Optimization-based parameter tuning of unscented Kalman filter for speed sensorless state estimation of induction machines

State estimation of induction machines may be a difficult problem, due to the non-linear behavior of theirs. For non-linear state estimation, the unscented Kalman filter (UKF) is a well-known extension of the linear Kalman filter. Operation of the UKF algorithm strongly depends on the process and measurement noise covariance parameters of the estimator. Determination of these parameters is not straightforward and can be difficult, especially if the number of state variables and hence the system complexity is relatively high. In this paper, the UKF algorithm is applied for speed sensorless state estimation of induction machines in such a way that seven state variables are estimated from the measured stator currents and from the known excitation voltages. In order to tune the noise parameters of the UKF, a new, optimization-based method is presented. This tuning method provides adequate behavior for the observer beside difficult operating conditions as it has been shown by simulation experiment.

[1]  Ravi Kumar Jatoth,et al.  Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking , 2010 .

[2]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[3]  S. Kumar,et al.  A critical evaluation and experimental verification of Extended Kalman Filter, Unscented Kalman Filter and Neural State Filter for state estimation of three phase induction motor , 2011, Appl. Soft Comput..

[4]  T. Kishore Kumar,et al.  Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Bearings Only Tracking , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[5]  Cristian Lascu,et al.  State Estimation of Induction Motor Drives Using the Unscented Kalman Filter , 2012, IEEE Transactions on Industrial Electronics.

[6]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[7]  T. M. Wolbank,et al.  State and parameter estimation for field-oriented control of induction machine based on unscented Kalman filter , 2012, International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion.

[8]  D. Fodor,et al.  Sensorless control of field oriented AC motors using embedded DSP board , 1999, ISIE '99. Proceedings of the IEEE International Symposium on Industrial Electronics (Cat. No.99TH8465).

[9]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Frede Blaabjerg,et al.  Comparative study of adaptive and inherently sensorless observers for variable-speed induction-motor drives , 2006, IEEE Transactions on Industrial Electronics.

[11]  José Jaime Da Cruz,et al.  Complete offline tuning of the unscented Kalman filter , 2017, Autom..

[12]  Henrique Marra Menegaz,et al.  A Systematization of the Unscented Kalman Filter Theory , 2015, IEEE Transactions on Automatic Control.

[13]  M. Ehsani,et al.  Simple Derivative-Free Nonlinear State Observer for Sensorless AC Drives , 2006, IEEE/ASME Transactions on Mechatronics.

[14]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[15]  M. Sami Fadali,et al.  Square Root Unscented Kalman Filters for state estimation of induction motor drives , 2011 .

[16]  P. Siano,et al.  Sensorless nonlinear control of induction motors using Unscented Kalman Filtering , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[17]  R. Toth,et al.  Speed sensorless linear parameter variant H/sub /spl infin// control of the induction motor , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[18]  Shaahin Filizadeh.  Electric Machines and Drives: Principles, Control, Modeling, and Simulation , 2013 .

[19]  Krisztián Horváth,et al.  Speed sensorless field oriented control of induction machines using unscented Kalman filter , 2017, 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP).