A Speed Observer for Sensorless Control of an Induction Motor

2021 ◽  
Vol 2 (2) ◽  
pp. 54-59
Author(s):  
Anatoly T. KLYUCHNIKOV ◽  

Half a century has passed since the time F. Blaschke received a patent for vector control of an induction motor with a speed sensor and a Hall sensor. Since that time, the transformation of generalized vectors in the Park—Gorev equations as projections on the axes in different coordinate frames aft, dq, and xy has been regarded to be a commonly accepted one. With this approach, five differential and four algebraic equations with cross-links have to be solved for studying the processes in an induction motor, which involves certain inconvenience of analyzing the processes in the machine. Eventually, many versions of high-quality electric motor control systems have been developed. Owing to the progress achieved in computer engineering, it has become possible to solve a fewer number of the Park—Gorev equations in complex form without decomposing the vectors into projections on the coordinate ases aft, dq, xy. At present, the majority of widely used programming languages (FORTRAN, C+, MathCAD, MatLAB, etc.) offer efficient tools for implementing the operations of summing and multiplying complex quantities. In the article, the Park-Gorev equations are solved without decomposing the vectors into their projections on the coordinate axes вб, dq, xy. In so doing, the induction motor complex speed observer uses only two voltage equations and two flux linkage equations. The rotor motion equation is not used to determine the speed. The obtained algorithms for solving by means of a complex speed observer made it possible to determine the currents, electromagnetic torque and motor’s moment of inertia. The proposed algorithms written in the б-в and x-y coordinate systems made it possible to determine the motor speed in its fast start-up process (0.2 s) with an error of less than 1%.

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
C. Ben Regaya ◽  
A. Zaafouri ◽  
A. Chaari

Many scientific researchers have proposed the control of the induction motor without speed sensor. These methods have the disadvantage that the variation of the rotor resistance causes an error of estimating the motor speed. Thus, simultaneous estimation of the rotor resistance and the motor speed is required. In this paper, a scheme for estimating simultaneously the rotor resistance and the rotor speed of an induction motor using fuzzy logic has been developed. We present a method which is based on two adaptive observers using fuzzy logic without affecting each other and a simple algorithm in order to facilitate the determination of the optimal values of the controller gains. The control algorithm is proved by the simulation tests. The results analysis shows the characteristic robustness of the two observers of the proposed method even in the case of variation of the rotor resistance.


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 864
Author(s):  
Youpeng Chen ◽  
Wenshao Bu ◽  
Yanke Qiao

In order to achieve the speed sensorless control of a bearingless induction motor (BL-IM), a novel sliding mode observation (SMO) method of motor speed is researched. First of all, according to the mathematical model of a BL-IM system, the observation model of stator current and that of rotor flux-linkage are derived. In order to overcome the chattering problem of a sliding mode observer, a continuous saturation function is adopted to replace the traditional sign function. Then, the SMO model of motor speed is derived, and the stability of the proposed motor speed SMO method is validated by the Lyapunov stability theory. At the end, the observed motor speed and rotor flux-linkage are applied to a BL-IM inverse “dynamic decoupling control” (DDC) system. Simulation results show that the real-time observation or dynamic tracking of motor speed and rotor flux-linkage are achieved in a more timely manner and more accurately, and higher steady-state observation accuracy is obtained; the proposed SMO method can be used in the BL-IM’s inverse DDC system to realize reliable magnetic suspension operation control without a speed sensor.


2014 ◽  
Vol 577 ◽  
pp. 329-333
Author(s):  
Su Mei Feng ◽  
Zhi Ping Yan ◽  
Xue Mei Wu

In order to improve the reliability of the control system, and to reduce the uncertainty of control process, this paper presents an excellent algorithm to control the induction motor speed using series estimator technology. The physical speed sensor is not used to detect speed of the motor, while applying the vector analysis method, through the stator current and the rotor flux are calculated to estimate motor speed. Corresponding to sensorless control drawbacks, the self-tuning control scheme is proposed through control scheme reformed. The double estimator technology is applied to reform scheme, the first order estimator is used to identify the system parameters, the second order estimator is used to calculate the parameters of the controller on-line, and control function is given real-time to control the speed of the motor, the simulation results show that the given control technology is advanced.


AVITEC ◽  
2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Siti Nur Alima ◽  
Mila Fauziyah ◽  
Denda Dewatama

Induction motors are widely used in the industrial world, home-based businesses as well as in households. Currently in the process of making tofu an induction motor is used as a motor to drive soy blending blades. At this time the use of induction motors is still manually by requiring the operator to regulate the speed of the motor. To reduce operator work, it is necessary to apply PI control as a motor speed controller so that a constant motor rotation is obtained. 1 phase induction motor can be adjusted with variable speed drive (VSD) 0.75KW 1 phase. Blending blade drive uses 0.5HP 1 phase induction motor. In the application of PI control requires some hardware namely Arduino Uno as a minimum system that gives PWM circuit input commands. And the speed sensor as a motor blending speed reader. PI tuning values obtained from the application of the Ziegerl-Nichols I method with the best Kp and Ki tuning values are 1.35 and 0.02673. This research was conducted with 3 speed variables namely 1400 rpm, 1300 and 1200 rpm. From the application of Kp and Ki tuning values, the smallest error value is 4.08% at 1400 rpm with the system response time peak (tp) 5s, rise time (tr) 3s faster, delay time (td) 3s, and settling time (ts) 9s , and a maximum overshoot of 9.8%.


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