The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to obtain. This challenge can be overcome by the use of adaptive techniques. Based on an error-extended Kalman filter with force estimation (errorEKF-FE), the adaptive method known as maximum likelihood is adjusted to fulfill the multibody requirements. This new filter is called adaptive error-extended Kalman filter (AerrorEKF-FE). In order to present a general approach, the method is tested on two different mechanisms in a simulation environment. In addition, different sensor configurations are also studied. Results show that, in spite of the maneuver conditions and initial statistics, the AerrorEKF-FE provides estimations with accuracy and robustness. The AerrorEKF-FE proves that adaptive techniques can be applied to multibody-based state estimators, increasing, therefore, their fields of application.

中文翻译：

---

使用自适应扩展卡尔曼滤波器的基于 ultibody 的输入和状态观察器

这项工作的目的是探索基于多体动力学的状态估计器的自适应方法的适用性，这些方法存在严重的非线性。卡尔曼滤波器的性能依赖于难以获得的噪声协方差矩阵的知识。可以通过使用自适应技术来克服这一挑战。基于具有力估计的误差扩展卡尔曼滤波器 (errorEKF-FE)，调整称为最大似然的自适应方法以满足多体要求。这种新滤波器称为自适应误差扩展卡尔曼滤波器 (AerrorEKF-FE)。为了呈现一般方法，该方法在模拟环境中的两种不同机制上进行测试。此外，还研究了不同的传感器配置。结果表明，尽管存在机动条件和初始统计数据，AerrorEKF-FE 仍可提供准确且稳健的估计。AerrorEKF-FE 证明自适应技术可以应用于基于多体的状态估计器，从而增加它们的应用领域。