Speed -Sensorless Estimation And Position Control Of Induction Motors For Motion Control Applications

Thesis (Ph.D.) University of Alaska Fairbanks, 2006High performance sensorless position control of induction motors (IMs) calls for estimation and control schemes which offer solutions to parameter uncertainties as well as to difficulties involved with accurate flux and velocity estimation at very low and zero speed. In this thesis, novel control and estimation methods have been developed to address these challenges. The proposed estimation algorithms are designed to minimize estimation error in both transient and steady-state over a wide velocity range, including very low and persistent zero speed operation. To this aim, initially single Extended Kalman Filter (EKF) algorithms are designed to estimate the flux, load torque, and velocity, as well as the rotor, Rr' or stator, Rs resistances. The temperature and frequency related variations of these parameters are well-known challenges in the estimation and control of IMs, and are subject to ongoing research. To further improve estimation and control performance in this thesis, a novel EKF approach is also developed which can achieve the simultaneous estimation of R r' and Rs for the first time in the sensorless IM control literature. The so-called Switching and Braided EKF algorithms are tested through experiments conducted under challenging parameter variations over a wide speed range, including under persistent operation at zero speed. Finally, in this thesis, a sensorless position control method is also designed using a new sliding mode controller (SMC) with reduced chattering. The results obtained with the proposed control and estimation schemes appear to be very compatible and many times superior to existing literature results for sensorless control of IMs in the very low and zero speed range. The developed estimation and control schemes could also be used with a variety of the sensorless speed and position control applications, which are challenged by a high number of parameter uncertainties

SDTC-EKF Control of an Induction Motor Based Electric Vehicle

International audienceThis paper presents the experimental implementation of sensorless direct torque control of an induction motor based electric vehicle. In this case, stator flux and rotational speed estimations are achieved using an extended Kalman filter. Experimental results on a test vehicle propelled by a 1-kW induction motor seem to indicate that the proposed scheme is a good candidate for an electric vehicle control

High performance speed control of single-phase induction motors using switching forward and backward EKF strategy

The aim of this research is to provide a high performance vector control of single-phase Induction Motor (IM) drives. It is shown that in the rotating reference frame, the single-phase IM equations can be separated into forward and backward equations with the balanced structure. Based on this, a method for vector control of the single-phase IM, using two modified Rotor Field- Oriented Control (RFOC) algorithms is presented. In order to accommodate forward and backward rotor fluxes in the presented controller, an Extended Kalman Filter (EKF) with two different forward and backward currents that are switched interchangeably (switching forward and backward EKF), is proposed. Simulation results illustrate the effectiveness of the proposed algorithm

Industrial applications of the Kalman filter:a review

International audienc

Optimal speed and torque estimations for improving the DTC dynamic performance of induction machines

High-performance AC drives require accurate speed, flux, and torque estimations to provide a proper system operation. Thus, this thesis proposes a robust observer, i.e. Extended Kalman Filter (EKF), to offer optimal estimations of these components in order to improve the dynamic performance of Direct Torque Control (DTC) of induction motor drives. The selection and quality of EKF covariance elements have a considerable bearing on the effectiveness of motor drives. Many EKF-based optimization techniques involve only a single objective for the optimal estimation of speed without giving concern to the other variables. In addition, the optimization is performed on a complicated EKF structure. Nevertheless, in this study, both speed and torque are concurrently estimated. The work presents a new method to investigate the selection of EKF filters by using a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) developed for resolving problems with multiobjectives. Filter element selection is the process of improving the concurrent estimation of speed and torque in order to increase EKF accuracy and allow higher drive efficiency. The proposed multi-optimal EKF-based estimation observer is used in combination with the sensorless direct torque control of induction motor. The investigated results for the multi-objective optimization indicate that the speed optimization gives superior performance when compared to the optimal torque. Owing to the large computation time of EKF algorithm, it increases the sampling time of DTC which leads to an increase in the motor torque ripples. The thesis proposes a Constant Frequency Torque Controller (CFTC) to replace the hysteresis torque controller that offers constant switching frequency and reduces torque ripples. Moreover, the CFTC has the capability of continuous switching regardless of speed variation; hence, leading to a consistent rotation of flux. Consequently, improvement on speed estimation, particularly at low and zero speed regions is accomplished and enhancement on the dynamic performance of torque is achieved when the reference speed change is applied from 0 rad/s, on the condition that the EKF observer is accurately optimized. To verify the improvements of the proposed methods, simulation and experimentation as well as comparison with the EKF-based DTC with the hysteresis controller are carried out

Stator Resistance Estimation Using Adaptive Estimation via a Bank of Kalman Filters

Accurate and efficient control of electric motors is dependent on knowledge of motor parameters such as the resistance and the inductance of the winding. However, these parameters are often unavailable to the control designer because they are dependent on the motor design and may change due to environmental effects such as temperature. An accurate real-time method to determine the values of these unknown parameters can improve motor performance over the entire operating range. In this work, a parameter estimation technique based on a bank of Kalman filters is used to adaptively estimate the motor winding resistance. Simulation results for a 3.5 horsepower interior permanent magnet (IPM) synchronous motor operating at rated torque demonstrate that this technique may be used for real-time estimation of motor parameters

Comparative Analysis of Estimation Techniques of SFOC Induction Motor for Electric Vehicles

International audienceThis paper presents system analysis, modeling and simulation of an electric vehicle with different sensorless control techniques. Indeed, sensorless control is considered to be a lower cost alternative than the position or speed encoder-based control of induction motors for an electric vehicle. Two popular sensorless control methods, namely, the Luenberger observer and the Kalman filter methods are compared regarding speed and torque control characteristics. They are also compared against the well-known model reference adaptive system. Simulations on a test vehicle propelled by 37-kW induction motor lead to very interesting comparison results

Field Oriented Controlled Speed Sensorless Control of Induction Motors

In this paper, a special class of adaptive control system, model reference adaptive controller (MRAC) for the speed estimation of the field oriented controlled (FOC) induction motor drive is presented. The proposed MRAC is formed using instantaneous and steady-state values of tuning signal insynchronously rotating reference frame, which is a fictitious quantity and has no physical significance. Requirement of no additional sensors makes the drive suitable for retrofit applications. The proposed MRAC-based speed sensorless field oriented controlled induction motor drive estimation technique has been simulated in MATLAB/SIMULIN

Speed Sensorless vector control of parallel-connected three-phase two-motor single-inverter drive system

This paper presents the characteristic behavior of direct vector control of two induction motors with sensorless speed feedback having the same rating parameters, paralleled combination, and supplied from a single current-controlled pulse-width-modulated voltage-source inverter drive. Natural observer design technique is known for its simple construction, which estimates the speed and rotor fluxes. Load torque is estimated by load torque adaptation and the average rotor flux was maintained constant by rotor flux feedback control. The technique’s convergence rate is very fast and is robust to noise and parameter uncertainty. The gain matrix is absent in the natural observer. The rotor speed is estimated from the load torque, stator current, and rotor flux. Under symmetrical load conditions, the difference in speed between two induction motors is reduced by considering the motor parameters as average and difference. Rotor flux is maintained constant by the rotor flux control scheme with feedback, and the estimation of rotor angle is carried out by the direct vector control technique. Both balanced and unbalanced load conditions are investigated for the proposed AC motor drive system. Experimental results presented in this paper show good agreement with the theoretical formulations