scholarly journals Direct torque control of induction motor with extended Kalman filter

2002 ◽  
Author(s):  
D. Pai A ◽  
L. Umanand ◽  
N.J. Rao
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Torque Control

Sensorless finite-state predictive torque control of induction motor fed by four-switch inverter using extended Kalman filter

2018 ◽  
Vol 37 (6) ◽  
pp. 2006-2024 ◽  
Author(s):  
Mohamed Chebaani ◽  
Amar Goléa ◽  
Med Toufik Benchouia ◽  
Noureddine Goléa
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Operating Conditions ◽  
Torque Control ◽  
Switching Frequency ◽  
Content Type ◽  
Finite State ◽  
Torque Ripples

Purpose Direct Torque Control (DTC) of induction motor drives is a well-established technique owing to features such as fast dynamic and insensibility to motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. To improve DTC performance, this study aims to propose and implement a sensorless finite-state predictive torque control using extended Kalman Filter in dSPACE environment. Design/methodology/approach This paper deals with the design of an extended Kalman filter for estimating the state of an induction motor model and for sensorless control of systems using this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. Findings Simulation and experimental results reveal that the drive system, associated with this technique, can effectively reduce flux and torque ripples with better dynamic and steady state performance. Further, the proposed approach maintains a constant switching frequency. Originality/value The proposed speed observer have been developed and implemented experimentally under different operating conditions such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.

scholarly journals Sensorless Direct Torque Control of the Six-Phase Induction Motor by Fast Reduced Order Extended Kalman Filter

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Asghar Taheri ◽  
Hai-Peng Ren ◽  
Chun-Huan Song
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Induction Machine ◽  
Torque Control ◽  
Motor Speed ◽  
Low Speed ◽  
Simplified Algorithm ◽  
Stator Resistance

In this paper, a simple extended Kalman Filter (EKF) controller for direct torque control (DTC) of a six-phase induction machine in all speed ranges is proposed. The aim of this paper is to decrease the execution time of EKF modeling of a six-phase induction motor. The proposed method is fast and can operate online. If the machine parameters are changed during the operation, the EKF algorithm is activated to find parameters used for controlling the motor. In low speed, not only the motor speed measurement but also the DTC of machine is difficult. The EKF model can estimate speed, flux, load torque, and stator resistance in low speed; thus, optimization can be performed in all loads and speed range. The proposed method increases the accuracy of DTC of the six-phase induction machine and decreases the computation cost of the system using the simplified algorithm. The simulation and experimental results verify the effectiveness and the robustness of the proposed method against parameter variations.

Three-Level DTC Based on Fuzzy Logic and Neural Network of Sensorless DSSM Using Extended Kalman Filter

2015 ◽  
Vol 5 (4) ◽  
pp. 453 ◽  
Author(s):  
Elakhdar Benyoussef ◽  
Abdelkader Meroufel ◽  
Said Barkat
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Synchronous Machine ◽  
Double Star ◽  
Torque Control ◽  
Operation Conditions ◽  
Stator Flux

This paper presents a direct torque control is applied for salient-pole double star synchronous machine without mechanical speed and stator flux linkage sensors. The estimation is performed using the extended Kalman filter known by it is ability to process noisy discrete measurements. Two control approaches using fuzzy logic DTC, and neural network DTC are proposed and compared. The validity of the proposed controls scheme is verified by simulation tests of a double star synchronous machine. The stator flux, torque, and speed are determined and compared in the above techniques. Simulation results presented in this paper highlight the improvements produced by the proposed control method based on the extended Kalman filter under various operation conditions.

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