Optimized Kalman filters for sensorless vector control induction motor drives

This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions

On the stator flux linkage estimation of an PMSM with extended Kalman filters

The demand for drives with high quality torque control has grown tremendously in a wide variety of applications. Direct torque control (DTC) for permanent magnet synchronous motors can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled. As such the estimation of the stator flux linkage is essential. In this paper the performance of the Extended Kalman Filter (EKF) for stator flux linkage estimation is studied. Starting from a formulation of the EKF for isotropic motors, the influence of rotor anisotropy and saturation is evaluated. Subsequently it is expanded to highly isotropic motors as well. In both cases the possibilities to add parameter estimations are evaluated

Sensorless Control of Electric Motors with Kalman Filters: Applications to Robotic and Industrial Systems

The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. First the case of a DC motor is considered and Kalman Filter-based control is implemented. Next the nonlinear model of a field-oriented induction motor is examined and the motor's angular velocity is estimated by an Extended Kalman Filter which processes measurements of the rotor's angle. Sensorless control of the induction motor is again implemented through feedback of the estimated state vector. Additionally, a state estimation-based control loop is implemented using the Unscented Kalman Filter. Moreover, state estimation-based control is developed for the induction motor model using a nonlinear flatness-based controller and the state estimation that is provided by the Extended Kalman Filter. Unlike field oriented control, in the latter approach there is no assumption about decoupling between the rotor speed dynamics and the magnetic flux dynamics. The efficiency of the Kalman Filter-based control schemes, for both the DC and induction motor models, is evaluated through simulation experiments

Industrial applications of the Kalman filter:a review

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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

Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

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

Machine model based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: A Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space