Sensorless vector control of asynchronous machine based on reduced order Kalman filter

The article describes sensorless vector control of the asynchronous machine based on Kalman filter. A control system block diagram is presented along with a functional scheme of the original speed observer using dynamic system state vector filter estimator. The synthesis of current regulator using machine parameters determined by inverse r model is shown. The method of a control matrix dimension reduction is proposed by removal the flux linkage calculations out of Kalman filter algorithms. Current and speed regulators, as well as a flux estimator, were implemented in the Mexbios Development Studio simulation environment. The robustness of a speed observer and the efficiency of the asynchronous machine sensorless control system were confirmed by the simulation results.

[1]  A. BENNASSAR,et al.  SPEED SENSORLESS INDIRECT FIELD ORIENTED CONTROL OF INDUCTION MOTOR USING AN EXTENDED KALMAN FILTER , 2016 .

[2]  Mohamed Boussak,et al.  A high-performance sensorless indirect stator flux orientation control of induction motor drive , 2006, IEEE Transactions on Industrial Electronics.

[3]  Frank L. Lewis,et al.  Applied optimal control & estimation : digital design & implementation , 1992 .

[4]  F. Alonge,et al.  Speed and rotor flux estimation of induction motors via on-line adjusted Extended Kalman Filter , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[5]  J. Holtz,et al.  Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and parameter identification , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[6]  Hu Jun-chen.  Simulation of Vector Control System of Asynchronous Motor , 2006 .

[7]  M. R. Douiri,et al.  Rotor resistance and speed identification using extended Kalman filter and fuzzy logic controller for induction machine drive , 2012, 2012 International Conference on Multimedia Computing and Systems.

[8]  Gennady M. Koshkin,et al.  Kalman filtering and control algorithms for systems with unknown disturbances and parameters using nonparametric technique , 2015, 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR).

[9]  Evgeniy Solodkiy,et al.  Practice of using MexBIOS Development Studio technologies in educational process , 2016, 2016 IX International Conference on Power Drives Systems (ICPDS).

[10]  V. A. Tupysev,et al.  Comparative analysis of reduced Kalman filters with guaranteed estimation quality , 2012 .

[11]  Eul-Jae Lee,et al.  Robust speed estimation for speed sensorless vector control of induction motors , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[12]  Murat Barut,et al.  Experimental Evaluation of Braided EKF for Sensorless Control of Induction Motors , 2008, IEEE Transactions on Industrial Electronics.

[13]  Murat Barut,et al.  Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters , 2007, IEEE Transactions on Industrial Electronics.

[14]  Ruslan Zhiligotov,et al.  Sensorless vector control of a permanent magnet synchronous motor , 2019 .

[15]  A. M. Zyuzev,et al.  The state of and prospects for using hardware—software simulators of electrotechnical complexes , 2015 .

[16]  B. Jayanand,et al.  Sensorlees direct vector control of induction motor using a Kalman filter trained neuro observer , 2001 .

[17]  Joachim Holtz,et al.  Sensorless control of induction motor drives , 2002, Proc. IEEE.

[18]  Lei Yuan,et al.  Vector Control of an Induction Motor based on a DSP , 2011 .