Online estimation of induction motor state space system using Recursive Least-Squares

Estimation of induction motors has mostly focused on parameter identification for use in Indirect Field Oriented Control (IFOC). Estimation of a general state space system has not received the same attention. In this paper, induction motor state space system is estimated online using Recursive Least-Squares (RLS). The system is estimated as seen from the inverter. More modern method such as subspace identification is available, but requires more computational power making it difficult for online applications. The simplicity of RLS makes it suited for online implementation. Using Simulink, a custom real-time estimation block has been developed. It allows for several estimation methods to be selected online. Timing information from the modulator is used to synchronize sampling to switching periods, reducing noise to a minimum. The estimated model is compared to an analytical dynamic induction motor model.