Programs
- Ph. D. in Mechanical Engineering -
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Conference Paper
Publisher : 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)
Source : 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), IEEE, Kannur, India (2017)
Url : https://ieeexplore.ieee.org/document/8342546
ISBN : 9781509061068
Keywords : angular velocity control, CKF, Covariance matrices, Cubature Kalman Filter (CKF), derivative-free online sequential state estimator, dq reference frame, estimation tool, Kalman filters, Linearization, machine control, mathematical model, Non-linear State Estimation, nonlinear filters, nonlinear state estimation, original Kalman filter, Permanent magnet motors, Permanent Magnet Synchronous Motor, Permanent Magnet Synchronous Motor (PMSM), PMSM, rotor position, rotors, Speed control, square-root Cubature Kalman Filter, square-root filtering, State estimation, stationary αβ reference frame, Synchronous motors
Campus : Amritapuri
School : School of Engineering
Center : Wind Energy Center
Department : Electrical and Electronics
Year : 2017
Abstract : This paper presents a non-linear state estimation of Permanent Magnet Synchronous Motor (PMSM) with currently developed non-linear estimator. The estimation tool used is Cubature Kalman Filter (CKF). The CKF is a derivative-free online sequential state estimator; compared to other nonlinear filters, it depends on integration for its operation. It inherits the property of original Kalman filter, including derivative-free and square-root filtering for improved stability and reliability. It can minimize errors due to linearization. Despite of having several advantages, CKF is not yet explored in the field of electric drives. Here PMSM is modeled in dq reference frame and for modeling of CKF and its algorithm uses modeling of PMSM in stationary αβ reference frame. The speed and rotor position is estimated and then it is feedback to PMSM for speed control. Simulation results shows the efficiency proposed estimator.
Cite this Research Publication : D. G. Pillai, A. Vivek, and V. Srikanth, “Non-linear state estimation of PMSM using derivative-free and square-root Cubature Kalman Filter”, in 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, 2017.