Model Reference Adaptive System Estimator Research Articles

Online induction motor rotor time constant estimation using perturbation-based extremum seeking control

<p class="Abstract"><span lang="EN-US">A novel online induction motor (IM) rotor time constant estimator based on a perturbation-based extremum seeking control (ESC) and model reference adaptive system (MRAS) is proposed. Since implementations of fieldoriented control (FOC) requires accurate values of IM parameters, such as rotor time constant, so accurate and robust on-line estimations of such parameters are crucial for any modern industrial IM drives. The proposed MRAS estimator employs ESC method to estimate the IM rotor resistance in various operating conditions. Meanwhile, since extremum seeking control is a model-free scheme, so it is robust to other IM parameter variations. The feasibility and effectiveness of the IM rotor resistance estimation by utilizing ESC scheme has been verified by simulation and experimental results. A 2.2 kW experimental setup has been implemented. </span></p>

Encoderless Parallel Predictive Torque Control for Induction Machine Using a Robust Model Reference Adaptive System

This paper proposes a robust model reference adaptive system (MRAS) estimator incorporating online parameter identification algorithm for parallel predictive torque control (PPTC) scheme. In contrast to conventional predictive torque control (PTC), the proposed PPTC is designed to facilitate the determination of weighting factor that modifies the relative importance of the control objectives, by simultaneously evaluating the torque and flux tracking error terms. The weighting factor is fine-tuned via an adaptive selecting mechanism, to obtain the attractive feature of reduced torque and current tracking errors. Besides, an encoderless MRAS-based estimator is employed for rotor speed and stator flux estimation to reduce the hardware complexity. However, this proposed encoderless PPTC algorithm still suffers from weak robustness against inevitable disturbances caused by parameter variations, which is an important issue to be further investigated. In this paper, an encoderless MRAS estimator combined with online parameter identification algorithm is proposed as an effective solution to accurately estimate the rotor speed and predicted stator flux-linkage, that improves robustness against mismatched parameters for the proposed encoderless PPTC scheme. The feasibility of the proposed algorithm is verified through the obtained experimental measurements, that achieves precise speed tracking capability as well as reduced torque and current tracking errors.

Influence of Selected Non-Ideal Aspects on Active and Reactive Power MRAS for Stator and Rotor Resistance Estimation

Mathematical models of induction motor (IM) used in direct field-oriented control (DFOC) strategies are characterized by parametrization resulting from the IM equivalent circuit and model-type selection. The parameter inaccuracy causes DFOC detuning, which deteriorates the drive performance. Therefore, many methods for parameter adaptation were developed in the literature. One class of algorithms, popular due to their simplicity, includes estimators based on the model reference adaptive system (MRAS). Their main disadvantage is the dependence on other machines’ parameters. However, although typically not considered in the respective literature, there are other aspects that impair the performance of the MRAS estimators. These include, but are not limited to, the nonlinear phenomenon of iron losses, the effect of necessary discretization of the algorithms and selection of the sampling time, and the influence of the supply inverter nonlinear behavior. Therefore, this paper aims to study the effect of the above-mentioned negative aspects on the performance of selected MRAS estimators: active and reactive power MRAS for the stator and rotor resistance estimation. Furthermore, improved reduced-order models and MRAS estimators that consider the iron loss phenomenon are also presented to examine the iron loss influence. Another merit of this paper is that it shows clearly and in one place how DFOC, with the included effect of iron losses and inverter nonlinearities, can be modeled using simulation tools. The modeling of the IM and DFOC takes place in MATLAB/Simulink environment.

Comparative Analysis of Control Methods with Model Reference Adaptive System Estimators of a Seven-Phase Induction Motor with Encoder Failure

This article presents a comparative analysis of the direct torque control with space vector modulation method for seven-phase squirrel-cage induction motor during the encoder failure. The direct torque control with space vector modulation method with model reference adaptive system with current model and current estimator and model reference adaptive system—flux based estimator applied in the control loop of the motor speed have been analyzed. The mathematical model of the seven-phase induction motor and model reference adaptive system-type estimators have been described. The method of space vector modulation applied to the seven-phase voltage source inverter, controlled seven-phase induction motor, has been described. The operation of direct torque control with space vector modulation has been verified via simulation tests, and the results of performed simulation studies have been analyzed and presented. The authors’ original contribution and work novelty includes comparison studies of the performance of the direct torque control method with space vector modulation of a seven-phase induction motor with two estimators: model reference adaptive system—flux based estimator and the novel model reference adaptive system with current model and current estimator.

Stator Current-Based Model Reference Adaptive Control for Sensorless Speed Control of the Induction Motor

This paper described that the stator current-based model reference adaptive system (MRAS) speed estimator is used for the induction motor (IM) indirect vector speed control without a mechanical speed sensor. Due to high sensitivity of motor parameters variation at low speed including zero, stability analysis of MRAS design is performed to correct any mismatch parameters value in the MRAS performed to estimate the motor speed at these values. As a result, the IM sensorless control can operate over a wide range including zero speed. The performance of the stator current-based MRAS speed estimator was analyzed in terms of speed tracking capability, torque response quickness, low speed behavior, step response of drive with speed reversal, sensitivity to motor parameter uncertainty, and speed tracking ability in the regenerative mode. The system gives a good performance at no-load and loaded conditions with parameter variation. The stator current-based MRAS estimator sensorless speed control technique can make the hardware simple and improve the reliability of the motor without introducing a feedback sensor, and it becomes more important in the modern AC IM. The sensorless vector control operation has been verified by simulation on Matlab and experimentally using Texas Instruments HVMTRPFCKIT with TMS320 F28035 DSP card and 0.18 kw AC IM.

Sensorless Induction Motor Drive Based on Model Reference Adaptive System Scheme Utilising a Fictitious Resistance

Abstract This article presents a new development of an indirect stator flux-oriented controller for sensorless speed induction motor drive utilising instantaneous and steady-state values, respectively, of a fictitious resistance symbolised as R_f. The dimension of the fictitious quantity, in this context, is the ohm, which is the difference between the stator d- and q-axis fictitious resistances. However, from the measurement of the stator voltage and currents of the machine, two independent resistance estimators are built. Therefore, the first is considered as a reference model of the induction machine (IM), and the second is considered as an adjustable model. Subsequently, the error between the states of the two models is used to drive a suitable adaptation mechanism that generates the estimation of the speed, for the adjustable model. Furthermore, the structure of the proposed estimator is free from stator resistance and eliminates the requirement of any flux computation. All the detailed simulation study is carried out in MATLAB/Simulink to validate the proposed method and to highlight the robustness and the stability of the proposed model reference adaptive system estimator.

Estimation of the Induction Motor Stator and Rotor Resistance Using Active and Reactive Power Based Model Reference Adaptive System Estimator

In this paper an induction motor parameters estimator, based on the Model Reference Adaptive System (MRAS), is presented and described. A comprehensive literature study on MRAS type parameters estimators for induction motors is also provided. The authors propose a novel PQ-MRAS estimator concept which enables the simultaneous calculation of stator and rotor resistances in the induction machine, which is its major advantage over previous investigations. The estimator employs the active (P) and reactive (Q) power of the machine which is calculated by the only measurable signals, such as stator voltage and current. The paper includes a detailed description of the proposed estimator. The PQ-MRAS was tested for various operating conditions of a drive system with the induction motor. The results obtained from computer simulation tests were verified on the laboratory set up with a DS1202 card.

A Very-Low-Speed Sensorless Control Induction Motor Drive with Online Rotor Resistance Tuning by Using MRAS Scheme

Abstract A sensorless indirect stator-flux-oriented control (ISFOC) induction motor drive at very low frequencies is presented herein. The model reference adaptive system (MRAS) scheme is used to estimate the speed and the rotor resistance simultaneously. However, the error between the reference and the adjustable models, which are developed in the stationary stator reference frame, is used to drive a suitable adaptation mechanism that generates the estimates of speed and the rotor resistance from the stator voltage and the machine current measurements. The stator flux components in the stationary reference frame are estimated through a pure integration of the back electro-motive force (EMF) of the machine. When the machine is operated at low speed, the pure integration of the back EMF introduces an error in flux estimation which affects the performance torque and speed control. To overcome this problem, pure integration is replaced with a programmable cascaded low-pass filter (PCLPF). The stability analysis method of the MRAS estimator is verified in order to show the robustness of the rotor resistance variations. Experimental results are presented to prove the effectiveness and validity of the proposed scheme of sensorless ISFOC induction motor drive.

A review on MRAS-type speed estimators for reliable and efficient induction motor drives

Induction machines have recently been very popular in variable-speed drives, because of their robust construction and relatively low manufacturing costs (brushless), maintenance-free and well-matured control methods. However, for high-precision control and efficiency optimization one needs the information on the rotor speed which can be measured using different speed sensors. All sensors require a mounting space and cabling, they also generate extra costs and reduce system reliability. Therefore, many of the recent research efforts have been dedicated to sensorless or encoderless electrical drives offering such considerable advantages as: lower cost, reduced size and hardware complexity of the drive system, elimination of sensor cables, lower maintenance requirements, possible operation in aggressive environment, higher noise immunity, reliable and user friendly operation. In this article all well-known sensorless techniques are shortly addressed, but the main focus is on the solutions based on the Model Reference Adaptive System (MRAS) concept. The mathematical models and schemes of all types of MRAS-type speed estimators known from the literature are gathered in this article. The comparative analysis of these speed estimators is done from the following points of view: the speed adaptation mechanism derivation based on the Lyapunov theory, stability problems near zero speed and in the regenerating operation mode, and the sensitivity of MRAS estimators to induction machine parameter changes.

Model reference adaptive system based sensorless speed estimation of brushless doubly‐fed reluctance generator for wind power application

Brushless doubly-fed reluctance generator (BDFRG) is drawing a growing attention of research over the traditional doubly-fed induction generator in variable speed application like wind power generation nowadays, due to its brushless structure. In this work, the applicability of different model reference adaptive system (MRAS) based sensorless speed estimation techniques for BDFRG in wind power application is explored. The MRAS estimators developed, in this context, are based on active power, reactive power and fictitious quantity. All these schemes are implemented under primary field orientation using machine secondary winding quantities as the functional variables. Matlab/Simulink study and hardware test results under variable wind speed (between cut-in and rated speed) are presented for the aforementioned speed estimation techniques. Furthermore, to ensure the robustness of the drive system, stability study using Popov's criteria for hyperstability and sensitivity analyses is presented.

Electromagnetic Torque and Speed Estimators for Permanent Magnet Synchronous Motor Drive Systems

This paper introduces a new adaptive speed estimator based sensorless control algorithm for speed control of Permanent Magnet Synchronous Motor (PMSM). The algorithm is based on Field Oriented Control (FOC) theory. In this control algorithm, the rotor speed and position feedbacks are obtained using a model reference adaptive system estimator. Also a load torque estimator is adapted to improve the control algorithm performance in speed control. Maximum Torque per Amper (MTPA) control strategy is employed in the proposed algorithm forthe speeds lower than base speed. Both the proposed control and conventional control structures are simulated usingMatlab/Simulink. Simulation results showed the some advantages of the proposed control algorithm compared to the conventional its.

Speed Estimators Using Stator Resistance Adaptation for Sensorless Induction Motor Drive

The paper describes speed estimators for a speed sensorless induction motor drive with the direct torque and flux control. However, the accuracy of the direct torque control depends on the correct information of the stator resistance, because its value varies with working conditions of the induction motor. Hence, a stator resistance adaptation is necessary. Two techniques were developed for solving this problem: model reference adaptive system based scheme and artificial neural network based scheme. At first, the sensorless control structures of the induction motor drive were implemented in Matlab-Simulink environment. Then, a comparison is done by evaluating the rotor speed difference. The simulation results confirm that speed estimators and adaptation techniques are simple to simulate and experiment. By comparison of both speed estimators and both adaptation techniques, the current based model reference adaptive system estimator with the artificial neural network based adaptation technique gives higher accuracy of the speed estimation.

Predictive direct power control for three-phase grid-connected converters with online parameter identification

Summary It is well known that predictive direct power control method can be influenced by the presence of modeling errors. Any small variations in model parameters will degrade the performance and stability of the control system. However, this value is difficult to be measured precisely in practical implementation. This paper proposes a predictive direct power control method for three-phase grid-connected converters with online parameter identification technique. Specifically, the proposed method alleviates the parameter mismatch problem by using the online parameter identification technique based on model reference adaptive system estimation theory. The proposed strategy not only identifies the parameter accurately, but also improves dynamic response without any additional sensors. The excellent steady-state and dynamic performance of the proposed method are confirmed through extensive simulations. Copyright © 2016 John Wiley & Sons, Ltd.

Model Predictive MRAS Estimator for Sensorless Induction Motor Drives

This paper presents a novel predictive model reference adaptive system (MRAS) speed estimator for sensorless induction motor (IM) drives applications. The proposed estimator is based on the finite control set-model predictive control (FCS-MPC) principle. The rotor position is calculated using a search-based optimization algorithm which ensures a minimum speed tuning error signal at each sampling period. This eliminates the need for a proportional–integral (PI) controller which is conventionally employed in the adaption mechanism of MRAS estimators. Extensive experimental tests have been carried out to evaluate the performance of the proposed estimator using a 2.2-kW IM with a field-oriented control (FOC) scheme employed as the motor control strategy. Experimental results show improved performance of the MRAS scheme in both open- and closed-loop sensorless modes of operation at low speeds and with different loading conditions including regeneration. The proposed scheme also improves the system robustness against motor parameter variations and increases the maximum bandwidth of the speed loop controller.

MRAS speed estimator with fuzzy and PI stator resistance adaptation for sensorless induction motor drives using RT-lab

Summary This paper presents a real-time simulation study of Model Reference Adaptive System based rotor speed estimator with parallel stator resistance adaptation mechanism for speed sensorless induction motor drive. Both, the traditional Proportional Integral and Fuzzy logic based control mechanisms are utilised for stator resistance adaptation, while, the rotor speed is estimated parallely by means of Proportional Integral based mechanism. The estimator's response to dynamic changes in Load perturbation and doubling of the nominal value of the actual stator resistance of the motor is observed. The superiority of the fuzzy based stator resistance adaptation in the Model Reference Adaptive System estimator is proved through results validated in real-time. The purpose of employing a fairly new real-time platform is to reduce the test and prototype time. The model is initially built using Matlab/Simulink blocksets and the results are validated in real time using RT-Lab. The RT-lab blocksets are integrated into the Simulink model and then executed in real-time using the OP-4500 target developed by Opal-RT. The real-time simulation results are observed in the workstation.

MRAS state estimator for speed sensorless ISFOC induction motor drives with Luenberger load torque estimation

This paper presents a novel method for estimating the load torque of a sensorless indirect stator flux oriented controlled (ISFOC) induction motor drive based on the model reference adaptive system (MRAS) scheme. As a matter of fact, this method is meant to inter-connect a speed estimator with the load torque observer. For this purpose, a MRAS has been applied to estimate the rotor speed with tuned load torque in order to obtain a high performance ISFOC induction motor drive. The reference and adjustable models, developed in the stationary stator reference frame, are used in the MRAS scheme in an attempt to estimate the speed of the measured terminal voltages and currents. The load torque is estimated by means of a Luenberger observer defined throughout the mechanical equation. Every observer state matrix depends on the mechanical characteristics of the machine taking into account the vicious friction coefficient and inertia moment. Accordingly, some simulation results are presented to validate the proposed method and to highlight the influence of the variation of the inertia moment and the friction coefficient on the speed and the estimated load torque. The experimental results, concerning to the sensorless speed with a load torque estimation, are elaborated in order to validate the effectiveness of the proposed method. The complete sensorless ISFOC with load torque estimation is successfully implemented in real time using a digital signal processor board DSpace DS1104 for a laboratory 3kW induction motor.

Design of a MRAS, Speed and Flux Estimators used in Sensorless Control of Induction Motors

Model reference adaptive system (MRAS) based techniques are one of the best methods to estimate the rotor speed. Speed and torque control of an induction motor is usually attained by application of a speed or position sensor. However, these require the additional mounting space, reduce the reliability and increase the cost of the motor. The recent trend in field oriented control (FOC) is towards the use of sensorless techniques that avert the use of speed sensor and flux sensor. This paper seeks to provide a direct comparison between two technics of induction motor MRAS based speed estimators, one based on the rotor flux and the other based on the back EMF. Comparison between the two techniques is made through computer simulations.

A MRAS-based stator resistance and speed estimation for sensorless vector controlled IPMSM drive

In this paper, model reference adaptive system (MRAS) technique has been used for speed estimation in sensorless speed control of interior permanent magnet synchronous motor (IPMSM) with space vector pulse width modulation (SVPWM). Most of the current researches studying the MRAS technique are based on non-saliency PMSM model and ignore the difference between d- and q-axes inductances. This paper studies a novel MRAS observer, considering the saliency of PMSM. However, this sensorless speed control shows great sensitivity to stator resistance and system noise, particularly, during low-speed operation. A novel stator resistance estimator is incorporated into the sensorless drive to compensate the effects of stator resistance variation. A stability-analysis method of this novel MRAS estimator is shown. Stable and efficient estimation of rotor speed at low region will be guaranteed by simultaneous identification of IPMSM. The stability of proposed stator resistance estimator is checked through Popov's hyperstability theorem. The proposed observer is experimentally tested using a 1.1-kW motor drive; stable operation at very low speeds under different loading conditions is demonstrated. The real time implementation concept on the digital signal processor (DSP) board of the VFOC design and SVPWM inverter technique are illustrated. Results show that the proposed MRAS technique can satisfactorily estimate the position and speed of PMSM with saliency.

Stator-Current-Based MRAS Estimator for a Wide Range Speed-Sensorless Induction-Motor Drive

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper deals with an analysis of the vector-controlled induction-motor (IM) drive with a novel model reference adaptive system (MRAS)-type rotor speed estimator. A stability-analysis method of this novel MRAS estimator is shown. The influence of equivalent-circuit parameter changes of the IM on the pole placement of the estimator transfer function and the stability of the whole drive system are analyzed and tested. The influence of the adaptation-algorithm coefficients of the MRAS-estimator scheme is also tested. The allowable range of motor-parameter changes is determined, which guarantees the stable operation of the sensorless field-oriented IM drive with this speed and flux estimator. Dynamical performances of the vector-control system with the current-type MRAS estimator are tested in a laboratory setup. </para>

Speed-Sensorless AC Drives With the Rotor Time Constant Parameter Update

This paper presents a new technique for online identification of an induction motor rotor time constant. The technique is designed for a shaft-sensorless indirect field-oriented control induction motor drive with a model reference adaptive system (MRAS)-based speed estimator. The MRAS estimator is sensitive to the changes in the rotor time constant, and online identification of that parameter is essential. If rotor parameter error exists, it does not only change the achieved rotor speed, but it also changes the dynamic behavior of the whole field control and speed estimation structure. The proposed rotor parameter update is exactly based on the newly introduced dynamic model of the potentially detuned MRAS-based speed estimator. The technique avoids the use of test signals and rather extracts the needed information from the ever-present signal jitter, which is inherent to the current and speed servo loops. This paper demonstrates that the phase angle difference between some spectral components of selected small signals within the speed estimator can be used for rotor parameter update. Computer simulations and experiments are performed under a variety of conditions to validate the effectiveness of the proposed rotor parameter update technique.