Sensorless Direct Torque Control Research Articles

Sensorless DTC of BDFRG using LMS algorithm for wind energy conversion systems

This paper presents a new sensorless speed and secondary winding resistance estimation technique for brushless doubly fed reluctance generator (BDFRG) using least mean square (LMS) algorithm. Detailed mathematical derivation of the algorithm is explained and simulation results in MATLAB Simulink are shown for sensorless direct torque control (DTC) of BDFRG using LMS algorithm. A comparison to traditional speed estimation technique for BDFRG using primary winding flux linkage formula in stationary frame is then held to show the advantages of this proposed technique over the traditional method of speed estimation. Power electronics of the drive system is also simulated to consider effect of harmonics injected by electronic switches on both algorithms.

Sensorless Direct Torque Control Using the Inductance Inflection Point for a Switched Reluctance Motor

This paper proposes a new position sensorless method for a direct torque control system using the inductance inflection point of a switched reluctance motor (SRM). The SRM essentially requires knowledge of the rotor position in the torque control system; this is because it generates torque by exciting the stator windings according to the rotor position. This rotor position information is an important factor used to obtain phase commutation point and to calculate instantaneous torque. Various rotor position detection methods have been used, but most of the methods increase the construction cost by requiring additional hardware and use many parameters for high-order analysis modeling, causing problems such as increasing the number of calculations. Therefore, in this paper, we propose a method using the inductance inflection point to solve these problems. It is possible to estimate the position of the rotor by detecting where the inductance inflection point occurs due to the rotor and stator overlapping with each other, and the phase commutation point and instantaneous torque can be calculated. The calculated instantaneous torque is used for direct torque control and torque ripple reduction through the torque-sharing function. The proposed method is verified through simulation and experiments.

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

<p>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.</p>

Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

This paper presents the speed estimator for speed sensorless direct torque control of a three-phase induction motor based on constant voltage per frequency (V/F) control technique, using artificial neural network (ANN). The estimated stator current equation is derived and rearranged consistent with the control algorithm and ANN structure. For the speed estimation, a weight in ANN, which relates to the speed, is adjusted by using Widrow–Hoff learning rule to minimize the sum of squared errors between the measured stator current and the estimated stator current from ANN output. The consequence of using this method leads to the ability of online speed estimation and simple ANN structure. The simulation and experimental results in high- and low-speed regions have confirmed the validity of the proposed speed estimation method.

Exponential Reaching Law and Sensorless DTC IM Control with Neural Network Online Parameters Estimation based on MRAS

The most important problem in the control of induction machine (IM) is the change of its parameters, especially the stator resistance and rotor-time constant. The objective of<em> </em>this paper is to implement a new strategy in sensorless direct torque control (DTC) of an IM drive. The rotor flux based model reference adaptive system (MRAS) is used<em> </em>to estimate conjointly<em> </em>the rotor<em> </em>speed, the stator resistance and the inverse rotor time constant, the process of the estimation is performed on-line by a new MRAS-based artificial neural network (ANN) technique. Furthermore, the drive is complemented with a new exponential reaching law (ERL), based on the sliding mode control (SMC) to significantly improve the performances of the system control compared to the conventional SMC which is known to be susceptible to the annoying chattering phenomenon. An experimental investigation was carried out via the Matlab/Simulink with real time interface (RTI) and dSPACE (DS1104) board where the behavior of the proposed method was tested at different points of IM operation.

Constant Switching Frequency DTC for Matrix Converter Fed Speed Sensorless Induction Motor Drive

The paper presents a constant switching frequency scheme for speed sensorless Direct Torque Control (DTC) of Matrix Converter fed Induction Motor Drive. The use of matrix converter facilitates improved power quality on input as well as motor side, along with Input Power Factor control, besides eliminating the need for heavy passive elements. Moreover, DTC through Space Vector Modulation helps in achieving a fast control over the torque and flux of the motor, with added benefit of constant switching frequency. A constant switching frequency aids in maintaining desired power quality of AC mains current even at low motor speeds, and simplifies input filter design of the matrix converter, as compared to conventional hysteresis based DTC. Further, stator voltage estimation from sensed input voltage, and subsequent stator (and rotor) flux estimation is done. For speed sensorless operation, a Model Reference Adaptive System is used, which emulates the speed dependent rotor flux equations of the induction motor. The error between conventionally estimated rotor flux (reference model) and the rotor flux estimated through the adaptive observer is processed through PI controller to generate the rotor speed estimate.

Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering

The demand for sensorless control of surface-mounted permanent magnet synchronous motor drives has grown rapidly. Among various sensorless control techniques developed, Matsui’s current model-based approach and the extended Kalman filter approach have gained much attention. However, the performance of these control methods can be severely worsened or may even become unstable under strong disturbances or sensing failures. This paper presents a comparative study of the extended Kalman filter, the resilient extended Kalman filter, and the unscented Kalman filter-based sensorless direct torque and flux control approaches for the surface-mounted permanent magnet synchronous motor drives. Computer simulation studies and hardware implementation results have shown the efficiency and superior performance of the resilient extended Kalman filter and the unscented Kalman filter over the traditional extended Kalman filter for sensorless direct torque control applications.

Contribution of the FPGAs for Complex Control Algorithms: Sensorless DTFC with an EKF of an Induction Motor

In a conventional direct torque control (CDTC) of the induction motor drive, the electromagnetic torque and the stator flux are characterized by high ripples. In order to reduce the undesired ripples, several methods are used in the literature. Nevertheless, these methods increase the algorithm complexity and dependency on the machine parameters such as the space vector modulation (SVM). The fuzzy logic control method is utilized in this work to decrease these ripples. Moreover, to eliminate the mechanical sensor the extended kalman filter (EKF) is used, in order to reduce the cost of the system and the rate of maintenance. Furthermore, in the domain of controlling the real-time induction motor drives, two principal digital devices are used such as the hardware (FPGA) and the digital signal processing (DSP). The latter is a software solution featured by a sequential processing that increases the execution time. However, the FPGA is featured by a high processing speed because of its parallel processing. Therefore, using the FPGA it is possible to implement complex algorithms with low execution time and to enhance the control bandwidth. The large bandwidth is the key issue to increase the system performances. This paper presents the interest of utilizing the FPGAs to implement complex control algorithms of electrical systems in real time. The suggested sensorless direct torque control using the fuzzy logic (DTFC) of an induction motor is successfully designed and implemented on an FPGA Virtex 5 using xilinx system generator. The simulation and implementation results show proposed approach’s performances in terms of ripples, stator current harmonic waves, execution time, and short design time.

Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller

Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller

Sensorless Direct Torque Control of Induction Motor Using AI Based Duty Ratio Controllers

This paper presents some improvements in direct torque control of an induction motor using fuzzy logic switching controller along with fuzzy logic and neural network based duty ratio controller. The conventional direct torque control of induction motor suffers from major drawbacks like high torque and flux ripples, current and torque distortion when sector changes and poor transient response. High torque and flux ripples are reduced to some extent by replacing hysteresis controller and switching table with fuzzy logic switching controller (FDTC). However, in FDTC the selected switching vector is applied for the complete switching time period which results in ripples under steady state. The FDTC steady state performance is improved by using duty ratio controller. Using duty ratio controller, the selected switching vector is applied to voltage source inverter only for the time determined by the duty ratio (δ) and for the remaining time, a zero switching vector is applied. As the switching vector is applied for only the time period till torque reaches its reference value, it results in the reduction of torque and flux ripples of induction motor. The selection of duty ratio is a nonlinear function of flux error, flux error and stator flux angle which is effectively implemented in this paper using the following artificial intelligence techniques: fuzzy logic and neural networks. The effectiveness of the proposed methods are evaluated by the simulation with Matlab/Simulink.

Sensorless DTC Drive of Induction Motor Using 3-Level Inverter

This paper presents a novel switching table based Direct Torque Control (DTC) of induction machine modified for the 3-level inverter. Due to its large number of voltage vectors, torque and flux ripples can be reduced by distinguishing small and large torque and flux errors and choosing different switching vectors for each case. This work also introduces two sensor-less DTC systems: the adaptive flux observer and the Model Reference Adaptive System (MRAS). Based on the dynamic model of the induction machine, the adaptive flux observer estimates the rotor flux and speed according to adaptation laws that must ensure the stability of the system. In another hand, the MRAS technique consists on minimizing the error obtained from the comparison of two of rotor flux models, one based on voltage and current and the other based on current and rotor speed, using an adaptive mechanism. The proposed schemes were verified by simulation under Matlab/Simulink environment. Simulation results show good dynamic performances and low ripples of torque response with the employment of 3-level inverter. As for speed estimation, both techniques allow a good estimation of rotor speed, but MRAS technique gives better dynamic performances.

Sensorless Direct Torque Control for Saliency Permanent Magnet Brushless DC Motors

Sensorless technology cannot only reduce the cost of motor drive system, but also improve system reliability. In the sensorless direct torque control (DTC) drive system of permanent magnet brushless dc motor (BLDCM), the estimations of instantaneous electromagnetic torque and stator flux linkage are two key issues. Additionally, some BLDCMs with salient-pole phenomenon make the estimations of torque and flux linkage even harder. A sensorless salient-pole BLDCM-DTC drive system is proposed in this paper. A stator current estimator with back electromotive-force (EMF) self-adaptation is first constructed according to stator current state equations and the back EMF is obtained by adaptive law. Then, a phase-locked loop is used to output smooth estimated value of rotor position angle based on the identified back EMF. At last, according to the estimated rotor position angle, the torque and stator flux linkage are calculated to realize saliency BLDCM-DTC running smoothly. Experimental results show that the estimated torque and stator flux linkage are very close to their actual values. The novel sensorless DTC drive system has nice dynamic and static performances, and the lowest steady-state speed can be decreased to 33 r/min.

Performance of a Sensorless Controlled Concentrated-Wound Interior Permanent-Magnet Synchronous Machine at Low and Zero Speed

This paper concerns the sensorless direct torque and flux control (DTFC) of a 14-pole/18-slot, fractional-slot concentrated winding interior permanent-magnet synchronous machine (IPMSM). The dynamic performance of the prototype machine at or near base speed was found to be comparable to a distributed wound IPMSM. However, the lowest operating speed of the machine under sensorless control scheme was only about 30% of the base speed. This paper demonstrates experimentally that this lack of performance can be overcome substantially by using the extended-flux linkage model and high-frequency (HF) injection methods. By using the extended-flux model, the lowest operable speed was reduced from 30% to 5% of the base speed. In order to operate the machine, at a standstill under sensorless control, the HF injection method was employed. Factors that affect the performance at zero speed were investigated systematically.

Simple Flux Regulation for Improving State Estimation at Very Low and Zero Speed of a Speed Sensorless Direct Torque Control of an Induction Motor

This paper presents a simple flux regulation for a direct torque control (DTC) of an induction motor (IM) to improve speed and torque estimations at low- and zero-speed regions. To accomplish this, a constant switching frequency controller (CSFC) is used to replace the three-level hysteresis torque comparator of a DTC IM. The DTC of IM utilizing CSFC (DTC-CSFC) retains the simple structure of a lookup table-based DTC drive. With DTC-CSFC, constant switching frequency is maintained, and at the same, the flux droop problem that normally occurs in DTC with the hysteresis controller (DTC-HC) at low speed is solved; subsequently, the stator flux and torque estimations at low speed are also improved. In the proposed system, the speed feedback for the closed-loop speed control is estimated using an extended Kalman filter, which requires heavy real-time computation. However, due to the simple structure of DTC-CSFC, small sampling time, hence large control bandwidth is possible. The performances of the speed sensorless DTC-HC and DTC-CSFC are compared experimentally under different operating conditions. With the improved stator flux regulation, experimental results of the DTC-CSFC showed a significant improvement in speed and torque estimations at very low and zero-frequency operations.

Fuzzy Logic Controller for Sensorless Direct Torque Control of an Induction Motor Driven by a Matrix Converter

Fuzzy Logic Controller for Sensorless Direct Torque Control of an Induction Motor Driven by a Matrix Converter

Low Speed Estimation in Sensorless Direct Torque Controlled Induction Motor Drive Using Extended Kalman Filter

Sensorless Direct Torque Control (DTC) is a powerful control scheme for high performance control of induction motor (IM) drives, which provides very quick dynamic response with simple structure and a decoupled control of torque and flux. The performance of the DTC drive depends greatly on the accuracy of the estimated flux components, torque and speed, using monitored stator voltages and currents. Low speed estimation is a great challenge because of the presence of transient offset, drift and domination of ohmic voltage drop.Extended Kalman filter (EKF) is a non linear adaptive filter which performs the process of finding the best estimate from the noisy data based on state space techniques and recursive algorithm. This paper mainly focuses on the accurate estimation of speed ranging from very low speed to rated speed using the equation of motion. A new state space model of the IM is developed for estimation in EKF, with load torque as an input variable and not as an estimated quantity which is the case in most previous studies. The developed algorithm is validated using MATLAB-Simulink platform for speeds ranging from low speed to rated speed at rated torque and at various torque conditions. An exhaustive analysis is carried out to validate the performance of DTC Induction motor drive especially at the low speeds. The results are promising for accurate estimation of speed ranging from low speed to rated speed using EKF.

Sensorless Direct Torque Control of Induction Machine Using Luenberger Observer and Simplified Extended Kalman Filter

A comparison between the sensorless Direct Torque Control (DTC) of the induction machine, based on different observers is proposed in this paper. The two DTC systems adopt alternatively a simplified extended Kalman filter and the Luenberger observer in order to estimate the speed of the induction machine and other quantities. PI regulator is used to regulate the estimate speed and improve the performances of the whole system. Simulation results show that the ripples of both torque and flux are reduced remarkable and a good dynamic of the speed and torque are achieved.

MRAS Speed Estimator Based on Type-1 and Type-2 Fuzzy Logic Controller for the Speed Sensorless DTFC-SVPWM of an Induction Motor Drive

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) - based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.

Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive

In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation.