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Real-time moving horizon estimation for a vibrating active cantilever

机译:振动主动悬臂的实时移动视界估计

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Vibrating structures may be subject to changes throughout their operating lifetime due to a range of environmental and technical factors. These variations can be considered as parameter changes in the dynamic model of the structure, while their online estimates can be utilized in adaptive control strategies, or in structural health monitoring. This paper implements the moving horizon estimation (MHE) algorithm on a low-cost embedded computing device that is jointly observing the dynamic states and parameter variations of an active cantilever beam in real time. The practical behavior of this algorithm has been investigated in various experimental scenarios. It has been found, that for the given field of application, moving horizon estimation converges faster than the extended Kalman filter; moreover, it handles atypical measurement noise, sensor errors or other extreme changes, reliably. Despite its improved performance, the experiments demonstrate that the disadvantage of solving the nonlinear optimization problem in MHE is that it naturally leads to an increase in computational effort.
机译:由于一系列环境和技术因素,振动结构在整个使用寿命中可能会发生变化。这些变化可以被认为是结构动态模型中的参数变化,而它们的在线估计可以用于自适应控制策略或结构健康监测中。本文在一种低成本的嵌入式计算设备上实现了移动视界估计(MHE)算法,该设备可以实时共同观察主动悬臂梁的动态状态和参数变化。该算法的实际行为已在各种实验方案中进行了研究。已经发现,对于给定的应用领域,运动层估计的收敛速度比扩展的卡尔曼滤波器要快。此外,它可以可靠地处理非典型测量噪声,传感器错误或其他极端变化。尽管其性能有所提高,但实验表明,解决MHE中的非线性优化问题的缺点在于,它自然会导致计算量的增加。

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