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.

本文介绍了一种新的无模型方法来合成虚拟传感器，用于估计在运行时无法测量但可用于测试台上的设计目的的动态量。在收集这些量的测量数据集以及在线操作期间也可用的其他变量后，虚拟传感器是使用机器学习技术通过训练预测器获得的，该预测器的输入是测量变量和银行提取的特征线性观察者的馈送相同的措施。该方法适用于推断影响系统动力学的物理状态和其他时变参数等量的值。

该方法的有效性体现在不同的数值例子中，包括非线性变参数系统的调度参数估计、开关线性系统模式的重构以及充电状态 (SoC) 的估计。锂离子电池。