Geometric optimal filtering for an articulated n‐trailer vehicle with unknown parameters

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-09-05 DOI:10.1002/rnc.7597
Damiano Rigo, Nicola Sansonetto, Riccardo Muradore
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Abstract

In this article, we consider the equations of motion for an articulated ‐trailer vehicle with different masses and inertias in the presence of force and torque commands. We design a second‐order optimal filter to estimate the pose of the first vehicle and of the trailers exploiting the Lie group structure and the nonholonomic and hooking constraints. We consider the filtering problem from three different perspectives: in the first masses and inertias are time‐varying and perfectively known, in the second they are known only in the first part of the maneuver, but they are not updated over time. In the last case masses and inertias are considered within the state vector and therefore estimated by the filter. Depending on the needs in real‐life applications (e.g., whether the masses and inertias remain fixed or change during the maneuver and are unknown), the best‐performing filter can be used. The sensing system consists of a GPS‐like configuration (global positioning system) obtained by using an antenna attached to the leading car.
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具有未知参数的 n 型铰接拖车的几何优化滤波
在本文中,我们考虑了具有不同质量和惯性的铰接拖车在力和扭矩指令作用下的运动方程。我们设计了一个二阶最优滤波器,利用李群结构、非整体性约束和挂钩约束来估计第一辆车和拖车的姿态。我们从三个不同的角度来考虑滤波问题:第一种情况下,质量和惯性是随时间变化且完全已知的;第二种情况下,它们仅在机动的第一部分是已知的,但不会随时间更新。在最后一种情况下,质量和惯性被视为状态矢量,因此由滤波器估算。根据实际应用的需要(例如,质量和惯性是保持固定还是在机动过程中发生变化且未知),可以使用性能最佳的滤波器。传感系统由一个类似于 GPS 的配置(全球定位系统)组成,该配置通过连接在前导车上的天线获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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