Devos Thijs, Kirchner Matteo, Croes Jan, Desmet Wim, Naets Frank
LMSD Research Group, Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium.
DMMS Core Lab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium.
Sensors (Basel). 2021 Nov 11;21(22):7492. doi: 10.3390/s21227492.
To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.
为了适应新型机电一体化系统日益增加的复杂性以及更严格的安全法规，先进的估计算法目前正朝着更高的模型复杂度转变。然而，更复杂的模型往往面临所需可观测性和计算量方面的问题。此外，传感器选择通常仍基于经验和便利性进行务实操作，而一种更具成本效益的方法是根据传感器的有效估计性能来评估其性能。在这项工作中，提出了一种新颖的估计和传感器选择方法，该方法能够针对不可观测和非线性系统模型稳定估计器黎卡提方程。当估计器仅针对某些不一定依赖于所有系统状态的特定感兴趣量时，这是可行的。提出了一种基于扩展卡尔曼滤波器的估计框架，其中基于卡尔曼可观测性矩阵的奇异值分解（SVD）将黎卡提方程投影到可观测子空间上。此外，还提出了一种传感器选择方法，该方法根据感兴趣量的误差协方差评估的估计性能对可能的传感器进行排序。这使得无需进行昂贵的测试活动就能评估传感器组的性能。最后，在一个数值示例以及一个汽车实验验证案例中对所提出的方法进行了评估。
