Observer Based Speed Control of PMSM Servo Systems

2011 ◽  
Vol 121-126 ◽  
pp. 3376-3380
Author(s):  
R.N. Huang ◽  
Cheng Song ◽  
Yun Jiang Lou
Keyword(s):  
Kalman Filter ◽  
State Observer ◽  
Detection Accuracy ◽  
Electromagnetic Torque ◽  
Load Torque ◽  
Servo Systems ◽  
Position Information ◽  
Simulation Results ◽  
Instantaneous Speed ◽  
Order State

A speed observer which is consisted of the full order state observer and Kalman filter was designed to estimate instantaneous speed in this paper. It utilized full order state observer to obtain load torque and position information, and combining with electromagnetic torque as the input of Kalman filter. Simulation results show that this method can improve the speed detection accuracy of the PMSM servo system.

Research on Speed Observer of PMSM Servo System

2011 ◽  
Vol 317-319 ◽  
pp. 1223-1227
Author(s):  
Rui Ning Huang ◽  
Cheng Song ◽  
Xiao Hui Liu ◽  
Yun Jiang Lou
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Servo System ◽  
Detection Accuracy ◽  
Optical Encoder ◽  
Average Speed ◽  
Speed Range ◽  
Simulation Results ◽  
Instantaneous Speed ◽  
Speed Simulation

The incremental optical encoder is widely used in PMSM servo system for speed detection. However, this method is detected the average speed, which will cause delay time, and make the speed control system unstable at low speed range. A speed observer was designed in this paper, which is combining state observer and Kalman filter to estimate instantaneous speed. Simulation results show that this method can improve the speed detection accuracy of the PMSM servo system.

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

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 88
Author(s):  
Riccardo Mandriota ◽  
Stefano Fabbri ◽  
Matthias Nienhaus ◽  
Emanuele Grasso
Keyword(s):  
External Disturbance ◽  
State Observer ◽  
Position Estimation ◽  
Load Torque ◽  
Estimation Errors ◽  
Position Information ◽  
Longitudinal Dynamics ◽  
Rotor Position ◽  
Electrically Assisted ◽  
Motor Load

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.

scholarly journals High-performance Load Torque Compensation of Industrial Robot using Kalman-filter-based Instantaneous State Observer

2015 ◽  
Vol 4 (5) ◽  
pp. 589-590 ◽  
Author(s):  
Takashi Yoshioka ◽  
Thao Tran Phuong ◽  
Akinori Yabuki ◽  
Kiyoshi Ohishi ◽  
Toshimasa Miyazaki ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Performance ◽  
Industrial Robot ◽  
State Observer ◽  
Load Torque

High-Performance Speed Sensorless Direct Torque Control of PMSM

2011 ◽  
Vol 130-134 ◽  
pp. 2828-2831 ◽  
Author(s):  
Yan Ping Xu ◽  
Ke Guo ◽  
Yan Ru Zhong
Keyword(s):  
Kalman Filter ◽  
High Performance ◽  
Permanent Magnet Synchronous Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Flux Linkage ◽  
Speed Sensorless ◽  
Load Torque ◽  
Simulation Results ◽  
Stator Flux

A high-performance speed sensorless direct torque control (DTC) system of permanent magnet synchronous motor (PMSM) is presented in this paper. The stator flux linkage, speed, rotor position and load torque of PMSM are observed using a fourth-order Extended Kalman Filter (EKF) and a second-order Kalman Filter (KF) and the observed load torque is used for feed-forward compensation of reference torque. Simulation results clearly demonstrate the performance of speed can be improved when load torque is changed and the validity of the proposed control strategy.

State estimation in fractional-order systems with coloured measurement noise

2017 ◽  
Vol 40 (6) ◽  
pp. 1819-1835 ◽  
Author(s):  
Behrouz Safarinejadian ◽  
Nasrin Kianpour ◽  
Mojtaba Asad
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Fractional Order ◽  
Estimation Error ◽  
Measurement Noise ◽  
Space Systems ◽  
Fractional Order Systems ◽  
State Space Systems ◽  
Simulation Results ◽  
Order State

This paper presents new estimation methods for discrete fractional-order state-space systems with coloured measurement noise. A novel approach is proposed to convert a fractional system with coloured measurement noise to a system with white measurement noise in which the process and measurement noises are correlated with each other. In this paper, two new Kalman filter algorithms for fractional-order linear state-space systems with coloured measurement noise, as well as a new extended Kalman filter algorithm for state estimation in nonlinear fractional-order state-space systems with coloured measurement noise, are proposed. The accuracy of the equations and relations is confirmed in several theorems. The validity and effectiveness of the proposed algorithms are verified by simulation results and compared with previous work. Results show that for linear and nonlinear fractional-order systems with coloured noise, the proposed methods are more accurate than conventional methods regarding estimation error and estimation error covariance. Simulation results demonstrate that the proposed algorithms can accurately perform estimation in fractional-order systems with coloured measurement noise.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

Gas pipeline leakage detection in the presence of parameter uncertainty using robust extended Kalman filter

2021 ◽  
pp. 014233122198911
Author(s):  
Mohadese Jahanian ◽  
Amin Ramezani ◽  
Ali Moarefianpour ◽  
Mahdi Aliari Shouredeli
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Parameter Uncertainty ◽  
Oil And Gas ◽  
Estimation Error ◽  
Real Data ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Parameter Uncertainties ◽  
Simulation Results

One of the most significant systems that can be expressed by partial differential equations (PDEs) is the transmission pipeline system. To avoid the accidents that originated from oil and gas pipeline leakage, the exact location and quantity of leakage are required to be recognized. The designed goal is a leakage diagnosis based on the system model and the use of real data provided by transmission line systems. Nonlinear equations of the system have been extracted employing continuity and momentum equations. In this paper, the extended Kalman filter (EKF) is used to detect and locate the leakage and to attenuate the negative effects of measurement and process noises. Besides, a robust extended Kalman filter (REKF) is applied to compensate for the effect of parameter uncertainty. The quantity and the location of the occurred leakage are estimated along the pipeline. Simulation results show that REKF has better estimations of the leak and its location as compared with that of EKF. This filter is robust against process noise, measurement noise, parameter uncertainties, and guarantees a higher limit for the covariance of state estimation error as well. It is remarkable that simulation results are evaluated by OLGA software.

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