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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.

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.

The main contribution of this study is to propose a new rotor position observer with the approach of model reference adaptive system (MRAS) associated with the quantity of control winding (CW) power factor of the brushless doubly-fed induction generator (BDFIG) for sensorless voltage control target in stand-alone systems. The proposed observer relies on the field-orientation scheme of power winding (PW) side. The proposed sensorless methodology does not need any computation process with integration for obtaining the flux components. In addition, the new methodology can reduce the voltage sensors required for detecting the actual CW voltage quantities to be used in the calculations process of the proposed MRAS procedure by directly realizing the CW reference stationary voltages from the input path of SVPWM. This would ensure the simplicity of the suggested procedure for detecting the rotational position of the generator. The stability confirmation of the presented estimation method is analyzed and realized with the aid of technique of hyperstability concept and aided with the criteria of Popov's analysis. Moreover, the verification of robustness for the proposed observer is studied via uncertainty issues of machine parameters. Comprehensive simulation and experimental results are carried out to verify this method

To realize the demagnetization fault diagnosis and degree estimation of permanent-magnet synchronous linear motors in linear motion applications with high precision and multiple working conditions, a novel double closed-loop control with an adaptive linear neuron (ALN) model reference adaptive system (MRAS) flux linkage observer is proposed in this article. First, a sliding-mode control (SMC) is introduced to design the velocity loop to obtain good speed adaptability. Then, the MRAS flux observer is established, and the ALN algorithm for modifying the MRAS adaptive law online is designed combining with MRAS flux observer. In addition, the finite element models of normal, weak (F1), mild (F2), moderate (F3), and severe (F4) demagnetization faults are established, and the accurate values of flux linkage under different demagnetization degrees are simulated. Furthermore, the demagnetization fault diagnosis algorithm is introduced with an SMC-ALN-MRAS flux observer. Finally, a prototype motor experimental platform is built, and comparative experiments are designed to confirm the correctness and effectiveness of the proposed method.

In this article, the performance of multilevel inverter-fed position sensorless permanent magnet synchronous motor (PMSM) drive is analyzed using modified model predictive torque control (MPTC) met...

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.

This paper presents brain emotional controller (BEC)-based adaptive mechanism in Model Reference Adaptive System (MRAS) to control sensorless permanent magnet synchronous motor (PMSM) drive. BEC is a bio-inspired intelligent controller developed using neurobiological connections of mammalian brain by inspiring limbic system. The developed controller is applied as speed controller and parameter estimation (i.e. rotor speed and rotor position) of PMSM drive. The proposed BEC stability is proved to guarantee convergence characteristics. The performance of BEC-based adaptive mechanism is evaluated in simulation environment, and obtained results are compared in real time in hardware-in-loop (HIL) environment. In order to hold the effective performance of the proposed method compared with fuzzy logic controller with similar tests, further integral performances are evaluated for BEC and fuzzy logic controllers. Results demonstrated in HIL and in simulation show applicability, robust and sensitiveness of the proposed BEC method.

The proposed technique focuses on the online estimations of multiplane rotor resistances for the multiphase induction machine operating under the flux-oriented frame. The deviations in regard to the steady-state performance caused by the mismatched fundamental and harmonic rotor resistances are analyzed in detail. The reactive-power-based model referenced adaptation system (MRAS) is formulated for the third harmonic plane, which is air gap flux-oriented. The quasi-normalized method with respect to the error terms between the referenced and adjustable models is addressed to improve the adaptive mechanism under various load conditions. Experimental validations have been performed to confirm the effectiveness of the proposed algorithm. The simultaneous online identifications for the fundamental and harmonic rotor resistances are achieved. The approximate convergence processes and steady performance can be obtained for a large range of load conditions by using constant adaptive gains. The estimated results are indirectly verified by the comparisons between command and actual flux. In addition, the dynamic performance for the proposed MRAS is conducted with the sudden change of load. The effects of inverter nonlinearity and temperature rise are taken into account.

This paper introduces a Fractional Order Field Oriented Control (FO-FOC) with sensorless Model Reference Adaptive System (MRAS) for the Permanent Magnet Synchronous Motor (PMSM). In this work, the conventional integer controllers of the vector control method are replaced with the Fractional Order PI (FOPIα) controllers. Particle Swarm Optimization technique (PSO) is employed to tune the gain, integration parameter and the fractional order parameter for each of the speed, current and MRAS controllers to get the optimum values. The presented methodology is tested using MATLAB Simulink simulation and the results show improved performance of the field oriented control technique by the employment of the fractional order controllers instead of the conventional integer at different operating points. Furthermore, the additional degree of freedom in the fractional controllers helps the PSO technique to get the optimum cost at a very low number of iterations compared with that of the integer one.

A novel $d$ -axis variable-structure control (VSC) current regulator is proposed for realizing fast and accurate sensorless control in the surface-mounted permanent magnet synchronous motor (SPMSM) in this article. In this proposed regulator, the sigmoid function is employed as a switching control law to reduce the chattering effect and increase the degrees of freedom in tuning the regulator. The sensorless control performance under the $d$ -axis VSC current regulator is testified by using the model reference adaptive system (MRAS) and sliding-mode control (SMC) method. In the MRAS observer, the adaptive law of speed and resistance is designed so that resistance, speed, and position can be estimated simultaneously, which can eliminate the influence of the resistance variations theoretically. In the sliding-mode observer, the back electromotive force observer is employed to estimate the speed and position of PMSM so that the estimation can cover a relative low-speed range because pure integration is not employed. Furthermore, the dead-time compensation voltages (DTCV) strategy is employed to online estimate disturbance voltage in a feed-forward manner, which can improve the sensorless control performance of PMSM. Experimental results obtained from an SPMSM drive system validate the superiority of the proposed $d$ -axis VSC current regulator.

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 ...

In the context of position or speed sensorless control scheme, an improved rotor position estimation process for doubly-fed induction generator (DFIG) is presented in this paper. Model reference adaptive system (MRAS) is considered for the computation of rotor position. The stator flux producing current is taken as main working variable for the estimator. The estimator has various desirable features such as free from integrator-differentiator based computation, no information needed about initial rotor position (catch on fly operation), capability to work at very low slip condition (near synchronous speed operation), non-involvement of flux calculation, negligibly sensitive to mismatch in machine parameters. The major advantage of the proposed scheme is that it is insensitive to magnetizing inductance and rotor resistance of the electrical machine. Also, the novelty of the estimator lies in the fact that the suggested estimator works stably and accurately from very light load to full load operating conditions. The robust operation of proposed estimator is verified through real-time experimental set up.

Sensorless speed control of permanent magnet synchronous motor (PMSM) remains in progress to beat the prevailing drawbacks like speed ripples, torque ripples, and poor performance at low speeds with no ­load or lightly loaded conditions In this paper, Sensorless Field Oriented Control (FOC) system which is based on artificial neural network (ANN) implementation is proposed. The control system uses the ANN-aided Model Reference Adaptive System (MRAS) for speed estimation. Both the speed estimator and the controller of the proposed system use ANN-based implementations. The performance of the proposed system is analyzed for speed control of PMSM, for various speed and load torque values. The simulation results of the proposed ANN-based speed estimator and controller model demonstrate that the ANN-aided MRAS based estimator has the capability to enhance the performance of the system. Specifically, it reduces the overall complexity and initial speed overshoot by 1% as compared to the conventional controllers employed for PMSM speed control. It also achieves 0.002 rad/s improvements on the steady-state error with acceptable settling and rising time in its dynamic response.

As for the inverse decoupling control system of a bearingless induction motor (BL-IM), in order to eliminate the influence of rotor resistance variation on its control performance, on the basis of the reactive power calculation of torque system, a novel fuzzy model reference adaptive (MRAS) identification method of rotor resistance is proposed. The reference model and adjustable model of instantaneous reactive power are derived in detail. In order to improve the identification performance of rotor resistance, a fuzzy PI adaptive law based on popov super stability theory is constructed. On this basis, a rotor resistance identifier is constructed, and it is used to on-line correct the rotor resistance parameter in the inverse system mathematical model of a BL-IM system. Based on the inverse decoupling control system of a BL-IM, the simulation experimental analysis and verification are carried out. The simulation experimental results have shown that when the proposed identification method of rotor resistance is used, not only the identification- and tracking-speed of rotor resistance can be effectively improved, but also the identification accuracy of rotor resistance can be improved; as for the BL-IM system, after the rotor resistance parameter is on-line corrected, not only the inverse dynamic decoupling control performance can be effectively improved, but also the robustness of BL-IM system to the variation of rotor resistance parameters can be improved.

This article proposes a rotor position observer based on a reactive power model reference adaptive system (MRAS) for sensorless direct voltage control (DVC) strategy of the stand-alone brushless doubly fed induction generators (BDFIGs). The proposed MRAS observer is based on the control winding (CW) reactive power and the power winding (PW) field orientation. The suggested new sensorless control method does not require flux computation, which can ensure the simplicity and efficacy of the proposed method. The stability analysis of the new observer is investigated and confirmed with the concept of hyperstability and using Popov’s criterion methodology. The robustness of the new control method is analyzed under parameter uncertainties. Furthermore, comprehensive simulation and experimental results are obtained with a prototype wound-rotor BDFIG. The corresponding results confirm the capability and effectiveness of the proposed sensorless DVC strategy with a good transient behavior under different operating conditions for the stand-alone BDFIG system. Moreover, these results assure that the presented rotor position observer is robust via any machine parameter change.

The use of renewable energies has increased in these last decades. The wind energy attracts more attention of several research studies. The control of the power generated by the wind turbine is very complicated. It requires the application of new techniques of control. This paper presents an application of Model reference adaptive system (MRAS) in the control of wind turbine power. The structure of the proposed MRAS consists of Neuro fuzzy (NF) controller and an adaptive system based on sliding mode controller (SMC). The use of NF and SMC methodologies is very interest and it allows improving the performances of the system control. The NF has the advantages of expert knowledge of the fuzzy inference system and the learning capabilities of neural networks. The use of SMC gives more flexibility to the adaptive system. According to digital simulation results, the designed MRAS-NF-SMC controller provides a good dynamic behaviour, and an excellent tracking of the requested trajectory

This paper presents a speed control for in-wheel brushless AC (BLAC) motors using a simple hall sensor to improve steering and driving performance of mobility scooters. To achieve smooth steering and motion controls, the sensor-sensorless combined estimation method is proposed. Furthermore, a modified current model reference adaptive system (MRAS) is also proposed to increase the dynamic response and estimation accuracy. The dead-time effect is directly compensated to improve the sensorless performance in the lower speed region. From standstill to the controllable minimum speed region, fast open-loop control method is applied to the traction motors. The vehicle velocity is then controlled using the speed information obtained from the proposed sensorless-sensor combined estimation scheme. To verify the effectiveness, a prototype mobility scooter was manufactured and the proposed control method was tested. Experiment results shows that the steering and motion of the vehicle are improved and the overall performance is increased compared to that of the conventional driving method.

Failure of any sensor involved in the operation of shunt active power filter (SAPF) not only degrades its performance but also deteriorates power quality. The purpose of the proposed technique is to eliminate load current, dc voltage, and filter current sensors, thus reducing the hardware cost of SAPF. In addition to the load and filter current sensor reduction, this article proposes a new dc voltage estimation technique, which will be used to eliminate the dc voltage sensor present in conventional SAPF. The proposed dc voltage estimation technique is based on model reference adaptive system (MRAS). A comparative analysis of the conventional SAPF (i.e., including all sensors) is performed with the proposed sensor reduction method to verify the effectiveness of the proposed method. Stability and sensitivity analysis of the estimation technique is performed in detail. The proposed work is simulated on the MATLAB/SIMULINK platform under various load conditions and disturbances. Further, it is validated on OPAL-RT 5700 platform.

In this paper, unipolar excitation-based speed sensorless vector-controlled switched reluctance motor (SRM) drive is addressed. The use of position/speed sensors has few apprehensions in terms of price, dependability, compactness, especially from electric vehicle (EV) application view point. Therefore, for speed/torque control of SRM, it is mandatory to have some estimation technique which estimates the speed/position satisfactorily and with ease. In this paper, F-MRAS (flux-based model reference adaptive system) speed estimator is employed for estimating the speed/position. The reason for selection of this speed estimator is because of its simplicity, no large lookup table requirement and no external circuitry requirement for pulses injection. This estimator depends on stator resistance of the machine. Therefore, to make the speed estimator robust to stator resistance variations, on-line estimation of the resistance and its compensation is required. From this view point, a novel stator resistance estimator is proposed which estimates the resistance on-line. Further, this paper discusses in detail about the stability analysis of proposed resistance estimator and F-MRAS-based speed estimator. The speed estimator in combination with the proposed resistance estimator performs well in both motoring and regeneration modes. An exhaustive simulation of the drive has been carried out in MATLAB/SIMULINK. Also, the results are confirmed experimentally on a dSPACE-1104 platform.

In this paper, a (BS_SM) new Backstepping_ Sliding mode controll structure combined with a (VM_SC_ MRAS) improved stator current MRAS based on adaptive speed observer using neuron network and sliding mode are proposed to sensorless vector control for The propulsion system of Ship. The design of the controller is based on new BS and SM sructure to improve its performance and robustness. VM_SC_ MRAS improved adaptive speed observer is proposed to estimate the speed of propeller. The combination of BS-SM controller with VM_SC_MRAS adaptive speed observer can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and load disturbances, improving dynamic performance and enhancing the robustness of the SPIM drive system, perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation. The simulation results lead to the conclusion that the proposed system for the propulsion system of ship is feasible. The simulation results on a test ship propelled showed that the proposed control approach operates satisfactorily

This research article corroborates the working of a model reference adaptive model (MRAS) with fractional-order proportional-integral (FOPI $$^\lambda $$ )-based encoderless speed control approach for ventilation system drive using recurrent neural network (RNN) for the low- and medium-range operation. The purpose of this study is to minimize the energy loss due to fluctuations and variation in the rotor speed and also find the optimum values of FOPI $$^\lambda $$ by using a recurrent neural network to enhance the overall implementation of the system. In this perspective, the low-speed execution of MRAS is poor due to the existence of a pure integral and derivative parameter. Towards enhancement of the performance at speed region, a MRAS method with RNN is used. The network is trained using the Levenberg–Marquardt (LM) algorithm, and FOPI $$^\lambda $$ control method is used for tuning the gain of proportional-integral of speed and current controller of the encoderless speed control of the ventilation drive. The presented RNN speed estimator with FOPI $$^\lambda $$ controller has shown better performance and stability in transitory and stable operation as well as it also provides an enhancement in the overall efficiency of the ventilation drive. The validation of the presented algorithm is detailed experiments on a fully digitized 5.5 kW ventilation system using the Lab VIEW interface.

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.

This paper proposes a direct torque control (DTC) of permanent magnet synchronous motor (PMSM) with high torque estimation accuracy. Based on the PMSM mathematical model considering iron loss resistance, this paper analyzes that when using stator current to estimate torque, the accuracy will be affected by iron loss resistance. Using stator magnetizing current can solve the current deviation caused by iron loss resistance and improve torque estimation accuracy. Since the stator magnetizing current cannot be obtained directly, a novel iron loss resistance observer based on the model reference adaptive system (MRAS) is designed to estimate iron loss resistance and stator magnetizing current simultaneously. Popov’s hyperstability theory guarantees the stability of the designed observer. Finally, a 1kW PMSM experimental platform is constructed to verify the proposed method. Both the simulation and experimental results show that the designed observer is stable, and using stator magnetizing current to estimate torque has better accuracy than stator current.

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.

This paper propose a novel Port Controlled Hamiltonian_Backstepping (PCH_BS) control structure with online tuned parameters, in combination with the modified Stator Current Model Reference Adaptive Syatem (SC_MRAS) based on speed and flux estimator using Neural Networks(NN) and sliding mode (SM) for sensorless vector control of the six phase induction motor (SPIM). The control design is based on combination PCH and BS techniques to improve its performance and robustness. The combination of BS_PCH controller with speed estimator can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and external load disturbances, enables very good static and dynamic performance of the sensorless drive system (perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation) in a wide speed range, and robust for the disturbances of the load, the speed variation and low speed. The proposed sensorless speed control scheme is validated through Matlab-Simulink. The simulation results verify the effectiveness of the proposed control and observer.

In this paper, a new controll structure are proposed to sensorless vector control the in-wheel motordrive system of Electric Vehicle (EV) to improve its performance and robustness. The design of the controlleris based on Backstepping and Hamitolnion control combined with a improved stator current MRAS adaptivespeed observer proposed to estimate the vehicle speed and it also can compensate for the uncertainties causedby the machine parameter variations, measurement errors, and load disturbances, improving dynamicperformance and enhancing the robustness of the SPIM drive system, perfect tuning of the speed referencevalues, fast response of the motor current and torque, high accuracy of speed regulation. A global EV model isalso evaluated based on the vehicle dynamics in this paper. The simulation results lead to the conclusion thatthe proposed system for the propulsion system of electric vehicle is feasible. The simulation results on a testvehicle propelled by two SPIM showed that the proposed control approach operates satisfactorily

To optimize the efficiency of the linear compressor, its operating frequency must be controlled equal to the system resonant frequency. The traditional resonant frequency tracking control algorithm relies on the steady state characteristics of the system, which suffers from slow convergence speed, low accuracy and slow system response. In order to solve these problems, a novel resonant frequency tracking control for linear compressor based on model reference adaptive system (MRAS) is proposed in this paper, and the parameter adaptive rate is derived by the Popov's hyperstability theory, so that the system resonant frequency can be directly calculated through the parameter adaptive rate. Furthermore, the traditional algorithm needs to calculate the piston stroke signal by integrating the back-EMF, which has the problem of integral drift. The algorithm proposed in this paper only needs the velocity signal, and the accuracy of the velocity calculation can be ensured by utilizing the self-adaptive band-pass filter (SABPF), thereby greatly improving the accuracy of the resonance frequency calculation. Simulation results verify the effectiveness of the proposed algorithm.

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.

Motor speed control requires motor speed data as feedback from control actions. Motor speed data is usually obtained from the speed sensor. In this paper, the motor speed observer for permanent magnet synchronous motor is proposed to obtain motor speed data based on motor back emf voltage making it more economical without a speed sensor. The Speed observer is designed based on the Model Reference Adapative System (MRAS) with using Least Squares Support Vector Machine Regression (LSSVMR) algorithm for adaptation mechanism tools. The proposed speed observer is tested with varying motor speeds. The test results show that the MRAS-based motor speed observer using LSSVMR has successfully estimated the rotation speed of the permanent magnet synchronous motor based on the back emf motor voltage. It can be seen from the maximum error of the motor speed, ie only 3.7 rpm at transient conditions and close to zero at steady state

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 article presents a new rotor speed observer for the sensorless control of stand-alone brushless doubly fed induction generators based on the control winding flux model reference adaptive system structure. The observer is incorporated in the control strategy for regulating the terminal voltage magnitude and frequency under the different operating conditions. The capability of the proposed observer and control method is demonstrated by comprehensive simulation results and verified by experiments. As shown, without using physical speed sensors, the sensorless control algorithm incorporating the proposed speed observer can effectively maintain the amplitude and frequency of power winding voltage constant at different rotor speeds and under different conditions of load and machine parameter, e.g., inductance and/or resistance, variations.

A sensorless speed estimation method for performing the indirect field-oriented control (IFOC) of induction motors (IMs) is presented in the current research. The method uses the stator voltage as state variable but eliminates the voltage transducers. Traditional MRAS methods use two sets of equations to estimate the motor speed. However, the proposed system utilizes the q-axis current regulator proportional–integral (PI) control output $$ \left( {V_{\text{sq}}^{*} } \right) $$ in the reference model as a new Model Reference Adaptive System (MRAS) technique and compares this value with the estimated voltage $$ \left( {\hat{V}_{\text{sq}} } \right) $$ in an adaptive model. The method calculates the steady-state q-axis stator voltage in adaptive model, so, the calculation burden in the estimation loop is significantly reduced. Choosing the q-axis stator voltage as state variable removes requirement of the rotor parameters in calculations, and therefore, the system becomes resistant to the alteration in rotor parameters. Furthermore, the mentioned special calculation ensures the elimination of the need for flux estimation, so the speed estimation technique becomes resistant to integral and derivative calculation-based problems. The MATLAB/Simulink simulation results and experiments under various operation conditions verifies the effectiveness of the studied technique.

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.

Field-oriented control (FOC) is standardly used in high-performance induction machine (IM) applications. However, the performance of FOC algorithms strongly depends on the level of agreement between the actual IM and the respective model used for the algorithm design. IM parameters’ values are known to vary with the operating conditions, so their online identification is preferable. In addition, it is often preferred to acquire the rotor speed by estimation because speed sensors increase the cost and size of the system while reducing its reliability. Model-reference-adaptive- system (MRAS) estimators are popular due to their simple implementation, but they are sensitive to IM parameters and, hence, usually include a parameter tuning mechanism. This paper proposes an indirect rotor- field- oriented controller with the parallel MRAS speed estimation and IM parameter tuning – implemented in an induction generator system. Both the control algorithm and the MRAS include the stray load and iron losses, magnetic saturation, and rotor resistance variation, whereas the MRAS additionally accounts for variations of the stator and stray-load resistances. The system performance is experimentally evaluated over wide ranges of the IM flux, speed, torque, and temperature.

The permanent magnet synchronous motors (PMSM) are widely employed in industrial, robotic, water pumping and HVAC applications due to their numerous benefits such as small size, high-energy efficiency, high performance, low inertia and the ability to operate in full load at low speeds. In case the PMSM drive system is supplied from photovoltaic (PV) modules, it can be a perfect match for water pumping or HVAC applications. In such a system, in order to extract full energy from PV modules, a maximum power point tracking (MPPT) algorithm must be employed. This article presents a PV system-fed PMSM drive system with sensorless speed control. The proposed system consists of two main parts. The first part deals with MPPT algorithm based on fuzzy logic controller and the second part deals with PMSM drive system with a sensorless speed estimator by using Model Reference Adaptive System (MRAS) approach to eliminate the use of an encoder. The operation of PMSM is accomplished by using the vector control method to obtain a similar dynamic of the DC motor. The overall system is modelled in Matlab/Simulink environment and simulation results are collected under various operating conditions.

For high-speed permanent magnet synchronous motor (PMSM), its efficiency is significantly affected by the performance of permanent magnets (PMs), and the phenomenon of demagnetization will occur with the increase of PM temperature. So, the temperature detection of PMs in rotor is very necessary for the safe operation of PMSM, and direct detection is difficult due to the rotation of rotor. Based on the relationship between permanent magnet flux linkage and its temperature, in this paper, a new temperature estimation method using model reference fuzzy adaptive control (MRFAC) is proposed to estimate PM temperature. In this method, the model reference adaptive system (MRAS) is built to estimate the permanent magnet flux linkage, and the fuzzy control method is introduced into MRAS, which is used to improve the accuracy and applicable speed range of parameters estimated by MRAS. Different permanent magnet flux linkages are estimated in MRFAC based on the variation of stator resistance, which corresponds to different working temperatures measured by thermal resistance, and the PM temperature will be obtained according to the estimated permanent magnet flux linkage. At last, the back electromotive force (BEMF) is measured on the experimental motor, and the flux linkage and PM temperature of the experimental motor are deduced according to the BEMF. Compared with the experimental results, the estimated PM temperature is very close to the actual test value, and the error is less than 5%, which verifies that the proposed method is suitable for the estimation of PM temperature.

International audience; Rotational speed sensor is one of the most important components used in wind energy conversion system control strategies. The latter is very expensive and may be faulty due to the harsh environment working, causing by that a low efficiency operation of the system. The use of a speed estimator or an observer may be a good alternative solution for the use either in sensorless control or detecting the sensor failure or degradation (FDI and FTC), provided that the observer is robust and ensures high rotational speed estimation accuracy. This paper presents a comprehensive study to solve the optimization problem of a model reference adaptive speed (MRAS) observer in a typical wind energy generation system based on permanent magnet synchronous generator using five adaptation mechanisms: The first one is the classical MRAS observer; it is based on proportional-integral (PI) controller. The second one uses a fuzzy logic controller (FLC) with two inputs. The third one uses the single-input fuzzy logic controller which represents the simplification of the conventional FLC. The fourth one uses the sliding mode controller, and the last one uses the super-twisting algorithm. A detailed comparison between the five adaptation mechanisms is carried out. The obtained results show a good estimation stability as well as a fast speed estimation at dynamic regime for the improved adaptation mechanisms compared with the conventional PI controller.

Purpose For vector control of permanent magnet synchronous motor (PMSM) requires motor speed and rotor position estimation. The precision of the open-loop techniques of the stator flux and speed for vector control PMSM drive drops as mechanical speed decreases. The stator resistance and estimated stator flux values crisscross have a huge effect on the transient and steady-state performance of the drive at lower speed. The framework turns out to be increasingly strong against parameter crisscross and signal noises by using adaptive observers for estimation of speed and flux. Design/methodology/approach This paper presents a comparison of two-speed observers for the vector control PMSM drive: the sliding mode observer (SMO) and the model reference adaptive system (MRAS). A comprehensive analysis of SMO and MRAS respects dynamic, steady-state performance and robustness, affectability, stability and computational complexity has been introduced. The abstract of the advantages and disadvantages of both observer and their comparative analysis have also been discussed. Findings Dynamic performance steady-state performance and robustness, affectability and stability. Originality/value This paper presents a sensorless scheme, namely, MRAS and SMO for control of PMSM drive. These sensorless techniques have been tested for a PMSM motor drive and the motor performance was compared for both techniques. Matlab/Simulink based simulation results conclude that the adaptive methods improve dynamic response, reduces torque ripples and extended speed range.

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.

21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, ELECTR NETWORK, JUL 11-17, 2020; International audience; In this paper, we propose a fully sensorless control strategy that guarantees robustness and stability properties. The aim is to maximize the extracted power and to ensure an optimum control without mechanical sensors to reduce the cost. Firstly, a fuzzy logic technique has been used to estimate the wind speed information. Then an improved model reference based on adaptive sliding mode controller has been developed to estimate the permanent-magnet synchronous generator speed. The proposed control strategy includes two control loops, an outer control loop based on fuzzy super twisting algorithm to regulate the generator speed to its optimal value and to provide the optimum DC side current, and an internal control loop based on sliding mode controller to regulate the DC side current to its optimal value and provides the appropriate signal for the DC-DC boost converter. The considered system in this work is supposed supplying a water pumping system for a use in isolated areas. The proposed control strategy has been compared to the conventional MPPT control method based on the concept of perturbation and observation. The obtained results show, a good estimation performance for the wind speed estimator and the improved MRAS speed observer, a good tracking performance and a good stability around the maximum power point. Comparing to the classical MPPT strategy, the current technique can greatly reduce the ripples that can generate vibrations and significant noise on the generator and the motor-pump group. Copyright (C) 2020 The Authors.

A novel 6/13-pole hybrid excitation axial field flux-switching permanent magnet machine (HEAFFSPMM) exhibits strong fault tolerance capability, high efficiency, and large torque density. However, merely few research on speed sensorless control in HEAFFSPMM exists. The speed sensorless control methods based on model reference adaptive system (MRAS) are studied and compared for the machine to improve the stability and reliability of the system and consequently improve the application of machine in control system. Based on the field-oriented control strategy, the MRAS observer of speed is designed and built by applying stator currents, stator flux linkages, and simplified stator currents. The three speed sensorless control algorithms of MRAS are compared and analyzed by using MATLAB/Simulink simulation and dSPACE1104 experimental platform. Results show that the speed sensorless control algorithm based on simplified stator currents has good control performance and high control accuracy.