An implementation of a programmable cascaded low-pass filter for a rotor flux synthesizer for an induction motor drive

This paper investigates a programmable cascaded low pass filter for the estimation of rotor flux of an induction motor, with a view to estimate the rotor time constant of an indirect field orientation controlled induction motor drive. Programmable cascaded low pass filters have been traditionally used in stator flux oriented vector control of the induction motor. This paper extends the use of this filter to estimate the rotor flux for the indirect field orientation control by generating rotor flux estimates from stator flux estimates. This is achieved by using a three-stage programmable cascaded low pass filter. The three-stage programmable cascaded low-pass filter investigated in this paper has resulted in excellent estimation of rotor flux in the steady-state and transient operation of an indirect field oriented drive. The estimated rotor flux data have also been used for the on-line rotor resistance identification with artificial neural network. Modeling and experiment results presented in this paper demonstrate this method of estimating rotor flux clearly.     

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.     

An online identification method for both stator-and rotorresistances of induction motors without rotational transducers

This paper describes an effective online method for identifying both stator and rotor resistances, which is useful in robust speed control of induction motors without rotational transducers. The identification method for stator resistance is derived from the steady-state equations of induction motor dynamics. On the other hand, the identification method for rotor resistance is based on the linearly perturbed equations of induction motor dynamics about the operating point. The identification method for both stator and rotor resistances uses only the information of stator currents and voltages. It can provide fairly good identification accuracy regardless of load conditions and be easily incorporated into any sensorless speed controller proposed in the prior literature. Some experimental results are presented to demonstrate the practical use of the identification method. A sensorless speed control system has been built for experimental work, in which all algorithms for identification and control are implemented on a digital signal processor. The experimental results confirm that the proposed method allows for high-precision speed control of commercially available induction motors without rotational transducers     

A high-performance sensorless indirect stator flux orientation control of induction motor drive

A new method for the implementation of a sensorless indirect stator-flux-oriented control (ISFOC) of induction motor drives with stator resistance tuning is proposed in this paper. The proposed method for the estimation of speed and stator resistance is based only on measurement of stator currents. The error of the measured q-axis current from its reference value feeds the proportional plus integral (PI) controller, the output of which is the estimated slip frequency. It is subtracted from the synchronous angular frequency, which is obtained from the output integral plus proportional (IP) rotor speed controller, to have the estimated rotor speed. For current regulation, this paper proposes a conventional PI controller with feedforward compensation terms in the synchronous frame. Owing to its advantages, an IP controller is used for rotor speed regulation. Stator resistance updating is based on the measured and reference d-axis stator current of an induction motor on d-q frame synchronously rotating with the stator flux vector. Experimental results for a 3-kW induction motor are presented and analyzed by using a dSpace system with DS1102 controller board based on the digital signal processor (DSP) TMS320C31. Digital simulation and experimental results are presented to show the improvement in performance of the proposed method.     

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.     

基于模型参考自适应系统的感应电机控制

采用模型参考自适应法设计了无速度传感器矢量观测器。现以电压模型为参考模型,电流模型为可调模型,推算出速度信息,计算输出控制信号,实现了对感应电机的精确控制;通过Matlab／Simulink对其进行仿真、验证,结果表明,该系统对定子磁链观测精度高,速度估计准确,改善了电机的控制特性。     

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.     

Online rotor resistance estimation using the transient state underthe speed sensorless control of induction motor

In the speed sensorless control of the induction motor, the machine parameters (especially rotor resistance R₂) have a strong influence on the speed estimation. It is known that the simultaneous estimation of the rotor speed and R₂ is impossible in the slip frequency type vector control, because the rotor flux is constant. But the rotor flux is not always constant in the speed transient state. In this paper, the R₂ estimation in the transient state without signal injection to the stator current is proposed. This algorithm uses the least mean square algorithm and the adaptive algorithm, and it is possible to estimate R₂ exactly. This algorithm is verified by the digital simulations and experiments     

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     

Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors

In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according to the error between the estimated motor speed and the speed given by the reference model. The speed of the vector-controlled IM is estimated using the $hbox{MRAS}^{rm CC}$ rotor speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor speed given by the proposed speed estimator. Simulation results are verified by experimental tests over a wide range of motor speed and drive parameter changes.      

Experimental Evaluation of Braided EKF for Sensorless Control of Induction Motors

Temperature- and frequency-dependent variations of the rotor (R'_r) and stator (R_s) resistances pose a challenge in the accurate estimation of flux and velocity in the sensorless control of induction motors (IMs) over a wide speed range. Solutions have been sought to the problem by signal injection and/or by the use of different algorithms for the different parameters and states of the same motor. In this paper, a novel Extended-Kalman-Filter (EKF)-based estimation technique is developed for the solution of the problem based on the consecutive operation of two EKF algorithms at every time step. The proposed ldquobraidedrdquo EKF technique is experimentally tested under challenging parameter and load variations in a wide speed range, including low speed. The results demonstrate a significantly increased accuracy in the estimation of R_s and R'_r, as well as load torque, flux, and velocity in transient and steady state, when compared with single EKFs or other approaches taken to estimate these parameters and states in the sensorless control of IMs. The improved results also motivate the utilization of the new estimation approach in combination with a variety of control methods which depend on accurate knowledge of a high number of parameters and states.     

A MRAS-based adaptive pseudoreduced-order flux observer for sensorless induction motor drives

The performance of vector-controlled sensorless induction motor drives is generally poor at very low speeds, especially at zero speed due to offset and drift components in the acquired feedback signals, and the increased sensitivity of dynamic performance to model parameter mismatch resulting especially from stator resistance variations. The speed estimation is adversely affected by stator resistance variations due to temperature and frequency changes. This is particularly significant at very low speeds where the calculated flux deviates from its set values. Therefore, it is necessary to compensate for the parameter variation in sensorless induction motor drives, particularly at very low speeds. This paper presents a novel method of estimating both the shaft speed and stator resistance of an induction motor. In this novel scheme, an adaptive pseudoreduced-order flux observer (APFO) is developed. In comparison to the adaptive full-order flux observer (AFFO), the proposed method consumes less computational time, and provides a better stator resistance estimation dynamic performance. Both simulation and experimental results confirm the superiority of the proposed APFO scheme for a wide range of resistance variations from 0 to 100%.     

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.     

Stator resistance tuning in a stator-flux field-oriented driveusing an instantaneous hybrid flux estimator

A self-tuning control scheme for stator-flux field-oriented induction machine drives in electric vehicles operating over a wide speed range is discussed in this paper. The stator flux can be determined accurately from the terminal voltage when the machine is operating at high speed. However, at low speed, the stator resistance must be known to calculate the stator flux. The problem of calculating the stator flux accurately over the entire speed range is addressed. The rotor flux can be found from the machine speed and rotor time constant. The stator flux, at low speed, is then calculated directly from the rotor flux. By alternating between these two methods of determining the stator flux, a self-tuning operation is achieved, wherein the stator and rotor resistances are periodically updated. Since both methods of determining the stator flux are forced to track one another, a smooth transition between flux estimators is obtained. The torque and flux are then controlled in a deadbeat fashion. Good torque control over a wide speed range can therefore be obtained. With the proposed scheme, the advantages of direct torque control are obtained over the entire speed range with the addition of a speed sensor     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

基于MRAS的异步电机无速度传感器的矢量控制

由于电机定转子参数的变化,利用一般的转子磁链对转速进行估算,将导致不能得到准确的结果。这里采用积分型转子磁链的参考和可调模型构建出一个基于MRAS的异步电机无速度传感器的矢量控制模型。该模型提高了矢量控制系统的动态性能并利用MATLAB,sIMULINK进行了异步电机无速度传感器矢量控制系统的仿真,验证了文中所采用的模型参考自适应的速度估算方法的可行性以及对参数误差的鲁棒性。     

Unified sensorless vector control of synchronous and induction motors

In this paper, a unified theory for sensorless flux estimation and vector control of induction motors and nonsalient permanent-magnet synchronous motors (PMSMs) is developed. It is shown that an estimator and vector controller for one of the motor types can also be applied to the other, with only minor modifications necessary. Two candidate estimators are considered: a variant of the well-known "voltage model" (VM) and a phase-locked-loop-type speed and position estimator. These are applied to both motor types, and evaluated experimentally. For the nonsalient PMSM, an important result is that synchronization can be guaranteed from any initial rotor position.