Neural Adaptive Kalman Filter for Sensorless Vector Control of Induction Motor

• Published in: International Journal of Power Electronics and Drive Systems Vol. 8; no. 4; p. 1841
• Main Authors: Imane, Ghlib, Youcef, Messlem, Abdelmadjid, Gouichiche, Zakaria, Chedjara
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.12.2017
• Subjects: Adaptive filters; Computer simulation; Kalman filters; Low speed; Model reference adaptive control; Neural networks; Performance enhancement; Robustness (mathematics); Rotor speed
• ISSN: 2088-8694; 2722-256X; 2088-8694
• DOI: 10.11591/ijpeds.v8.i4.pp1841-1851

This paper presents a novel neural adaptive Kalman filter for speed sensorless field oriented vector control of induction motor. The adaptive observer proposed here is based on MRAS (model reference adaptive system) technique, where the linear Kalman filter calculate the stationary components of stator current and the rotor flux and the rotor speed  is calculated with an adaptive mechanism. Moreover, to improve the performance of the PI classical controller under different conditions, a novel adaptation scheme based on ADALINE (ADAptive LInear NEuron) neural network is used. It offers a solution to the PI parameters to stabilize automatically about their optimum values and speed estimation to converge quicker to the real. The proposed adaptive Kalman filter represents a good comprise between estimation accuracy and computationally intensive. The simulation results showed the robustness, efficiency, and superiority of the proposed scheme compared to the classical method even in low speed region.