A comparative study of Kalman filtering for sensorless control of a permanent-magnet synchronous motor drive

Author name used in this publication: Borsje, P.Author name used in this publication: Wong, Y. K.Author name used in this publication: Ho, S. L.Refereed conference paper2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Industrial applications of the Kalman filter:a review

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Sensorless Pedalling Torque Estimation Based on Motor Load Torque Observation for Electrically Assisted Bicycles

The need for reducing the cost of and space in Electrically Assisted Bicycles (EABs) has led the research to the development of solutions able to sense the applied pedalling torque and to provide a suitable electrical assistance avoiding the installation of torque sensors. Among these approaches, this paper proposes a novel method for the estimation of the pedalling torque starting from an estimation of the motor load torque given by a Load Torque Observer (LTO) and evaluating the environmental disturbances that act on the vehicle longitudinal dynamics. Moreover, this work shows the robustness of this approach to rotor position estimation errors introduced when sensorless techniques are used to control the motor. Therefore, this method allows removing also position sensors leading to an additional cost and space reduction. After a mathematical description of the vehicle longitudinal dynamics, this work proposes a state observer capable of estimating the applied pedalling torque. The theory is validated by means of experimental results performed on a bicycle under different conditions and exploiting the Direct Flux Control (DFC) sensorless technique to obtain the rotor position information. Afterwards, the identification of the system parameters together with the tuning of the control system and of the LTO required for the validation of the proposed theory are thoroughly described. Finally, the capabilities of the state observer of estimating an applied pedalling torque and of recognizing the application of external disturbance torques to the motor is verified

Speed and position estimator of for sensorless PMSM drives using adaptive controller

Nowadays, the elimination of the speed sensor in Permanent Magnet Synchronous Machine (PMSM) is greatly recommended to increase efficiency and reduce the cost of the drives. This paper proposes a simple estimator for speed and rotor position of PMSM drives using adaptive controller. The novelties of the proposed method are the simple estimator equations and the absence of the voltage probe which depend on direct and quadrature reference current only. The simplified mathematical model of the PMSM is formulated by using V-I model, based on adaptive control. Then, the speed estimation error of the voltage and current model based are analyzed. Thus, an adaptation mechanism model is established to cancel the error of the measured and estimated d-q currents. Since the output of the estimator is the position feedback, the performances of speed responses are presented. The hardware implementation of proposed sensorless drives is realized via dSPACE DS11103 panel. dSPACE Real Time Implementation (RTI) is the linkage between software and hardware set-up. It automatically processes the MATLAB Simulink model into dSPACE DS11103 processor. The experimental-hardware results demonstrate that the speed and position estimator of the proposed method is able to control the PMSM drives for forward and reverse of speed command, acceleration, deceleration and robustness to load disturbanc

Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures.Comment: 13 pages, 33 figure

Surface Permanent Magnet Synchronous Motors’ Passive Sensorless Control: A Review

Sensorless control of permanent magnet synchronous motors is nowadays used in many industrial, home and traction applications, as it allows the presence of a position sensor to be avoided with benefits for the cost and reliability of the drive. An estimation of the rotor position is required to perform the field-oriented control (FOC), which is the most common control scheme used for this type of motor. Many algorithms have been developed for this purpose, which use different techniques to derive the rotor angle from the stator voltages and currents. Among them, the so-called passive methods have gained increasing interest as they do not introduce additional losses and current distortion associated instead with algorithms based on the injection of high-frequency signals. The aim of this paper is to present a review of the main passive sensorless methods proposed in the technical literature over the last few years, analyzing their main features and principles of operation. An experimental comparison among the most promising passive sensorless algorithms is then reported, focusing on their performance in the low-speed operating region

Performance Comparison of Two Estimators for Two-Phase Permanent Magnet Synchronous Motor

This paper presents and compares the performance of two Kalman filter schemes, the discrete extended Kalman filter (EKF) and unscented Kalman filter (UKF) for estimating the states (winding currents, rotor speed and rotor angular position) of two-phase Permanent Magnet Synchronous Motor (PMSM). Estimating the states of the system is performed by propagating the mean and covariance of the state distribution. For linear systems, the general recursive Kalman filter algorithm based on MMSE (minimum mean squared error) is the straightforward estimation technique to be implemented. For nonlinear systems, extended Kalman filter (EKF) is considered to be the best nonlinear estimator. The EKF is based on linearizing the state and output equations at every sampling instant. Therefore, this estimator requires continuously computation of the Jacobian matrix. The unscented Kalman filter (UKF) is based on implementation of the unscented transformation (UT) to the nonlinear state distribution (motor model). The UT uses the intuition that it is easier to approximate a probability distribution than it is to approximate an arbitrary nonlinear function or transformation. Apply this intuition to motor model, a set or cloud of points are generated around each state of motor model with specified sample mean and sample covariance. The nonlinear function (PMSM model) is applied to each of these points in turn to yield a transformed sample, and the predicted mean and covariance are calculated from the transformed sample. Based on predicted mean and covariance the UKF recursive algorithm can be developed. The performance  comparisons are based on standard deviation estimation errors of both estimators and the time computation effort required execute the algorithms of both filters. The simulated results show that the UKF gives best estimates at motor low speed, while its estimation performance degrade at high motor speed. On the other hand, the EKF shows bad estimation characteristics at low frequency and it yields good estimates at high source frequency. However,, the EKF algorithm keeps lower time computation effort over wide range of rotor speed than that required to execute the UKF software for the same range of source frequency. The PMSM motor model and the algorithms of both filters are built in Matlab package using S-function capability and scalar control strategy are used to account for constant stator magnetizing flu