Online Black-Box Modelling for IoT Digital Twins through Machine Learning

Many applications involving physical systems, such as system control or fault detection, call for a behavioral, black-box, or digital twin of the real system. By observing input-output pairs, a nonlinear system’s black-box twinning model can be built, thus enabling real-time accurate estimation of the system’s health and status. We propose a modeling approach that can be implemented with little hardware resources and predicts system output with acceptable accuracy for a wide range of applications. This approach consists of building a compact numerical model, based on the concept of sum-decomposability, with reduced computational complexity and memory requirements, well suited for microcontroller based IoT applications. The black-box modeling theory, the sizing process, and the learning method are reported. The outputs of two examples of non-linear systems are replicated in real-time using a pioneer experimental setup built around a microcontroller. According to experimental results, online learning and prediction are performed at 1 kS/s with a prediction error comparable to the resolution of the digitalized input-output data.