Derivation and Review of Optimization-Based Estimation for Motor Drives and Power Electronics

Optimization-based estimation (OBE) is an advanced estimation technique to improve the performance of motor drives and power converters in the aspects of noise rejection, accuracy, dynamic performance and sensor cost. The related works lack of systematic study and review. This paper fills the void of a comprehensive study on the optimization-based estimation techniques in the main fields of motor drives and power electronics. Different from the conventional linear state estimation methods, the OBE solves a Constrained Finite Time Optimal Estimation (CFTOE) problem by leveraging the past sampling information to derive the estimated values. The working principle of OBE is similar with the well-known model predictive control (MPC) in the aspect of optimization mechanism. The corresponding time sequences of OBE and MPC are dual. This paper studies the duality theory of OBE and MPC and the observability of OBE. Also, the developed techniques for the applications of OBE in motor drives and power electronics are reviewed with the corresponding fundamental modeling, function implementation and advantages. The merits of OBE have been demonstrated in the aspects of feasibility for nonlinear model, estimated accuracy and noise rejection. The OBE is validated experimentally and compared with the traditional Luenberger observer. Also, the challenges and future works are illustrated for OBE in the field of energy conversion systems.