Position Estimation Error Ripple Elimination for Model-Based Method

2019 ◽  
pp. 147-202
Author(s):  
Gaolin Wang ◽  
Guoqiang Zhang ◽  
Dianguo Xu
Keyword(s):  
Estimation Error ◽  
Position Estimation ◽  
Model Based

scholarly journals Performance Analysis of a Full Order Sensorless Control Adaptive Observer for Doubly-Fed Induction Generator in Grid Connected Operation

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1254
Author(s):  
Gianluca Brando ◽  
Adolfo Dannier ◽  
Ivan Spina
Keyword(s):  
Sensorless Control ◽  
Estimation Error ◽  
Position Estimation ◽  
Adaptive Observer ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Rotor Position ◽  
Stator Flux ◽  
Doubly Fed ◽  
Rotor Position Estimation

This paper focuses on the performance analysis of a sensorless control for a Doubly Fed Induction Generator (DFIG) in grid-connected operation for turbine-based wind generation systems. With reference to a conventional stator flux based Field Oriented Control (FOC), a full-order adaptive observer is implemented and a criterion to calculate the observer gain matrix is provided. The observer provides the estimated stator flux and an estimation of the rotor position is also obtained through the measurements of stator and rotor phase currents. Due to parameter inaccuracy, the rotor position estimation is affected by an error. As a novelty of the discussed approach, the rotor position estimation error is considered as an additional machine parameter, and an error tracking procedure is envisioned in order to track the DFIG rotor position with better accuracy. In particular, an adaptive law based on the Lyapunov theory is implemented for the tracking of the rotor position estimation error, and a current injection strategy is developed in order to ensure the necessary tracking sensitivity around zero rotor voltages. The roughly evaluated rotor position can be corrected by means of the tracked rotor position estimation error, so that the corrected rotor position is sent to the FOC for the necessary rotating coordinate transformation. An extensive experimental analysis is carried out on an 11 kW, 4 poles, 400 V/50 Hz induction machine testifying the quality of the sensorless control.

Sensorless Control of Switched Reluctance Motors Based on Pulse Injection, Position Sectors Divided and Variable Thresholds

2014 ◽  
Vol 989-994 ◽  
pp. 3252-3257
Author(s):  
Zi Fei Jia ◽  
De An Zhao ◽  
Yu Yan Zhao
Keyword(s):  
Control Method ◽  
Estimation Error ◽  
Position Estimation ◽  
Switched Reluctance Motors ◽  
Switched Reluctance ◽  
Variable Threshold ◽  
Pulse Injection ◽  
Angle Section ◽  
Two Phases ◽  
Rotor Position Estimation

To realize starting operation and reduce position estimation error of switched reluctance motor (SRM) without position sensor, a novel control method based on pulse injection, divided angle section and variable threshold is presented. The starting operation of SRM can be accomplished by injecting high frequency pulse and judging position sectors. Variable threshold is used to reduce position estimation error. The value of threshold is obtained by looking up table prestored in controller. The method avoids complicated mathematical model and is suitable for starting operation with two phases. Besides, rotor position estimation error of this method is analyzed and the method which can decreased the error is proposed. At last, the experiment has been done to verify the performance of the control method.

Prototyping Sensor Network System for Automatic Vital Signs Collection

2013 ◽  
Vol 52 (03) ◽  
pp. 239-249 ◽  
Author(s):  
H. Noma ◽  
C. Naito ◽  
M. Tada ◽  
H. Yamanaka ◽  
T. Takemura ◽  
...  
Keyword(s):  
Sensor Network ◽  
Vital Sign ◽  
Paired Comparison ◽  
Estimation Error ◽  
Vital Signs ◽  
Position Estimation ◽  
University Hospital ◽  
Network System ◽  
Sensing System ◽  
Proximity Sensing

SummaryObjective: Development of a clinical sensor network system that automatically collects vital sign and its supplemental data, and evaluation the effect of automatic vital sensor value assignment to patients based on locations of sensors.Methods: The sensor network estimates the data-source, a target patient, from the position of a vital sign sensor obtained from a newly developed proximity sensing system. The proximity sensing system estimates the positions of the devices using a Bluetooth inquiry process. Using Bluetooth access points and the positioning system newly developed in this project, the sensor network collects vital sign and its 4W (who, where, what, and when) supplemental data from any Blue-tooth ready vital sign sensors such as Continua-ready devices. The prototype was evaluated in a pseudo clinical setting at Kyoto University Hospital using a cyclic paired comparison and statistical analysis.Results: The result of the cyclic paired analysis shows the subjects evaluated the proposed system is more effective and safer than POCS as well as paper-based operation. It halves the times for vital signs input and eliminates input errors. On the other hand, the prototype failed in its position estimation for 12.6% of all attempts, and the nurses overlooked half of the errors. A detailed investigation clears that an advanced interface to show the system’s “confidence”, i.e. the probability of estimation error, must be effective to reduce the oversights.Conclusions: This paper proposed a clinical sensor network system that relieves nurses from vital signs input tasks. The result clearly shows that the proposed system increases the efficiency and safety of the nursing process both subjectively and objectively. It is a step toward new generation of point of nursing care systems where sensors take over the tasks of data input from the nurses.

Model-based Head Orientation Estimation for Smart Devices

2021 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Qiang Yang ◽  
Yuanqing Zheng
Keyword(s):  
Radiation Pattern ◽  
Microphone Array ◽  
Estimation Error ◽  
Microphone Arrays ◽  
Smart Devices ◽  
Head Orientation ◽  
Context Information ◽  
Orientation Estimation ◽  
Model Based ◽  
The Voice

Voice interaction is friendly and convenient for users. Smart devices such as Amazon Echo allow users to interact with them by voice commands and become increasingly popular in our daily life. In recent years, research works focus on using the microphone array built in smart devices to localize the user's position, which adds additional context information to voice commands. In contrast, few works explore the user's head orientation, which also contains useful context information. For example, when a user says, "turn on the light", the head orientation could infer which light the user is referring to. Existing model-based works require a large number of microphone arrays to form an array network, while machine learning-based approaches need laborious data collection and training workload. The high deployment/usage cost of these methods is unfriendly to users. In this paper, we propose HOE, a model-based system that enables Head Orientation Estimation for smart devices with only two microphone arrays, which requires a lower training overhead than previous approaches. HOE first estimates the user's head orientation candidates by measuring the voice energy radiation pattern. Then, the voice frequency radiation pattern is leveraged to obtain the final result. Real-world experiments are conducted, and the results show that HOE can achieve a median estimation error of 23 degrees. To the best of our knowledge, HOE is the first model-based attempt to estimate the head orientation by only two microphone arrays without the arduous data training overhead.

scholarly journals Online Identification of Intrinsic Load Current Dependent Position Estimation Error for Sensorless PMSM Drives

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 163186-163196
Author(s):  
Hechao Wang ◽  
Kaiyuan Lu ◽  
Dong Wang ◽  
Frede Blaabjerg
Keyword(s):  
Estimation Error ◽  
Position Estimation ◽  
Load Current ◽  
Online Identification ◽  
Dependent Position
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