This paper introduces a novel model-free approach to synthesize virtual sensors for the estimation of dynamical quantities that are unmeasurable at runtime but are available for design purposes on test benches. After collecting a dataset of measurements of such quantities, together with other variables that are also available during on-line operations, the virtual sensor is obtained using machine learning techniques by training a predictor whose inputs are the measured variables and the features extracted by a bank of linear observers fed with the same measures. The approach is applicable to infer the value of quantities such as physical states and other time-varying parameters that affect the dynamics of the system. The proposed virtual sensor architecture - whose structure can be related to the Multiple Model Adaptive Estimation framework - is conceived to keep computational and memory requirements as low as possible, so that it can be efficiently implemented in embedded hardware platforms. The effectiveness of the approach is shown in different numerical examples, involving the estimation of the scheduling parameter of a nonlinear parameter-varying system, the reconstruction of the mode of a switching linear system, and the estimation of the state of charge (SoC) of a lithium-ion battery.
翻译:本文介绍了一种新型的无模式方法,用于综合虚拟传感器,以估计动态数量,这些在运行时无法计量,但可用于测试台站的设计目的。在收集此类数量测量的数据集以及在线操作期间可获得的其他变量之后,通过培训机学习技术获得虚拟传感器,其输入为测量变量的预测器,以及由线性观察家银行以同样的测量尺度提取的特征。该方法适用于推算影响系统动态的物理状态和其他时间变化参数等数量的价值。拟议的虚拟传感器结构——其结构可能与多模型调整估计框架有关——旨在尽可能将计算和记忆要求保持在低水平,从而能够在嵌入的硬件平台中高效地实施。该方法的有效性体现在不同的数字实例中,包括估算非线性参数转换系统的时间表参数,重建线性转换系统的模式,以及估算锂电池的充电量状况(SoC)。