Sensorless Control Of Induction Motor Research Articles

Sensorless ANFIS-Based Control of PV-Powered Double Stator Induction Motors for EVs

Sensorless ANFIS-Based Control of PV-Powered Double Stator Induction Motors for EVs

Displacement sensorless control of a bearingless induction motor based on modified high-frequency signal injection

ABSTRACT To enhance the rotor radial displacement identification accuracy of a bearingless induction motor (BIM) in displacement sensorless control, a displacement sensorless control strategy based on modified high-frequency signal injection is proposed. Firstly, a BIM flux linkage equation is analyzed, and by integrating the principle of the conventional high-frequency signal injection method, a self-detection model for the BIM rotor radial displacement is established. Secondly, a cascade notch filter and a low-pass filter are used in the signal demodulation process of the modified high-frequency signal injection. This strategy enables the DC component relevant to rotor displacement to be extracted from the high-frequency current，and obtains the rotor radial displacement equations. Finally, compared with the conventional high-frequency signal injection control, the proposed BIM displacement sensorless control system based on the modified high-frequency signal injection has higher displacement identification accuracy under different operation conditions, such as no-load, sudden load, and radial displacement mutation, as shown in simulation and experimental results. Overall, the motor performs well in terms of speed and suspension.

Sensorless Control of Induction Motor Based on Rotors Slot Harmonics and Digital Adaptive Filters

Sensorless Control of Induction Motor Based on Rotors Slot Harmonics and Digital Adaptive Filters

Sensorless Control of Induction Motor Based on Super-Twisting Sliding Mode Observer With Speed Convergence Improvement

Sensorless Control of Induction Motor Based on Super-Twisting Sliding Mode Observer With Speed Convergence Improvement

Novel Speed Sensorless DTC Design for a Five-Phase Induction Motor with an Intelligent Fractional Order Controller Based-MRAS Estimator

Abstract This paper presents a fractional-order adaptive mechanism-based model reference adaptive system (MRAS) configuration for speed estimation of sensorless direct torque control (DTC) of a five-phase induction motor. In effect, the fractional-order proportional-integral (FOPI) controller parameters are obtained by the particle swarm optimisation (PSO) algorithm to enhance the MRAS observer response. Thus, the developed algorithm in the speed loop control of the DTC strategy to increase its robustness against disturbances. Moreover, a comparative study has been done of the proposed MRAS-PSO/FOPI speed estimator with the conventional MRAS-proportional-integral (PI) and the PSO-based MRAS-PI. Simulation results have carried out of the different controllers used in the adaptation mechanism of the MRAS estimator, to show the performance and robustness of the proposed MRAS-PSO/FOPI algorithm in use.

Speed Sensorless Control of Induction Motors with All-Pass Filters Considering the Negative Operating Frequency

This paper proposes all-pass filters (APFs) to enable the reversal operation for the speed sensorless control of induction motors.

A Fractional Order Controller for Sensorless Speed Control of an Induction Motor

Agriculture activities are completely dependent upon energy production worldwide. This research presents sensorless speed control of a three-phase induction motor aided with an extended Kalman filter (EKF). Although a proportional integral (PI) controller can ensure tracking of the rotor speed, a considerable magnitude of ripples is present in the torque generated by a motor. Adding a simple derivative to have a proportional integral derivative (PID) action can cause a further increase in ripple magnitude, as it allows the addition of high-frequency noise in the system. Therefore, a fractional-order-based PID control is presented. The proposed control scheme is applied in a closed loop with the system, and simulation results are compared with the PID controller. It is evident from the results that the fractional order control not only ensures 20 times faster tracking, but ripple magnitude in torque was also reduced by a factor of 50% compared to that while using PID and ensures the effectiveness of the proposed strategy.

Displacement sensorless control of a bearingless induction motor based on hybrid flux estimation

ABSTRACT To address the problems of bulk-size, high cost for traditional mechanical displacement sensors in a bearingless induction motor (BL-IM), a displacement sensorless control strategy based on hybrid flux estimation for the BL-IM is proposed. By analyzing the merits and shortcomings of the voltage-type and the current-type flux estimator, a hybrid flux estimation model composed of two kinds of flux estimators is designed using the complementary characteristics of the two estimation models in high- and low-speed applications, and the velocity correlation function is used to achieve dynamic smooth switching between two models in the medium-speed zone. Secondly, the radial displacement of the rotor is calculated according to the mathematical model of the BL-IM, so as to realize the self-detection of the rotor displacement. Then, the vector control displacement sensorless system of the BL-IM is established. Finally, the simulation and experimental study are carried out on an experimental prototype designed by our research group. The simulation and experimental results show that the flux estimator has good estimation accuracy, and the sensorless control system of the BL-IM based on flux estimation not only has good self-detection ability of rotor radial displacement, but also has good speed characteristic and suspension properties.

A Comparative Study of Fuzzy SMC with Adaptive Fuzzy PID for Sensorless Speed Control of Six-Phase Induction Motor

Multi-phase motors have recently replaced three-phase induction motors in a variety of applications due to the numerous benefits they provide, and the absence of speed sensors promotes induction motors with variable speed drives. Sensorless speed control minimizes unnecessary speed encoder cost, reduces maintenance, and improves the motor drive’s reliability. The performance comparison of the fuzzy sliding mode controller (FSMC) with adaptive fuzzy proportional integral derivative (AFPID) control methods for sensorless speed control of six-phase induction motors was analyzed in this study, and the proposed control system has an advantage for multiphase machines, specifically six-phase induction motors (IMs) in this study, as they are the current active research area for electric vehicles, hybrid electric vehicles, aerospace, ship propulsion, and high-power applications. The speed control of a six-phase induction motor was performed by using an AFPID controller and FSMC. The comparative performance analysis was based on sensorless speed control of the six-phase induction motor. A proportional integral derivative (PID) controller is commonly employed as it is used to eliminate oscillations, but it has several drawbacks, such as taking a long time to decrease the error and stabilize the system at constant speed. The fuzzy type-2 and PID controllers were hybridized so as to obtain the advantages of both to enhance the system performance. Finally, the comparison result revealed that the FSMC preforms significantly better by achieving good tracking performance. The control technique maintains the sliding mode approach’s robustness while providing reduced overshoots with a smooth control action, and the FSMC revealed good dynamic response under load variations when compared to the AFPID controller.

Virtual voltage vector based predictive current control of speed sensorless induction motor drives

Nowadays, predictive current control (PCC) strategy has been recognized as a promising option for the control of electric drives for their control flexibility, fast dynamic response and weighting factorless structure of objective function. However, such kinds of conventional control strategies make drives to suffer from the ingress of significant harmonics in stator current, increasing further the steady state flux ripple followed by a noticeable torque ripple. To ameliorate these aforementioned shortcomings of conventional scheme, the present work employs the concept of virtual voltage vector (V3) in PCC strategy. The mathematical steps of the proposed V3PCC make the scheme more frontward in delay compensation, and also less parameter sensitive than the conventional one. As a baseline for the performance analysis, the present V3PCC is compared with a weighting factorless V3 based predictive flux control (PFC) scheme in induction motor (IM) drive. A speed adaptive flux observer is considered for the speed estimation purpose in both the control schemes. The performance of both the sensorless schemes for IM drive are analysed in MATLAB/Simulink, and also experimentally verified in a 2.2 kW IM drive test bench.

Robust Flux and Speed State Observer Design for Sensorless Control of a Double Star Induction Motor

In this paper, a robust flux and speed observer for sensorless control of a double star induction motor is presented. Proper operation of vector control of the double star induction motor requires reliable information from the process to be controlled. This information can come from mechanical sensors (rotational speed, angular position). Furthermore, mechanical flux and speed sensors are generally expensive and fragile and affect the reliability of the system. However, the control without sensors must-have performance that does not deviate too much from that which we would have had with a mechanical sensor. In this framework, this work mainly deals with the estimation of the flux and speed using a robust state observer in view of sensorless vector control of the double star induction motor. The evaluation criteria are the static and dynamic performances of the system as well as the errors between the reference values and those estimated. Extensive simulation results and robustness tests are presented to evaluate the performance of the proposed sensorless control scheme. Furthermore, under the same test conditions, a detailed comparison between the proposed state observer and the sliding mode-MRAS technique is carried out where the results of its evaluation are investigated in terms of their speed and flux tracking capability during load and speed transients and also with parameter variation. It is worth mentioning that the proposed state observer can obtain both high current quality and low torque ripples, which show better performance than that in the MRAS system.

Sensorless Control of Induction Motor Based on Differential Flatness Theory and Reduced MRAS Observer

In this paper, we propose a hybrid sensorless control method of IM, This method consists of using the differential flatness theory and reduced MRAS observer. The control design proceeds by showing that each input of the motor model stands for a differentially flat system, where the flat output is chosen to be the associated state variable. Next for each regulation loop is a virtual control input is computed, that can invert the loop’s dynamics and can eliminate the system’s tracking error. The reduced MRAS observer is used to estimate the rotor speed and flux. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach for sensorless control of induction motor.

Type‐2 Fuzzy Logic controller‐based stator current Model reference adaptive system speed observer for a hybrid electric vehicle to improve transient response during limp home mode

SummaryIn this paper, a stator current‐based model reference adaptive system (SCMRAS) for indirect vector control of induction motor fed to hybrid electric vehicle (HEV) for improving the transient response during limp home period has been proposed. In the proposed SCMRAS, the measured stator currents are employed in voltage model to eliminate the integrator in reference model. The stator currents are estimated and are compared with actual current components to estimate the rotor speed. Further, to improve the performance of SCMRAS during limp home period, the PI controller in the adaptation mechanism is replaced with type 2 fuzzy logic controller (T2FLC). The prototype model of the proposed SCMRAS using dSPACE DS 1104 R&D controller board has been developed for implementing speed sensorless indirect vector control of induction motor drive. The performance of SCMRAS and proposed SCMRAS using T2FLC estimators during limp home period is compared.

Speed Sensorless Model Predictive Torque Control of Induction Motors Using a Modified Adaptive Full-Order Observer

In this article, a new design of the state observer is proposed to reduce the unstable low-speed region of the adaptive full-order observer (AFO) based speed sensorless speed control of induction motors (IM). The proposed state observer is designed in z-domain rather than in s-domain following the pole-placement approach. The convergence rate of the proposed observer can be made faster than that of the IM poles without extra oscillation effects and the unstable low-speed region of AFO can be reduced to the zero-synchronous-frequency (ZSF) line. Also, a modified adaptation law of the AFO is derived to estimate the rotor speed. Based on the modified AFO, a continuous control set model predictive control (CCS-MPC) is designed for the inner-loop torque control. The cost function of the CCS-MPC is made of the predicted tracking errors on the state and control input. Finally, the dual-loop speed sensorless speed control using the modified AFO and CCS-MPC is presented in simulations and experiments.

Improvement of sensorless control of induction motor by voltage source inverter nonlinearities compensation and extended Kalman filter

Improvement of sensorless control of induction motor by voltage source inverter nonlinearities compensation and extended Kalman filter

An Improved Sensorless Control of Induction Motor Using ADALINE: Theory and Experiment

This article develops a new observer to achieve highly speed estimation for induction motor control. Its principle consists of estimating flux and speed separately, by using linear Luenberger as flux observer and intelligent PI controller as rotor speed estimator. Considering that is generally challenging to established proper PI parameters, the ADALINE algorithm is integrated as a trainable module to automatically calculate the KP and the KI gains in each iteration during all operation induction motor. This algorithm helps to search on the one hand the best regulation of the controller in real-time, taking into account the objectives to achieve (an accurate estimation) and the respect constraints (heavy calculation). On the other hand, it makes it possible to improve the adaptive Luenberger estimation problem in the unobservable zone (low speed). The experimental comparison of the observers demonstrates the visible improvement of the enhanced observer. It can converge to real speed in a short time with less static and transient error.

A new intelligent adaptation mechanism of MRAS based on a genetic algorithm applied to speed sensorless direct torque control for induction motor

A new intelligent adaptation mechanism of MRAS based on a genetic algorithm applied to speed sensorless direct torque control for induction motor

Conformable fractional order controller design and optimization for sensorless control of induction motor

PurposeIn recent years, use of sensorless control methods for electrical motor-based variable speed drive systems has been increasing rapidly to compensate the increasing costs in industrial systems. Also, use of induction motors is popular for a long time to decrease the cost of these industrial systems. This study aims to design an effective controller to improve the sensorless speed control performance of induction motor. To achieve this, a conformable fractional order proportional integral (CFOPI) controller is designed.Design/methodology/approachThe system is modeled based on small signal analysis by using the input–output data, experimentally. To do this, system identification toolbox of Matlab is used. The proposed controller is established on conformable fractional integral approach proposed by Khalil et al. (2014). CFOPI controller coefficients are optimized using particle swarm optimization method on the created small signal-based simulation model of the system to minimize the integral time absolute error. To prove the success of the proposed method, a traditional fractional order proportional integral (TFOPI) controller is tested under the same experimental system with the CFOPI controller.FindingsTFOPI and CFOPI controllers are tested with the optimum parameters. Reference and actual speed trends are obtained for both methods. In induction motor start-up test, settling-times are measured as 8.73 and 8.44 s and steady-state oscillations are 2.66% and 0% (almost) for TFOPI and CFOPI controllers, respectively. In variable referenced speed tracking test, CFOPI performs well at all speed levels, while TFOPI fails to reach the reference speed at most speed levels.Practical implicationsProposed CFOPI control method can be easily implemented in industrial systems, thanks to its simple algorithm. digital signal peripheral interface controller (dsPIC) based driver circuit with designed CFOPI controller used in this study can be applied directly to industrial systems such as elevators, conveyors, cranes and drills. Moreover, it can improve the performance of induction motor-based variable speed drive systems.Originality/valueThe proposed method provides robust performance for induction motor used in control systems. Additionally, it does this by using less complex algorithm written on the processors according to the traditional fractional order controllers.

Modified MRAS approach for sensorless speed control of induction motor for reliability improvement

Modified MRAS approach for sensorless speed control of induction motor for reliability improvement

Speed sensorless control of a bearingless induction motor with combined neural network and fractional sliding mode

To address the problem of speed and flux observation in sensorless control of a bearingless induction motor under the influence of parameter changes and external disturbances, a speed sensorless control strategy combining radial basis function (radial basis function, RBF) neural network and fractional sliding mode is proposed. According to the current error, fractional sliding mode control rate is designed to reduce the speed-observed chatter of the bearingless induction motor and its adverse effect on the rotor suspension stability. Then, combined with the theory of RBF neural network, the new optimal control rate is obtained by using its approximation ability. At the same time, the stability of two control rate is proved. Thus, the flux linkage and speed under normal operation, parameter change and external disturbance are observed and the new speed sensorless control is realized. The simulation and experimental results show that the proposed joint RBF neural network approximation algorithm and fractional sliding mode speed sensorless control system of the bearingless induction motor can not only effectively identify the flux and speed under three conditions of no-load, load disturbance and speed change, but also ensure the good suspension of the motor rotor in the x-axis and y-axis directions.