Using a Bivariate Polynomial in an EKF for State and Inductance Estimations in the Presence of Saturation Effects to Adaptively Control a PMSM

• DOI: 10.1109/access.2022.3215511
• Publication Date: 2022-11-03T21:40:57Z
• Authors: Tanja Zwerger; Paolo Mercorelli
• Keywords: Parameter Estimation; extended Kalman filter; Extended Kalman Filter; 02 engineering and technology; /dk/atira/pure/core/keywords/559014392; name=Engineering; bivariate polynomial; TK1-9971; Permanent Magnetic Synchronous Machine; PMSM; 0202 electrical engineering, electronic engineering, information engineering; Electrical engineering. Electronics. Nuclear engineering; Bivariate Polynomial; parameter estimation

This paper takes into consideration a combined extended Kalman filter (CEKF) by using bivariate polynomial for the estimation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{d}$ </tex-math></inline-formula> and notation="LaTeX">$L_{q}$ in saturation conditions. In context (KF), are modelled as nonlinear augmented states to control permanent magnetic synchronous machine (PMSM). Once estimated, continuous monitoring conditions is achieved ensure desired torque even under The proposed adaptive method based on maximum per ampere (MTPA) consists an feedforward PI controller. A discussion light measured results Hardware-in-the-loop also included.