Nonlinear state observer for PMSM with evolutionary algorithm

Abstract

This paper is addressed to a problem of state observation for permanent magnet synchronous motor (PMSM) and its design parameter tuning via evolutionary algorithm. Recently proposed flux, position and speed observer that is based on nonlinear parameterization of motor model and dynamic regressor extension and mixing (DREM) technique is considered. Though global asymptotic convergence of this observer was guaranteed for all positive real values of several design parameters the choice of their values for a particular motor was not well considered. To overcome this drawback a genetic algorithm is used to perform automatic tuning of required coefficients minimizing cost function that is associated with estimation errors. Simulation results supplemented by verification demonstrate the efficiency of the proposed approach resulting in a set of easy-to-implement-in-practice values of design parameters.