Optimization-Based Position Sensorless Finite Control Set Model Predictive Control for IPMSMs

Abstract

This paper presents nonlinear optimization-based position and speed estimation scheme for IPMSM drives with arbitrary signal injection generated by inherent switching ripples associated with finite control set model predictive control (FCSMPC). The existing standard sensorless techniques are not suitable for FCSMPC which applies voltage vectors directly to an electrical machine without a modulator. The proposed method optimizes the nonlinear cost function derived from the standard IPMSM model with respect to position and speed at every sampling interval. This method can be applied to any type of signal injection and, hence, an ideal candidate for sensorless FCSMPC. In this method, the signal injection is needed only to generate persistent excitation to maintain the observability at low speeds. A strong persistent excitation is always present with FCSMPC except at standstill where the control applies null vector when the reference currents are zero. This situation is overcome in this paper by introducing a small negative $d$ -axis current at standstill. Thus, the proposed method can estimate the position and speed over a wide speed range starting from standstill to the rated speed without a changeover or additional signal injection. This paper also presents detailed convergence analysis and proposes a compensator for standstill operation that prevents converging to saddle and symmetrical solutions, and therefore, also eliminates the well-known ambiguity of $\pi$ rad in position estimation. The performance of the proposed sensorless scheme is experimentally verified for a wide range of operating conditions.