A COMPARISON STUDY BETWEEN EXTENDED KALMAN FILTER (EKF) AND IMMERSION & INVARIANCE (I&I) METHODS TO ESTIMATE THE SPEED OF PMSM MOTOR BASED ON THE STRUCTURE OF PORT-CONTROLLED HAMILTONIAN SYSTEM

Abstract

Permanent Magnet Synchronous Motor (PMSM) is used increasingly in a wide range of industrial applications due to its advantageous features such as high efficiency, high torque to inertia ratio, low noise, and robustness. An accurate knowledge of motor parameters is essential in order to achieve a better performance. In this study, the problem of speed tracking of the PMSM motor considering uncertain speed is addressed. The model structure of the PMSM motor with uncertain speed is formulated in the structure of port-controlled Hamiltonian system in discrete-time setting. An adaptive discrete-time interconnection and damping assignment passivity based controller (IDA-PBC) for the uncertain PMSM motor is proposed. Besides, discrete-time immersion and invariance (I&I) based estimator is designed to estimate the uncertain motor speed. This estimated speed is used in the IDA-PBC controller to provide an automatic tuning for the motor speed. The asymptotic stability of the estimator is achieved based on Lyapunov theory. The proposed adaptive controller is applied to the PMSM motor where the controller performance is tested by Matlab/Simulink. The proposed I&I based estimation method is compared to the estimation method of Extended Kalman Filter (EKF). Simulation results show the productivity and effectiveness of the proposed method