Reference Input Signals Formation for Induction Motor Control in Traction Drive
نویسندگان
چکیده
The purpose of this work is to build the analytical improved (with resistances estimation) real time computation reference inputs for rotor flux and torque in vector control system an induction motor a traction electric drive. must maximize electromagnetic conditions voltage source instability, particularly magnetic field weakening mode. conventional way mode form coupling task inversely proportional speed or square second third zones respectively. Such input signals are not able provide maximum capability over entire range, achieved different ways. For instance, feedback useful enhancement internal external perturbations. A wide change with reveals nonlinear properties motor. However, systems, proportional-integrating (PI) regulators usually used. Therefore, firstly, linear PI controllers be robust, secondly, guaranty linear, saturated state each controller. proposed expressions calculating as functions actual approximate expressions. estimation possible error shows that acceptable. Simulation performed taking into account calculation signal by microcontroller dynamics frequency invertor. simulation resulting validates effectiveness using formation real-time setting torque.
منابع مشابه
Analysis of Speed Control in DC Motor Drive Based on Model Reference Adaptive Control
This paper presents fuzzy and conventional performance of model reference adaptive control(MRAC) to control a DC drive. The aims of this work are achieving better match of motor speed with reference speed, decrease of noises under load changes and disturbances, and increase of system stability. The operation of nonadaptive control and the model reference of fuzzy and conventional adaptive contr...
متن کاملModel Reference Neural Predictive Controller for Induction Motor Drive
In this paper an accurate nonlinear model of induction motor using an artificial neural network (ANN) is given. This modeling technique is done by using the data from the system inputs/outputs information without requiring the knowledge about machine parameters. The ANN training is carried out off-line using the Levenberg-Marquardt algorithm. Then, the proposed neural network model is used as p...
متن کاملanalysis of speed control in dc motor drive based on model reference adaptive control
this paper presents fuzzy and conventional performance of model reference adaptive control(mrac) to control a dc drive. the aims of this work are achieving better match of motor speed with reference speed, decrease of noises under load changes and disturbances, and increase of system stability. the operation of nonadaptive control and the model reference of fuzzy and conventional adaptive contr...
متن کاملdevelopment and implementation of an optimized control strategy for induction machine in an electric vehicle
in the area of automotive engineering there is a tendency to more electrification of power train. in this work control of an induction machine for the application of electric vehicle is investigated. through the changing operating point of the machine, adapting the rotor magnetization current seems to be useful to increase the machines efficiency. in the literature there are many approaches wh...
15 صفحه اولApplication of Speed Estimation Techniques for Induction Motor Drives in Electric Traction Industries and vehicles
Induction motors are the most commonly used in the traction industries and electric vehicles, due to their low primary cost, low maintenance costs, and good performance. Speed identification is needed for the induction motor drives. However, using of speed sensors in the induction motor drives is associated with problems such as, extra cost, reduced reliability, added mounting space, etc.. Ther...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Izvestiâ Vysših U?ebnyh Zavedenij i Ènergeti?eskih ob Edinennij SNG. Ènergetika
سال: 2023
ISSN: ['1029-7448', '2414-0341']
DOI: https://doi.org/10.21122/1029-7448-2023-66-3-205-214