieving autonomy is accurate and timely information regarding the condition of the system. This information could be used at various levels of autonomy. For example, updated models of the system dynamics could be used to adapt low-level system control. This information could also be used at higher levels to decide whether a system is capable of completing a task. The work presented here involves the investigation of system identification and nonlinear filtering algorithms that are compatible with the general goals of autonomous control. Application to both flexible structures and high performance aircraft is considered.; Combining existing identification algorithms in a two-step approach for application to flexible structures is considered. A batch subspace identification algorithm is used in combination with a recursive filtering algorithm to provide a method of generating and updating a linear model for a flexible structure. This method is demonstrated on data collected from a two-link flexible arm.; Aircraft state and parameter estimation is also considered. A previously introduced nonlinear filtering algorithm called the Unscented Kalman Filter (UKF) is developed and investigated for two different approaches to the problem. First, the UKF is used to estimate the nonlinear state along with the aerodynamic force and moment environment. Second, the UKF is developed for the more traditional problem of identifying the stability and control derivatives of an aircraft's aerodynamic model.
机译：自治是未来高性能和远程操作应用程序的一项关键技术。本质上，自主性将通过更多高度不确定的环境将自动控制扩展到更广泛的运行条件，并提高对系统故障的容忍度。实现自治的重要组成部分是有关系统状况的准确，及时的信息。该信息可以在各种自治级别上使用。例如，可以使用系统动力学的更新模型来适应低级系统控制。此信息也可以用于更高级别，以决定系统是否能够完成任务。这里介绍的工作涉及系统识别和与自主控制一般目标兼容的非线性滤波算法的研究。考虑同时应用于柔性结构和高性能飞机。考虑将两步方法中的现有识别算法相结合，以应用于柔性结构。批处理子空间识别算法与递归过滤算法结合使用，以提供一种生成和更新用于柔性结构的线性模型的方法。在从两个链接的柔性臂收集的数据上演示了此方法。还考虑了飞机状态和参数估计。已开发出一种称为Unscented Kalman滤波器（UKF）的非线性滤波算法，并针对该问题的两种不同方法进行了研究。首先，使用UKF来估计非线性状态以及空气动力和力矩环境。其次，UKF是为解决飞机空气动力学模型的稳定性和控制导数的更传统问题而开发的。

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