Speed Sensorless Control Of Induction Motor Research Articles

Cascaded Multilevel Inverter IM Speed-Sensorless Vector Control System Based on MRAS Theory

Cascaded multilevel inverters could realize high-voltage output based on a series connection of power cells which use standard low-voltage component configurations. This characteristic could achieve high-quality output voltage waveforms and input current waveforms. These merits are made for motor control, especially in the field of speed-sensorless vector control of induction motor based on the theory of MRAS. This paper constructs a simulation system with the help of MATLB/SIMULINK and a system combined cascaded H-bridge multilevel inverter with induction motor with the help of DSP and FPGA. The simulation and experiment results verified the superiority of cascaded multilevel inverter applied on the MRAS speed-sensorless vector control of induction motor.

Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors

This paper presents the real-time implementation of a bi input-extended Kalman filter (EKF) (BI-EKF)-based estimator in order to overcome the simultaneous estimation problem of the variations in stator resistance <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rs</i> and rotor resistance <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rr</i> ' aside from the load torque <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tL</i> and all states required for the speed-sensorless control of induction motors (IMs) in the wide speed range. BI-EKF algorithm consists of a single EKF algorithm using consecutively two inputs based on two extended IM models developed for the simultaneous estimation of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rr</i> ' and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rs</i> . Therefore, from the point of real-time implementation, it requires less memory than previous EKF-based studies exploiting two separate EKF algorithms for the same aim. By using the measured stator phase voltages and currents, the developed estimation algorithm is tested with real-time experiments under challenging variations of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rs</i> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rr</i> ', and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tL</i> in a wide speed range; the results obtained from BI-EKF reveal significant improvement in the all estimated states and parameters when compared with those of the single EKFs estimating only <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rr</i> ' or <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rs</i> .

Bi Input-extended Kalman filter based estimation technique for speed-sensorless control of induction motors

This study offers a novel extended Kalman filter (EKF) based estimation technique for the solution of the on-line estimation problem related to uncertainties in the stator and rotor resistances inherent to the speed-sensorless high efficiency control of induction motors (IMs) in the wide speed range as well as extending the limited number of states and parameter estimations possible with a conventional single EKF algorithm. For this aim, the introduced estimation technique in this work utilizes a single EKF algorithm with the consecutive execution of two inputs derived from the two individual extended IM models based on the stator resistance and rotor resistance estimation, differently from the other approaches in past studies, which require two separate EKF algorithms operating in a switching or braided manner; thus, it has superiority over the previous EKF schemes in this regard. The proposed EKF based estimation technique performing the on-line estimations of the stator currents, the rotor flux, the rotor angular velocity, and the load torque involving the viscous friction term together with the rotor and stator resistance is also used in the combination with the speed-sensorless direct vector control of IM and tested with simulations under the challenging 12 scenarios generated instantaneously via step and/or linear variations of the velocity reference, the load torque, the stator resistance, and the rotor resistance in the range of high and zero speed, assuming that the measured stator phase currents and voltages are available. Even under those variations, the performance of the speed-sensorless direct vector control system established on the novel EKF based estimation technique is observed to be quite good.

Speed-sensorless control of induction motor based on reactive power with rotor time constant identification

This study presents a modified model reference adaptive system speed estimator for induction motors (IMs) based on regulation of the instantaneous rotor magnetising reactive power, which does not use integration or stator resistance in the reference model. The proposed modifications allow analytical tuning and facilitate implementation of the estimator in a digital signal processor. The detuning of the estimator with respect to the rotor time constant is exploited as a source of information in a novel parallel identification mechanism for this parameter. The rotor time constant error is obtained by correlation of the high-frequency components of the speed estimator error and the torque producing current, thus enabling simultaneous estimation of the IM speed and the rotor time constant. The robustness and accuracy of the proposed schemes are verified experimentally.

Parameter Sensitivity Analysis of an ImprovedOpen-Loop Speed Estimate forInduction Motor Drives

In the last decades, several schemes have been proposed for speed-sensorless control of induction motor drives. Promising approaches are closed-loop observers, as model reference adaptive systems (MRAS) and Kalman filters, and open-loop estimators, which have recently aroused lively interest because of their simplicity and low-cost profile. As known, open-loop algorithms use only motor equations to derive the speed estimation, whose accuracy turns out to be strictly related to the motor model and its parameters. This paper presents an improved open-loop speed estimation algorithm, which uses reference voltages and measured currents, after that a state-of-the-art dead time compensation has been performed. The often undervalued topic of parameter sensitivity is handled by an accurate mathematical investigation, confirmed by a complete set of both simulated and experimental results. The estimated speed is then used as feedback signal in a closed loop speed control, and a performance evaluation is accomplished as well.

Switching EKF technique for rotor and stator resistance estimation in speed sensorless control of IMs

High performance speed sensorless control of induction motors (IMs) calls for estimation and control schemes that offer solutions to parameter uncertainties as well as to difficulties involved with accurate flux/velocity estimation at very low and zero speed. In this study, a new EKF based estimation algorithm is proposed for the solution of both problems and is applied in combination with speed sensorless direct vector control (DVC). The technique is based on the consecutive execution of two EKF algorithms, by switching from one algorithm to another at every n sampling periods. The number of sampling periods, n, is determined based on the desired system performance. The switching EKF approach, thus applied, provides an accurate estimation of an increased number of parameters than would be possible with a single EKF algorithm. The simultaneous and accurate estimation of rotor, R r ′ and stator, R s resistances, both in the transient and steady state, is an important challenge in speed sensorless IM control and reported studies achieving satisfactory results are few, if any. With the proposed technique in this study, the sensorless estimation of R r ′ and R s is achieved in transient and steady state and in both high and low speed operation while also estimating the unknown load torque, velocity, flux and current components. The performance demonstrated by the simulation results at zero speed, as well as at low and high speed operation is very promising when compared with individual EKF algorithms performing either R r ′ or R s estimation or with the few other approaches taken in past studies, which require either signal injection and/or a change of algorithms based on the speed range. The results also motivate utilization of the technique for multiple parameter estimation in a variety of control methods.

Design of a Robust Stable Flux Observer for Induction Motors

This paper presents a robustly adaptive flux observer for speed-sensorless induction motor control. The proposed approach employs additional robustifying signals to cope with the parametric uncertainties instead of designing an estimator, which has been normally used in power electronic drives. For that, the sliding-mode like adaptive controls are designed and their gain parameters are determined so that the observer dynamics are stable in the sense of Lyapunov, and furthermore they can guarantee the robustness against parametric uncertainties in induction motor systems. Estimated rotor speed is to be used to generate feedback control signal for the speed sensorless vector control system. To show the validity and efficiency of the proposed system, simulation results are presented.

Internal Model Controller of an ANN Speed Sensorless Controlled Induction Motor Drives

Internal Model Controller of an ANN Speed Sensorless Controlled Induction Motor Drives

LOW SPEED SENSORLESS VARIABLE STRUCTURE CONTROL OF INDUCTION MOTOR

Torque and speed sensorless induction motor control is presented in this paper. The idea is realized using a sliding mode closed loop rotor flux observer for estimation of electromotive force of machine. This signal is then used in nonlinear stator flux and torque control of induction motor and in rotor flux observer for speed and flux estimation. The analysis of the proposed method is included. Proposed control scheme was implemented on DSP system extended with FPGA where PWM procedure with dead time compensation was realized. Experimental results demonstrated high efficiency of the proposed estimation and control method.

A New Scheme for Speed-Sensorless Control of Induction Motor

Various control algorithms have been proposed for the speed-sensorless control of an induction motor. These sensorless algorithms are mainly based on the speed feedback with the flux and speed estimations. This paper proposes a new scheme for the speed-sensorless control of an induction motor. The proposed scheme is based on the current estimation without the flux and speed estimations, in which the controlled stator voltage is applied to the induction motor so that the difference between stator currents of the mathematical model and motor may be forced to decay to zero. The performance of the proposed scheme is verified through simulation and experiment.

A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives

Accurate knowledge of stator resistance is of utmost importance for correct operation of a number of speed sensorless induction motor control schemes in the low speed region. Since stator resistance inevitably varies with operating conditions, stable and accurate operation at near-zero speed requires an appropriate online identification algorithm for the stator resistance. The paper proposes such an identification algorithm, which is developed for the rotor flux-based model reference adaptive system (MRAS) type of the speed estimator in conjunction with a rotor flux oriented control scheme. In this speed estimation method, only one degree of freedom (out of the two available) is utilized for speed estimation. It is proposed to utilize the second available degree of freedom as a means for adapting the stator resistance online. The parallel stator resistance and rotor speed identification algorithm is developed in a systematic manner, using Popov's hyperstability theory. It increases the complexity of the overall control system insignificantly and enables correct speed estimation and stable drive operation at near-zero speeds. The proposed speed estimator with parallel stator resistance identification is at first verified by simulation. Extensive experimentation is conducted next at low speeds of rotation and successful stator resistance identification is achieved down to 0.5-Hz frequency of rotation.

Strategies for magnetizing speed-sensorless induction machines

Whenever possible, only easily measurable quantities like stator currents and DC link voltage are used to control inverter-fed induction motors nowadays. Also for high-performance AC drives speed-sensorless control of induction motors has received increasing attention from the viewpoint of cost, trouble immunity and reliability. Different algorithms for speed identification are known. They allow good performance of torque or speed control in almost the whole operating area of an induction machine without using rotational transducers. One remaining problem is to start the machine when mechanical speed is not known. This paper now presents two new strategies for magnetizing a demagnetized speed-sensorless induction machine also with non-zero speed.