Adaptive state space representations enabling reliable and robust decision-making in asynchronous drives for mechatronic applications

In contemporary mechatronic applications decision-making is often based on information about the underlying model governing the dynamical evolution, in order to ensure optimal operation with respect to a prioritized objective. Modeling errors stemming from parameter uncertainty or varying operational conditions result in inevitable deviations from the theoretical estimate and consequently in suboptimal operation. Intelligent systems need to be equipped with inherent means to compensate for these a priori unknown discrepancies, hereby guaranteeing a robust operation in uncertain environments. In this manuscript, advanced filtering techniques are applied to assess both an optimal model representation and state estimates. An appropriate interconnection between both model and state estimation is determined. The proposed methodology is demonstrated for an electric drive, embodying a DC-source, a voltage source inverter (VSI) and an asynchronous machine, as the presence of discrete switching sequences and physical constraints introduces additional challenges. Results prove that the error on the state estimates can be improved by 92.7-97.2%, outperforming the classical estimation techniques, while the relative model mismatch is scaled down to 0.03%, even in highly demanding scenarios. The introduced strategy thus enables high-fidelity virtual sensing and reliable decision-making procedures for advanced asynchronous drives when modeling errors can be anticipated.