Research on Parameter Identification of Electromagnetic Actuator for Diesel Speed Governing

To identify parameters of electromagnetic actuators with analytical models, there are problems such as poor model accuracy, multiple physical fields coupling of model, and slow convergence. Based on error sensitivity numerical analysis, a parameter semi-analytical identification method for electromagnetic actuators is proposed. In this article, a diesel engine speed governing electromagnetic actuator is taken as the research object. First, with finite element method, a numerical simulation model of the electromagnetic actuator is established, and sensitivity of main geometrical parameters relative to electromagnetic force is analyzed. Secondly, with theoretical deduction, a difference model of the nominal and measurement electromagnetic force is built, and the electromagnetic actuators geometrical parameter identification formula is gotten. Thirdly, different numerical methods to construct a system error sensitivity matrix are compared, and the compared result is the accuracy of central difference better. Finally, the average static characteristics error of the electromagnetic actuator is reduced from 3.3174 to 1.0182. Therefore, the identification method is verified effective and feasible.

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A Calibration Approach for the Parallel Kinematics Machines Tools Based on Error Sensitivity Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.842.355 ◽

2013 ◽

Vol 842 ◽

pp. 355-362

Author(s):

Yong Shi ◽

Wen Tao Liu

Keyword(s):

Numerical Methods ◽

Structural Parameters ◽

Geometrical Parameters ◽

Parallel Kinematics ◽

Sensitivity Matrix ◽

Error Sensitivity ◽

Identification Method ◽

Parallel Kinematics Machines ◽

Error Sensitivity Analysis ◽

Spatial Locations

In order to identify the geometrical parameters of parallel kinematics machines tools (PKM), a new parameters identification method is presented. The identification method is proposed based on a pose discrepancy model, which is deduced from the error between the nominal and measurement relative distance of two different spatial locations of the moving platform. In the identification method, an error sensitivity matrix, which expresses the sensitivity between the pose error and geometrical structural parameters error of PKM, can be created with numerical methods. The results of different numerical methods are analyzed. A measurement method to get the precise lengths of legs is presented, which decrease the number of identified parameters. In an experiment, the error of PKM is reduced from 6.71mm to 1.144mm. Therefore, the identification method is verified effective and feasible.

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A Simplified Thermal Model and Online Temperature Estimation Method of Permanent Magnet Synchronous Motors

Applied Sciences ◽

10.3390/app9153158 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3158 ◽

Cited By ~ 3

Author(s):

Zhu ◽

Xiao ◽

Lu ◽

Wu ◽

Tao

Keyword(s):

Parameter Identification ◽

Permanent Magnet ◽

State Equation ◽

The State ◽

Geometrical Parameters ◽

Permanent Magnet Synchronous Motors ◽

Temperature Estimation ◽

Synchronous Motors ◽

Identification Method ◽

Estimation Scheme

Monitoring critical temperatures in permanent magnet synchronous motors is crucial for improving working reliability. Aiming at resolving the difficulty in online temperature estimation, an accurate and simple five-node lumped parameter thermal network (LPTN) is proposed and the mathematical model of the LPTN is built. Both radial and axial heat transfer paths inside the motor are considered to model the complete thermal circuit. In addition, an innovative parameter identification method based on multiple linear regression is applied to identify the parameters of the LPTN model. The parameters in the state equation are identified instead of the data of the motor, which are strongly dependent on the material and geometrical parameters. Finally, an open-loop estimation scheme based on the state equation and Kalman filter algorithm is adopted to predict the motor temperature online. The model performances are validated by extensive experiments under varying speed and torque conditions in terms of the accuracy and robustness. The results indicate that the temperature estimation error is within the range of ±5 °C in most cases and the proposed model can quickly follow the load variation. Besides, the online temperature estimation scheme and parameter identification method are easy and convenient to implement in an embedded system, which is feasible in automobile applications.

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