Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions

Development and Implementation of Some Controllers for Performance Enhancement and Effective Utilization of Induction Motor Drive

The technological development in the field of power electronics and DSP technology is rapidly changing the aspect of drive technology. Implementations of advanced control strategies like field oriented control, linearization control, etc. to AC drives with variable voltage, and variable frequency source is possible because of the advent of high modulating frequency PWM inverters. The modeling complexity in the drive system and the subsequent requirement for modern control algorithms are being easily taken care by high computational power, low-cost DSP controllers. The present work is directed to study, design, development, and implementation of various controllers and their comparative evaluations to identify the proper controller for high-performance induction motor (IM) drives. The dynamic modeling for decoupling control of IM is developed by making the flux and torque decoupled. The simulation is carried out in the stationary reference frame with linearized control based on state-space linearization technique. Further, comprehensive and systematic design procedures are derived to tune the PI controllers for both electrical and mechanical subsystems. However, the PI-controller performance is not satisfactory under various disturbances and system uncertainties. Also, precise mathematical model, gain values, and continuous tuning are required for the controller design to obtain high performance. Thus, to overcome these drawbacks, an adapted control strategy based on Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller is developed and implemented in real-time to validate different control strategies. The superiority of the proposed controller is analyzed and is contrasted with the conventional PI controller-based linearized IM drive. The simplified neuro-fuzzy control (NFC) integrates the concept of fuzzy logic and neural network structure like conventional NFC, but it has the advantages of simplicity and improved computational efficiency over conventional NFC as the single input introduced here is an error instead of two inputs error and change in error as in conventional NFC. This structure makes the proposed NFC robust and simple as compared to conventional NFC and thus, can be easily applied to real-time industrial applications. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using feedback linearization of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using proposed simplified NFC as compared to the conventional NFC, rather it shows superior performance over PI-controller-based drive. A hybrid fuel cell (FC) supply system to deliver the power demanded by the feedback linearization (FBL) based IM drive is designed and implemented. The modified simple hybrid neuro-fuzzy sliding-mode control (NFSMC) incorporated with the intuitive FBL substantially reduces torque chattering and improves speed response, giving optimal drive performance under system uncertainties and disturbances. This novel technique also has the benefit of reduced computational burden over conventional NFSMC and thus, suitable for real-time industrial applications. The parameters of the modified NFC is tuned by an adaptive mechanism based on sliding-mode control (SMC). A FC stack with a dc/dc boost converter is considered here as a separate external source during interruption of main supply for maintaining the supply to the motor drive control through the inverter, thereby reducing the burden and average rating of the inverter. A rechargeable battery used as an energy storage supplements the FC during different operating conditions of the drive system. The effectiveness of the proposed method using FC-based linearized IM drive is investigated in simulation, and the efficacy of the proposed controller is validated in real-time. It is evident from the results that the system provides optimal dynamic performance in terms of ripples, overshoot, and settling time responses and is robust in terms of parameters variation and external load

A Fault-Tolerant Control Architecture for Induction Motor Drives in Automotive Applications

International audienceThis paper describes a fault-tolerant control system for a high-performance induction motor drive that propels an electrical vehicle (EV) or hybrid electric vehicle (HEV). In the proposed control scheme, the developed system takes into account the controller transition smoothness in the event of sensor failure. Moreover, due to the EV or HEV requirements for sensorless operations, a practical sensorless control scheme is developed and used within the proposed fault-tolerant control system. This requires the presence of an adaptive flux observer. The speed estimator is based on the approximation of the magnetic characteristic slope of the induction motor to the mutual inductance value. Simulation results, in terms of speed and torque responses, show the effectiveness of the proposed approach

Advanced Fault-Tolerant Control of Induction-Motor Drives for EV/HEV Traction Applications: From Conventional to Modern and Intelligent Control Techniques

International audienceThis paper describes active fault-tolerant control systems for a high-performance induction-motor drive that propels an electrical vehicle (EV) or a hybrid one (HEV). The proposed systems adaptively reorganize themselves in the event of sensor loss or sensor recovery to sustain the best control performance, given the complement of remaining sensors. Moreover, the developed systems take into account the controller-transition smoothness, in terms of speed and torque transients. The two proposed fault-tolerant control strategies have been simulated on a 4-kW induction-motor drive, and speed and torque responses have been carried to evaluate the consistency and the performance of the proposed approaches. Simulation results, in terms of speed and torque responses, show the global effectiveness of the proposed approaches, particularly the one based on modern and intelligent control techniques in terms of speed and torque smoothness

Detection and Diagnosis of Motor Stator Faults using Electric Signals from Variable Speed Drives

Motor current signature analysis has been investigated widely for diagnosing faults of induction motors. However, most of these studies are based on open loop drives. This paper examines the performance of diagnosing motor stator faults under both open and closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum analysis. Evaluation results show that the stator fault causes an increase in the sideband amplitude of motor current signature only when the motor is under the open loop control. However, the increase in sidebands can be observed in both the current and voltage signals under the sensorless control mode, showing that it is more promising in diagnosing the stator faults under the sensorless control operation

Implementation of a new flux rotor based on model reference adaptive system for sensorless direct torque control modified for induction motor

Introduction. In order to realize an efficient speed control of induction motor, speed sensors, such as encoder, resolver or tachometer may be utilized. However, some problems appear such as, need of shaft extension, which decreases the mechanical robustness of the drive, reduce the reliability, and increase in cost. Purpose. In order to eliminate of speed sensors without losing. Several solutions to solve this problem have been suggested. Based on the motor fundamental excitation model, high frequency signal injection methods. The necessity of external hardware for signal injection and the adverse influence of injecting signal on the motor performance do not constitute an advantage for this technique. Fundamental model-based strategies method using instantaneous values of stator voltages and currents to estimate the rotor speed has been investigate. Several other methods have been proposed, such as model reference adaptive system, sliding mode observers, Luenberger observer and Kalman filter. The novelty of the proposed work consists in presenting a model reference adaptive system based speed estimator for sensorless direct torque control modified for induction motor drive. The model reference adaptive system is formed with flux rotor and the estimated stator current vector. Methods. The reference model utilizes measured current vector. On the other hand, the adjustable model uses the estimated stator current vector. The current is estimated through the solution of machine state equations. Practical value. The merits of the proposed estimator are demonstrated experimentally through a test-rig realized via the dSPACE DS1104 card in various operating conditions. The experimental results show the efficiency of the proposed speed estimation technique. Experimental results show the effectiveness of the proposed speed estimation method at nominal speed regions and speed reversal, and good results with respect to measurement speed estimation errors obtained.Вступ. Щоб реалізувати ефективне керування швидкістю асинхронного двигуна, можна використовувати датчики швидкості, такі як енкодер, резольвер або тахометр. Однак виникають деякі проблеми, такі як необхідність подовження валу, що знижує механічну міцність приводу, знижує надійність та збільшує вартість. Мета. Для усунення датчиків швидкості без втрати. Було запропоновано кілька рішень на вирішення цієї проблеми. На основі моделі основного порушення двигуна використовуються методи подачі високочастотного сигналу. Необхідність зовнішнього обладнання для подачі сигналу та несприятливий вплив подачі сигналу на роботу двигуна не є перевагою цього методу. Досліджено метод стратегій на основі фундаментальних моделей з використанням миттєвих значень напруг та струмів статора для оцінки швидкості обертання ротора. Було запропоновано кілька інших методів, таких як еталонна адаптивна система моделі, спостерігачі режиму ковзання, спостерігач Люенбергера і фільтр Калмана. Новизна запропонованої роботи полягає у поданні модельної еталонної адаптивної системи оцінки швидкості прямого бездатчикового управління моментом, модифікованої для асинхронного електроприводу. Еталонна адаптивна система моделі формується з магнітним потоком ротора та оціненим вектором струму статора. Методи. Еталонна модель використовує вимірюваний вектор струму. З іншого боку, модель, що регулюється, використовує передбачуваний вектор струму статора. Струм оцінюється шляхом вирішення рівнянь стану машини. Практична цінність. Переваги запропонованого оцінювача продемонстровані експериментально на тестовій установці, реалізованій на платі dSPACE DS1104 у різних умовах експлуатації. Експериментальні результати свідчать про ефективність запропонованої методики оцінки швидкості. Експериментальні результати показують ефективність запропонованого методу оцінки швидкості в областях номінальних швидкостей та реверсивних швидкостей, а також хороші результати щодо отриманих похибок оцінки швидкості вимірювання

Particle swarm optimization-based stator resistance observer for speed sensorless induction motor drive

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

Speed Sensorless Control of Six-Phase Asynchronous Motor Drive

Multi -phase ac motor drives are nowadays considered for various applications, due to many advantages that they offer when compared to three-phase motors. Cancellation of mechanical position or speed sensors at the motor shaft have the attractions for adjustable speed drives of induction motor to reduce the cost and increase the reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. This paper investigates speed estimation method using model reference adaptive system (MRAS) to improve the performance of a sensorless vector controller of six-phase induction motor (IM). In the proposed method, the stator current is used as the state variable to estimate the speed. Since the stator current error is represented as a function of the first degree for the error value in the speed estimation, the proposed method provides fast speed estimation and is also, more robust to variations in the stator resistance, compared with other MRAS methods. Consequently, this method can improve the performance of a sensorless vector controller in a low speed region and at zero-speed. The proposed method is verified by simulation using the Matlab/Simulink package. The performance of the proposed system is investigated at different operating conditions. The proposed controller is robust and suitable for high performance six-phase induction motor drives. Simulation results validate the proposed approaches