An improved stator flux estimation for speed sensorless stator fluxorientation control of induction motors

This paper proposes a programmable low pass filter (LPF) to estimate stator flux for speed sensorless stator flux orientation control of induction motors. The programmable LPF is developed to solve the DC drift problem associated with a pure integrator and a LPF. The pole of the programmable LPF is located far from the origin in order to decrease the time constant with the increasing speed. In addition, the programmable LPF has a phase/gain compensator to estimate exactly stator flux in a wide speed range. Consequently, the drift problem is much improved and the stator flux is exactly estimated in the wide speed range. The validity of the proposed programmable LPF is verified by speed sensorless vector control of a 2.2 kW three-phase induction motor     

A programmable cascaded low-pass filter-based flux synthesis for astator flux-oriented vector-controlled induction motor drive

The concept of a programmable cascaded low-pass filter method of flux vector synthesis has been introduced in the literature. In this paper, the idea is expanded, analyzed, improved, and then applied to a stator flux oriented 100-kW electric vehicle drive. It was verified that the flux estimation works well at zero speed finite torque start-up mode and low- and high-speed field weakening regions, thus completely eliminating the need of a speed sensor     

Online Identification of Stator Transient Inductance in Rotor-Flux-Oriented Induction Motor Drive

In a rotor-flux-oriented induction motor drive, stator transient inductance is varied with the change of operating conditions. If the stator transient inductance is not tuned, the field orientation cannot be obtained. As a result, q-axis rotor flux does not become zero, and the performance is deteriorated. This paper shows the problems caused by the detuning of stator transient inductance and proposes a simple online tuning scheme of stator transient inductance for an indirect rotor flux-oriented induction motor drive. Stator transient inductance is estimated only by stator voltage and stator current. The proposed method is verified by simulation and experimental results.     

Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive

This paper presents a new method of online estimation for the stator and rotor resistances of the induction motor for speed sensorless indirect vector controlled drives, using artificial neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The rotor speed is synthesized from the induction motor state equations. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both resistances are estimated experimentally, using the proposed neural network in a vector controlled induction motor drive. Data on tracking performances of these estimators are presented. With this speed sensorless approach, the rotor resistance estimation was made insensitive to the stator resistance variations both in simulation and experiment. The accuracy of the estimated speed achieved experimentally, without the speed sensor clearly demonstrates the reliable and high-performance operation of the drive     

Speed sensorless stator flux-oriented control of induction motor in the field weakening region using Luenberger observer

In a conventional speed sensorless stator flux-oriented (SFO) induction motor drive, when the estimated speed is transformed into the sampled-data model using the first-forward difference approximation, the sampled-data model has a modeling error which, in turn, produces an error in the rotor speed estimation. The error included in the estimated speed is removed by the use of a low pass filter (LPF). As the result, the delay of the estimated speed occurs in transients by the use of the LPF. This paper investigates the problem of a conventional speed sensorless SFO system due to the delay of the estimated speed in the field weakening region. In addition, this paper proposes a method to estimate exactly speed by using Luenberger observer. The proposed method is verified by the simulation and experiment with a 5-hp induction motor drive.     

Speed-sensorless sliding-mode torque control of an induction motor

Novel induction motor control optimizing both torque response and efficiency is proposed in the paper. The main contribution of the paper is a new structure of rotor flux observer aimed at the speed-sensorless operation of an induction machine servo drive at both low and high speed, where rapid speed changes can occur. The control differs from the conventional field-oriented control. Stator and rotor flux in stator fixed coordinates are controlled instead of the stator current components in rotor field coordinates i_sd and i_sq. In principle, the proposed method is based on driving the stator flux toward the reference stator flux vector defined by the input command, which are the reference torque and the reference rotor flux. The magnitude and orientation angle of the rotor flux of the induction motor are determined by the output of the closed-loop rotor flux observer based on sliding-mode control and Lyapunov theory. Simulations and experimental tests are provided to evaluate the consistency and performance of the proposed control technique     

Flux observer with online tuning of stator and rotor resistancesfor induction motors

This paper proposes an adaptive flux observer for induction motors, where stator and rotor resistances are estimated in online environments. The variation of motor parameters during operation degrades the performance of the controller and the flux observer. Among the parameters of induction motors, rotor resistance is a crucial one for flux estimation, and stator resistance also becomes critical in the low-speed region. Under the persistent excitation condition, the proposed method estimates the actual values of stator and rotor resistances simultaneously, which guarantees the exact estimation of the rotor flux. The persistent excitation condition is not satisfied when the electric torque of an induction motor is absent due to the lack of rotor currents. Even in this case, the proposed method achieves the correct estimation of the rotor flux. Simulations and actual experiments show that the rotor flux is estimated in all operating conditions and that both resistances converge to their actual values when the electrical motor torque exists     

Model Reference Adaptive Controller-Based Rotor Resistance and Speed Estimation Techniques for Vector Controlled Induction Motor Drive Utilizing Reactive Power

In this paper, a detailed study on the model reference adaptive controller (MRAC) utilizing the reactive power is presented for the online estimation of rotor resistance to maintain proper flux orientation in an indirect vector controlled induction motor drive. Selection of reactive power as the functional candidate in the MRAC automatically makes the system immune to the variation of stator resistance. Moreover, the unique formation of the MRAC with the instantaneous and steady-state reactive power completely eliminates the requirement of any flux estimation in the process of computation. Thus, the method is less sensitive to integrator-related problems like drift and saturation (requiring no integration). This also makes the estimation at or near zero speed quite accurate. Adding flux estimators to the MRAC, a speed sensorless scheme is developed. Simulation and experimental results have been presented to confirm the effectiveness of the technique.     

Field oriented control of induction machines employing rotor endring current detection

The usual method of induction motor torque control uses the indirect field orientation principle in which the rotor speed is sensed and slip frequency is added to form the stator impressed frequency. Unfortunately, the rotor resistance varies as the motor heats up under load thereby changing the rotor time constant which has a deleterious effect on the torque response. In this paper two new field oriented control schemes are presented which employ rotor end ring current detection and thereby remove the dependence of the controller accuracy on temperature so that the controller is entirely independent of rotor time constant variations. The field orientation schemes do not require an incremental encoder for rotor position sensing. The motor torque can be accurately controlled even down to zero speed operation     

Speed sensorless stator flux-oriented control of induction machine in the field weakening region

In a conventional speed sensorless stator flux-oriented (SFO) induction machine drive system, when the estimated speed is transformed into the sampled-data model using the first-forward difference approximation, a modeling error occurs in the sampled data model. As the result, an error in the rotor speed estimation is produced. The error included in sampled data model of the estimated speed is removed by the use of a digital low pass filter (LPF). However, the delay of the estimated speed occurs in transients due to the use of the LPF. Consequently, current control loss occurs at the transition to field weakening region by the delay of the estimated speed. This paper investigates the problem of a conventional speed sensorless SFO system produced by the delay of the estimated speed in the field weakening region. In addition, this paper proposes a new method to estimate exactly rotor speed by using a Kalman filter. The proposed method is verified by simulation and experiment.     

Simple Derivative-Free Nonlinear State Observer for Sensorless AC Drives

In this paper, a new Kalman filtering technique, unscented Kalman filter (UKF), is utilized both experimentally and theoretically as a state estimation tool in field-oriented control (FOC) of sensorless ac drives. Using the advantages of this recent derivative-free nonlinear estimation tool, rotor speed and$dq$-axis fluxes of an induction motor are estimated only with the sensed stator currents and voltages information. In order to compare the estimation performances of the extended Kalman filter (EKF) and UKF explicitly, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. In the simulation results, it is shown that UKF, whose several intrinsic properties suggest its use over EKF in highly nonlinear systems, has more satisfactory rotor speed and flux estimates, which are the most critical states for FOC. These simulation results are supported with experimental results.     

An Adaptive Sliding Stator Flux Observer for a Direct-Torque-Controlled IPM Synchronous Motor Drive

This paper focuses on performance improvements of the stator flux estimation for a direct-torque-controlled interior permanent magnet synchronous motor drive. In this paper, an adaptive sliding observer is presented to estimate the stator flux linkage based on the motor current model. The experimental results show that the proposed observer has been able to deliver more accurate estimation than an open-loop estimator both in the steady state and during transients. The observer has better dynamic behavior, disturbance resistance, and high-accuracy estimation ability. With the integrated flux observer, the drive system can operate at very low speed down to 10 r/min (0.33 Hz) with half full load.     

An improved flux observer based on PLL frequency estimator for sensorless vector control of induction motors

This paper presents an improved method of flux estimation for sensorless vector control of induction motors based on a phase locked loop (PLL) programmable low-pass filter (LPF) and a vector rotator. A PLL synchronized with the voltage vector is used for stator frequency estimation. The pure integration of the stator voltage equations is difficult to implement and LPFs with a fixed cutoff provide good estimates only in the relatively high frequency range-at low frequencies, the estimates fail in both magnitude and phase. The method proposed corrects the above problem for a wide range of speeds. Simulations and experimental results on a 0.25-hp three-phase induction machine verify the validity of the approach.     

Estimation of induction machine inverse rotor time constant using torque angle tangent calculation

A new inverse rotor time constant estimation scheme for an induction machine is presented. For high performance induction machine control, indirect rotor flux oriented vector control is the most commonly applied control technique. It requires that an accurate estimate of the inverse rotor time constant is obtained to ensure correct orientation of the current vector with the rotor flux vector. An incorrect estimate will result in an incorrect flux level, reduced dynamic torque performance and reduced maximum available torque. A novel parameter estimation scheme is presented, based on the calculation of the tangent of the torque angle. The effectiveness of the technique is demonstrated through simulation and practical results.     

A digital current control technique for voltage-fed induction motor drives

Precise control of stator current is essential to high performance field orientation controlled induction motor drives. Any current error degrades the performance of the drive in the same way as incorrect tuning of field orientation. Previous research has shown that accurate current control can be achieved with intelligent but complex control algorithms. This paper presents a new current control scheme which can achieve high accuracy and fast dynamic response but which is very simple for microprocessor implementation. The scheme was derived using the discrete state space modelling of the induction motor. The control law does not require knowledge of rotor flux and was independent of the field orientation control tuning. Good static and dynamic performances were obtained in both the simulation and experimental verifications. The results also show that the leakage inductance model error might cause a current ripple. However, this parameter can be tuned to its correct value easily by inspecting the current response.     

Microprocessor-Based Field-Oriented Control of A CSI-Fed Induction Motor Drive

This paper describes the method of field orientation of the stator current vector with respect to the stator, mutual, and rotor flux vectors, for the control of an induction motor fed from a current source inverter (CSI). A control scheme using this principle is described for orienting the stator current with respect to the rotor flux, as this gives natural decoupling between the current coordinates. A dedicated micro-computer system developed for implementing this scheme has been described. The experimental results are also presented.     

Sliding-Mode Flux Observer With Online Rotor Parameter Estimation for Induction Motors

Field orientation techniques without flux measurements depend on the parameters of the motor, particularly on the rotor resistance or rotor time constant (for rotor field orientation). Since these parameters change continuously as a function of temperature, it is important that the value of rotor resistance is continuously estimated online. A fourth-order sliding-mode flux observer is developed in this paper. Two sliding surfaces representing combinations of estimated flux and current errors are used to enforce the flux and current estimates to their real values. Switching functions are used to drive the sliding surfaces to zero. The equivalent values of the switching functions (low-frequency components) are proven to be the rotor resistance and the inverse of the rotor time constant. This property is used to simultaneously estimate the rotor resistance and the inverse of the time constant without prior knowledge of either the rotor resistance or the magnetizing inductance. Simulations and experimental results prove the validity of the proposed approach     

A speed estimator for high performance sensorless control ofinduction motors in the field weakening region

The paper proposes a modified version of the model reference adaptive system (MRAS) based speed estimator, whose outputs of the reference and the adjustable model are rotor flux space vectors. The estimator is modified in such a way that the variation in the instantaneous level of the main flux saturation during operation in the field weakening is recognized and properly compensated at all times. The speed estimation scheme is equally applicable to both vector controlled and direct torque controlled induction machines, since it operates in the stationary reference frame and requires measurement of only stator voltages and currents. Verification of the proposed scheme is provided by simulation and by experimentation on an indirect feedforward rotor flux oriented induction machine for speed references of up to twice the base speed     

Design and implementation of the extended Kalman filter for thespeed and rotor position estimation of brushless DC motor

A method for speed and rotor position estimation of a brushless DC motor (BLDCM) is presented in this paper. An extended Kalman filter (EKF) is employed to estimate the motor state variables by only using measurements of the stator fine voltages and currents. When applying the EKF, it was necessary to solve some specific problems related to the voltage and current waveforms of the BLDCM. During the estimation procedure, the voltage- and current-measuring signals are not filtered, which is otherwise usually done when applying similar methods. The voltage average value during the sampling interval is obtained by combining measurements and calculations, owing to the application of the predictive current controller which is based on the mathematical model of motor. Two variants of the estimation algorithm are considered: (1) speed and rotor position are estimated with constant motor parameters and (2) the stator resistance is estimated simultaneously with motor state variables. In order to verify the estimation results, the laboratory setup has been constructed using a motor with ratings of 1.5 kW, 2000 r/min, fed by an insulated gate bipolar transistor inverter. The speed and current controls, as well as the estimation algorithm, have been implemented by a digital signal processor (TMS320C50). The experimental results show that is possible to estimate the speed and rotor position of the BLDCM with sufficient accuracy in both steady-state and dynamic operation. Introducing the estimation of the stator resistance, the speed estimation accuracy is increased, particularly at low speeds. At the end of the paper, the characteristics of the sensorless drive are analyzed. A sensorless speed control system has been achieved with maximum steady-state error between reference and actual motor speed of ±1% at speeds above 5% of the rated value     

An innovative direct self-control scheme for induction motor drives

The paper presents a new direct self-control (DSC) scheme for induction motor drives using the stator voltage third harmonic component in order to estimate the air-gap flux and the torque as well as to synchronize the supply voltage vector. Compared to previous DSC schemes the new one is independent from any motor parameter variation, specifically on stator resistance thus showing better performances at low speeds. The paper starts with a quick review on standard DSC main features pointing out the influence of stator resistance variations on the flux and torque control. The new DSC scheme is then introduced and evaluated by simulations and experimental tests on a 1.5 kW induction motor drive