Speed estimation of an induction motor drive using an optimized extended Kalman filter

Author name used in this publication: K. L. ShiAuthor name used in this publication: Y. K. WongAuthor name used in this publication: S. L. Ho2001-2002 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Optimized Kalman filters for sensorless vector control induction motor drives

This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions

Industrial applications of the Kalman filter:a review

International audienc

Pulse-width modulation direct torque control induction motor drive with Kalman filter

The paper deals with application of Kalman filter in induction motor drive using pulse-width modulation direct torque control (PWM-DTC). In the first part, the conventional PWM-DTC drive is described and Kalman filter is utilized to filter components of stator current vector those are assumed to be disturbed by white noise. The second part contains simulation results that are obtained in different cases of load torque, process and measurement noise covariances. The integral time absolute error (ITAE) performance index, undershoot, ripple of important quantities are used to compare the conventional drive structure and proposed drive structure with Kalman filter. The simulation results confirm the expected dynamic response of the proposed structure

A comparative study of Kalman filtering for sensorless control of a permanent-magnet synchronous motor drive

Author name used in this publication: Borsje, P.Author name used in this publication: Wong, Y. K.Author name used in this publication: Ho, S. L.Refereed conference paper2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Observer-based Fault Detection and Diagnosis for Mechanical Transmission Systems with Sensorless Variable Speed Drives

Observer based approaches are commonly embedded in sensorless variable speed drives for the purpose of speed control. It estimates system variables to produce errors or residual signals in conjunction with corresponding measurements. The residual signals then are relied to tune control parameters to maintain operational performance even if there are considerable disturbances such as noises and component faults. Obviously, this control strategy outcomes robust control performances. However, it may produce adverse consequences to the system when faults progress to high severity. To prevent the occurrences of such consequences, this research proposes the utilisation of residual signals as detection features to raise alerts for incipient faults. Based on a gear transmission system with a sensorless variable speed drive (VSD), observers for speed, flux and torque are developed for examining their residuals under two mechanical faults: tooth breakage with different degrees of severities and shortage of lubricant at different levels are investigated. It shows that power residual signals can be based on to indicate different faults, showing that the observer based approaches are effective for monitoring VSD based mechanical systems. Moreover, it also shows that these two types fault can be separated based on the dynamic components in the voltage signals

A new approach for speed estimation in induction motor drives based on a reduced-order extended Kalman filter

This paper presents and proposes a new approach to achieve robust speed estimation in induction motor sensorless control. The estimation method is based on a reduced-order extended Kalman filter (EKF), instead of a full-order EKF. The EKF algorithm uses a reduced-order state-space model structure that is discretized in a particular and innovative way proposed in this paper. With this model structure, only the rotor flux components are estimated, besides the rotor speed itself. Important practical aspects and new improvements are introduced that enable us to reduce the execution time of the algorithm without difficulties related to the tuning of covariance matrices, since the number of elements to be adjusted is reduced

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs

Speed estimation of induction motor using model reference adaptive system with Kalman filter

The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control