Search

Indirect Field Oriented Control (IFOC) can provide an excellent dynamic response in Three Phase Induction Motor (TIM) drives and is widely used in the industry. Still, as a considerable part of the electrical energy is used to drive motors, intense research has been carried out to reduce the energy consumption of the induction motor drives and improve their efficiency. In this paper, a high-efficiency Smooth-IFOC (SIFOC) scheme is proposed and implemented experimentally with improved current controllers and the Loss Model Control (LMC) technique, which achieves up to 41% reduction in energy consumption compared to the traditional IFOC approach. The main contribution is the new Adaptive LMC (ALMC) control scheme, which includes two recursive adaptation algorithms to minimize the LMC technique sensitivity to the variation of the motor parameters. The algorithms are derived considering MRAS concept and both active (P) and reactive (Q) power to estimate the stator and rotor bandwidths, which are used to update the ALMC controller. The SIFOC-ALMC approach is verified experimentally, with up to 7.2% energy savings compared to SIFOC-LMC. Moreover, since the traditional IFOC controller is sensitive to rotor bandwidth variation, estimating this parameter improves the robustness of the SIFOC drive system.

This paper proposes an algorithm correcting the field orientation inaccuracy caused by resolver periodic error and rotor time constant variation. It is found that the periodic error brings harmonic current vectors and torque ripples while the rotor time constant variation makes dq currents deviate from the reference values. The crossproduct of fundamental and harmonic current vectors contains the information of resolver periodic error. The gradient descent method with a variable step is introduced to compensate the periodic error. During the process, the crossproduct is minimized. A new form of cross-product calculation is derived to reduce the computation time. The rotor time constant is identified by the reactive-power based model reference adaptive system (MRAS). Voltages and currents in the stationary reference frame are used in the reference model to avoid the influence of resolver periodic error. Appropriate parameters of the MRAS are selected to guarantee the stability. The algorithm is integrated with an induction motor (IM) drive which is based on indirect field-oriented control (IFOC). The feasibility of the proposed algorithm is verified by simulation results and experiments.

Accurate rotor time constant ( $T_{r}$ ) value is necessary to ensure an acceptable performance of the indirect field-oriented control (IFOC) strategy, where a detuned $T_{r}$ affects rotor flux orientation and, in turn, results in poor dynamic and steady-state response of torque. The problems associated with detuned $T_{r}$ will be exacerbated in sensorless drives when a model-based speed estimation algorithm is employed. This article investigates the problem of simultaneous identification of the rotor time constant and rotor speed for sensorless IFOC of six-phase induction motor (6PIM) drives. First, an adaptive observer is proposed for online detection of the low-frequency sinusoidal signal, which is intentionally injected into the rotor flux command. Then, a novel model reference adaptive system (MRAS)-based $T_{r}$ estimator is proposed using the detected signal. The Lyapunov stability theorem is used to ensure the asymptotic stability of the proposed identification system. The proposed method is based on the induction machine (IM) model in primary subspace, i.e., $\alpha $ – $\beta $ subspace. Hence, it is usable for other multiphase IM (three phases and higher). Nevertheless, the 6PIM is adopted here as a case study. The simulation and experimental results clarify the effectiveness of the proposed parallel estimation system.

To handle the suspended rotor speed self-identification problem of a bearingless induction motor (BL-IM), a sensorless control strategy based on fuzzy PI fractional model reference adaptive system ...

This paper presents MRAS speed sensorless control of induction motor using type-2 fuzzy logic controller (T2FLC). These controllers replace the PI ones, in the new MRAS strategy proposed in Benlaloui et al. (IEEE Trans Energy Convers 30(2):588–595, 2015), in order to improve the induction motor performances and robustness at low speed region. Indeed, the choice of these controllers is made because of their adaptation-based schemes which permit to handle nonlinear uncertain systems without the need of precise mathematical model required when using PI controllers. Comparative study had shown a better rejection of disturbance and high insensitivity to stator resistance compared to the PI and the T1FLC controllers. The effectiveness of the proposed speed-based T2FLC estimation method and its good robustness are validated by simulation and by experimental results.

In this paper, a sliding mode observer (SMO) for the speed-sensorless direct torque vector control of linear induction motor(LIM) is proposed, considering the influence of dynamic end effects. The proposed SMO estimate the motor speed, the rotor flux, the angular position of the rotor flux, and the speed dependent parameter $w$ , only by using the measured stator voltages and stator currents. The nonlinear sliding mode technique guarantees very good performance for different situations of motor speed. The proposed SMO does not use complicated observer gains and robust to the parameter variation over the full-speed range. Moreover, the convergence of SMO is proven by using the Lyapunov theory. Furthermore, LIM Drive state observability is presented with detailed analysis. The performance of the proposed SMO is verified by hardware-in-the-loop test (HIL) system. Furthermore, a comparative study with and Luenberger observer(LO) and model reference adaptive system(MRAS) is presented.

This article proposes a rotor temperature estimation method for in-service induction machine (IM) based on parameter identification, which combines the advantage of recursive least squares (RLS) and model reference adaptive system (MRAS). The RLS with forgetting factor is firstly adopted to identify the parameters of motor inductances. Then, the online identification of rotor resistance can be realized via the MRAS based on the instantaneous reactive power (IRP) and the proportional integral (PI) regulation adaptive law designed by the Popov hyper-stable theory. Thereby, the rotor temperature is computed by the resistance-temperature relationship of metals. To achieve a fast convergence of the parameter identification, rotor slot harmonics are extracted from the stator current and used to determine the rotor speed in real time. Furthermore, to obtain a more accurate initial value of the stator and rotor leakage inductances and resistances, the first 5–15 cycles of the IM starting process are used to mimic the locked-rotor test condition. A merit of the proposed method is that it requires significantly less time to estimate the rotor resistance than the traditional methods. With a novel test bench, experimental validation is performed on a 22-kW IM. In the test, the rotor temperature is measured in three different ways: 1) wireless sensors inserted in the rotating rotor core and rotor end rings, 2) infrared sensor for rotor end ring temperature, and 3) PT100 installed in stator end winding. With this test bench, the real rotor temperature was revealed, and the effectiveness of the presented method is also verified.

High-performance field-oriented motor control requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. So far, Speed sensorless control of induction motors has been faced with various techniques of speed estimation. However, the main drawbacks of these models are their insufficient performance at low speeds, along with sensitivity to parameters variation, which engenders an unsteady drive system. This paper proposes an effective sensorless indirect Field Oriented control scheme for an induction motor drive. The proposed speed observer combines artificial intelligence and model reference adaptive system (MRAS). This observer is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the conventional MRAS performance especially in low-speed regions and reduce its sensitivity to noise and system uncertainties as well. Otherwise, the effectiveness of the proposed speed estimator is tested in simulation using Matlab/Simulink software environment. The control structure is checked in the low speed with load variation.

In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy.

This article presents an improved direct torque control strategy based on proportional-integral controllers applied to an asynchronous cage motor. The main objective of this study is to improve the system performance by reducing the electromagnetic torque and stator flux ripples while adjusting the currents shape by optimizing the total harmonic distortion, and this by supplying the motor with a diode clamped five-level inverter using the phase opposition disposition technique. Furthermore, the use of an observer employing the stator flux based model reference adaptive system allows improving the control performance by using a sensorless algorithm to obtain an accurate estimation, therefore, increase the system reliability. A comparative study between the five-level inverter installation with the same system and the same control strategy but another two different topologies of inverters; such as the NPC three-level inverter and the conventional two-level inverter has been made under MATLAB/SIMULINK to validate the efficiency of the proposed installation under various tracking and robustness tests.

This article presents an efficient method for control of a solar water pumping system consisting of an induction motor drive (IMD) with a model reference adaptive system (MRAS) based adaptive mechanism of speed estimation. An improved perturb and observe control technique is implemented to achieve maximum power point tracking. The gating signals are produced by utilizing a space vector pulsewidth modulation scheme for a three-phase inverter. An improved third order integrator (ITOI) is proposed for adaptation of flux for improvement in the performance of the drive. The MRAS is used for estimation of slip speed and synchronous speed two estimated speed signals are used to generate the motor speed. The proposed control methodology with the MRAS-based reactive power technique for the estimation of speed has improved the system stability for a wide range of drive speed. This system with a single-stage photovoltaic (PV) topology and switching logic obtained from space vector modulation operating three-phase voltage source inverter fed IMD with a pump as a load is designed and implemented in MATLAB/Simulink. The suitability of the developed system is validated through experimentation in the laboratory.

This paper uses a stator current error-based model reference adaptive system (MRAS) to estimate the speed of an induction motor drive. Usually, speed error signal uses the cross product of stator current error vector and flux linkage vector for the estimation of speed. However, this speed error signal has limitations, such as speed tracking error and instability (at regenerative mode) under loaded conditions at low speeds. To mitigate these issues, a modified MRAS speed estimation algorithm is proposed in this paper. In the proposed method, the adaptive model state variables are formulated in terms of flux linkages (stator and rotor) in order to alleviate the impact of current noise in the flux linkage estimation. Moreover, a compensator is added to reduce the speed error tracking in the reference model. Furthermore, the stability of the drive is kept by using the dot product of stator current error vector and the rotor flux linkage vector during the regenerative mode of operation. The analysis of the proposed algorithm is presented and validated experimentally on the laboratory setup with direct torque control-space vector modulation technique for a wide operating range including the dynamic performance.

In this paper, a multi-leg based multi-drive configuration is presented to be used in multi AC drive applications such as the hot rolling mills. The proposed configuration enjoys compactness, fewer semiconductors, and lower drive cost compared with conventional topology, making it a promising approach. In a conventional rolling mill stand, an active front-end (AFE) rectifier and two inverters are required for grid-side and motor-side connections. However, in the proposed configuration, all converters are unified using a multi-leg structure. To improve the operational performance of the drive, a model predictive control (MPC) is designed. In this approach, an individual cost function for every output, along with a comprehensive cost function containing all control objectives for the overall system, is defined. By obtaining the valid and invalid switching states of the proposed converter, the defined multi-objective cost function is minimized to find the most optimum switching states in each sampling time. In addition, to improve the robustness of the proposed multi-drive system, a model reference adaptive system (MRAS) is designed to estimate rotor speed and stator resistance. The high-performance capability of the proposed multi-drive system is evaluated in both steady and dynamic states using MATLAB/Simulink software and compared with a conventional configuration. According to simulation results, it is deduced that the independent control of the top and bottom motors in a rolling mill stand can be guaranteed using the proposed low-cost sensor-less drive.

The magnetizing inductance in linear induction motor (LIM) is heavily influenced by the dynamic end effect. Considering that the accuracy of magnetizing inductance usually plays an important role in control system, an online magnetizing inductance estimation strategy for LIM is proposed in this paper. It is based on the back electromotive force (EMF) model reference adaptive system (MRAS) and second-order sliding-mode supertwisting algorithm (STA). Compared to the conventional flux based MRAS identification strategy, there is no pure integration and differential operation in both reference and adaptive models for the proposed method so that integral initial values, DC bias and high-frequency-noise amplification problems can be solved. Stability analysis is given by using the Popov's criterion for hyperstability. Comprehensive simulation and experimental results are given to verify the effectiveness.

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.

Squirrel-cage induction machines (IMs) with indirect field-oriented control are widely used in industry and are frequently chosen for their accurate and dynamic torque control. During operation, however, temperature rises leading to changes in machine parameters. The rotor resistance, in particular, alters, affecting the accuracy of the torque control. The authors investigated the effect of a rotor resistance parameter mismatch in the control algorithm on the angular rotor flux misalignment and the subsequent deviation of stator currents and motor torque from their setpoints. Hence, an online, data-driven torque compensation to eliminate the temperature effect is proposed to enable robust torque-controlled IMs. A model-based analysis and experimental mapping of the temperature effect on motor torque is presented. A temperature-torque lookup-table is subsequently implemented within the control algorithm demonstrating the ability to reduce the detrimental effect of temperature on torque control. Experimental results on a 5.5 kW squirrel-cage induction motor show that the proposed data-driven online temperature compensation method is able to reduce torque mismatch when compared to having no temperature compensation. Up to 17% torque mismatch is reduced at nominal torque and even up to 23% at torque setpoints that are lower than 20% of the nominal torque. A limited torque error of

Dot product of stator current and rotor flux can be chosen to form a model reference adaptive system (MRAS) to update the rotor time constant online in indirect-vector-controlled induction motor drives. Such a functional candidate is attractive due to the fact that its steady-state value is immune to stator resistance variation and dead-time effect. However, the stability of the estimation system is still not clarified. In this paper, the small-signal model of the whole system is established, paving a way to explore its stability and improve its performance further. It is clearly shown that both inappropriate selection of stator resistance and adaptation gain in the MRAS would result in instability phenomenon when the motor is operated at low-speed regenerating mode. In order to address this problem and hence guarantee the stable operation, the stator resistance used in the rotor flux estimator is designed to vary according to the signs of the $q$ -axis commanded current and stator frequency. Besides, the adaptation gain in the MRAS is designed based on the stability analysis using the root-locus method. The validity of the proposed theory is demonstrated through simulations and experiments.

This paper proposes a new model reference adaptive system (MRAS) for the sensorless speed estimation of field-oriented induction motor (IM) drive utilizing d - and q -circuits effective impedances. The functional quantity of the MRAS, in this context, is the difference between the stator d - and q -circuits' effective working impedances. This unique structure does not require any flux computation while estimating speed. Moreover, the formulation is free from any resistance terms. The proposed estimator performs satisfactorily at all the operating points and exhibits stability in all the four quadrants of the drive's operation. The relevant stability and sensitivity studies, in this regard, are done in MATLAB/Simulink. Furthermore, the controller performance is validated experimentally by dSPACE-1103-based IM drive setup.

This paper proposes a fuzzy logic control for model reference adaptive system (MRAS)-based speed adaptation technique of an induction motor drive (IMD) used for the bidirectional power flow between a photovoltaic (PV) array and a single-phase utility grid on the same DC link for water pumping. This system comprises of one current sensor and one voltage sensor for sensing grid quantities and one current sensor and one voltage sensor for sensing the DC link current and its voltage. The DC link current is used to estimate three-phase motor winding currents. Phase current is reconstructed from DC link current through a space vector modulation technique. The mechanical sensorless control of the proposed system is achieved by stator voltage and current-based speed adaptation. The direct torque control (DTC) is used for an IMD. The generalised integrator-based fundamental voltage generation algorithm is proposed for power flow control between the single-phase utility grid and DC link voltage feeding an induction motor-driven water pump. The developed prototype is tested in the laboratory and its suitability is judged through various results under different conditions of insolation variations.

Model reference adaptive system(MRAS) based techniques are one of the best method to estimate the rotor speed due to its performance and straight forward stability approach.These techniques use two different models which have made the speed estimation a reliable scheme especially when the variations.The scheme use the stator equation and rotor equation as the reference model and the adjustable model respectively.The output error from both models is tuned using a PI controller yielding the estimated rotor speed.It presents the identification and parameter estimation of an induction motor model with parameters varying as functions of the operating conditions. A Sensorless torque control system for induction motors is developed. The system allows for fast and precise torque tracking over a wide range of speed.The induction motor is controlled through field orientation techniques that require knowledge of the rotor speed.Since speed sensors decrease the reliability of a drive system (and increase its price), a common trend in motor control is to use an observer to estimate speed.

PurposeThe major disadvantage of the field-oriented control (FOC) scheme of induction motors is its dependency on motor parameter variations because of the temperature rise. Among the motor parameters, rotor resistance is a parameter that can degrade the robustness of FOC scheme. An inaccurate setting of the rotor resistance in the slip frequency may result in undesirable cross coupling and performance degradation. To overcome this disadvantage, the purpose of this paper is to propose a model reference adaptive system (MRAS) rotor time constant tuning to improve the induction motor drive performance and to compensate the flux orientation error in vector control law.Design/methodology/approachFirst, the dynamic model and the indirect field-oriented control of induction motor are derived. Then, an inverse rotor time constant tuning is proposed based on MRAS theory where a new adaptation signal formulation is used as reference model, and the estimated stator currents obtained from induction motors (IM) state space resolution is used in the adaptive model.FindingsThe effectiveness and robustness of IM speed control with the proposed MRAS inverse rotor time constant estimator is verified through MATrix LABoratory/Simulink model simulation and laboratory experimental results. The simulation and experimental results show good transient drive performances, satisfactory for rotor resistance estimation and robustness with regard to uncertainties and load torque disturbance.Originality/valueThis paper presents an online tuning of the inverse rotor time constant using a new adaptation signal MRAS model. The proposed estimator is proved to guarantee the stability for different operating conditions, especially in very low/zero speed region and heavy load torque. The stability analysis of the proposed estimation procedure is also demonstrated.

This paper deals with a model reference adaptive system (MRAS) estimator based on the second-order sliding-mode (SOSM) strategy for speed-sensorless direct torque control (DTC) of a six-phase induction motor. A compensated flux observer is proposed using the well-known supertwisting algorithm, which is inherently an SOSM technique. This observer is adopted as the reference model in the MRAS-based speed estimator to overcome the chattering problem of the classical sliding-mode techniques. The estimator is robust against parameter uncertainties and dc offsets. In addition, a supertwisting-algorithm-based second-order sliding-mode (ST-SOSM) controller is designed for the speed loop in the DTC strategy to increase its robustness under applying external load disturbances. The speed control loop needs the load torque signal. For this purpose, an ST-SOSM load torque observer is proposed, where it provides a robust performance without the penalty of high chattering. Simulation and experimental results confirm the validity and the effectiveness of the proposed approaches.

A smoothed Kalman filter trained recurrent neural network is proposed as an observer for sensorless vector control of three phase induction motor. Recurrent neural networks which are capable of online training is used here. The speed and flux are estimated using this observer for closed loop vector control. The proposed observer shows good performance in transient and steady state and also in variations in load and speed. Simulations are performed on a 0.75HP induction motor drive and results are compared with those of an extended Kalman filter trained recurrent neural observer.

This paper presents a advanced field-oriented control (FOC) for an open-end stator windings six phase induction motor (SPIM-OESW) with fuzzy logic (FL) speed controller. The proposed motor topology is fed by four, three-phase voltage inverters with two isolated DC sources. The FOC technique requires a precise knowledge of some machine's variables such as rotor position and rotor flux which are difficult to measure. Usually, the rotor position and motor speed are measured by a shaft-mounted encoder, but that makes the maintenance activities more difficult, and reduces system's reliability. Motor flux measurement is more complicated and requires machine's built in sensors. Therefore, a rotor flux-based model reference adaptive system (RF-MRAS) is proposed in this paper for the estimation of both rotor flux and rotor speed. In addition, the classic integral proportional (PI) controller is replaced by a FL controller to achieve better speed response at different operating points. The proposed control scheme is built within the Simulink environment combined with the Real-Time platform. Real time results are obtained to illustrate the effectiveness of the proposed control under different operating conditions.

This paper presents a new technique of implementing the back-EMF MRAS speed observer used in sensorless vector control systems with induction motors. The adjustable model of the speed observer is made on the basis of the estimated d-q components of the back electromotive forces (Back-EMF) using in this sense the afferent components the space vector of the rotor flux, obtained on the basis of the reduced order Gopinath observer. The testing and evaluation of the dynamic performances of the speed and rotor flux observers are by numerical simulation in Matlab-Simulink performed.

This paper presents a new approach in speed and rotor flux estimation for induction motors. In this sense, the rotor flux is estimated with the help of a new full-order state observer, and the speed is estimated with the help of a back-emf MRAS observer. For the proposed observer its dynamic performances and stability are analyzed. The paper also presents the value range of the proportionality coefficient in the component of the observer gain matrix. The validity of the observer are proved by numerical simulation in Matlab-Simulink.

This paper proposes the application of the NUIO inside a FOC induction motor drive for the simultaneous estimation of the load torque and the rotor speed. The idea is to estimate at first the speed with the current model in parallel with a reference model developed on the basis of the voltage model of the induction machine. Then, the estimated speed is given as input to a nonlinear unknown input observer (NUIO) to estimate the load torque. This estimation is then used to correct the previous estimation of the speed. Simulation and experimental results confirm the goodness of the method for an extended range of speed and different load torque, and they confirm the reduction of error in transient and steady state with respect to a simple MRAS scheme without NUIO.

This paper deals with the fields programmable gate array (FPGA) implementation of a sensorless rotor flux oriented control (RFOC) in a reduced delta inverter fed induction motor drive. In fact, this work represents the cost-effectiveness and the compactness of the electric motor drive control within the consideration of two major reductions: (i) the half reduction of the inverter feeding the electric motor when the reduced delta inverter is used, (ii) the elimination of the speed encoder which allows the decrease of the cost and the volume of the system control. The sensorless RFOC strategy is considered in this paper with a speed estimation based on the application of the MRAS algorithm. The implementation of this strategy on the FPGA virtex 5 board is made using the Xilinx system generator programming tools (XSG). To execute this implementation, a hardware co-simulation test is performed. The performances of this test are approved with the validity of the good agreement between the co-simulation and the XSG simulation results.

This paper presents a different technique for the online stator resistance estimation using a particle swarm optimization (PSO) based algorithm for rotor flux oriented control schemes of induction motor drives without a rotor speed sensor. First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts. Finally, a novel method for the stator resistance estimation based on the PSO algorithm is presented for the two MRAS-type observers. Simulation results in the Matlab/Simulink environment show good adaptability of the proposed estimation model while the stator resistance is varied to 200% of the nominal value. The results also confirm more accurate stator resistance and rotor speed estimation in comparison with the conventional technique.

This paper uses model predictive control that is superior technique to handle sensorless speed control of induction motor driven by a solar system. The proposed system, solar photovoltaic (PV) array connected with voltage source inverter (VSI) and predictive control system, is MRAS. A MPPT algorithm based on perturbation and observation is used for harnessing the full power from a PV series. System predictive control strategy is a predictive control approach to standard optimization. For less dependency on model parameters, MPC can achieve sufficient control efficiency and has been widely used in process control systems. MRAS speed estimation method was implemented to achieve sensorless operation to boost the device reliability and cost reduction of hardware. In MATLAB/Simulink environment, the desired configuration is modeled and simulated.