Speed measurement error reduction via adaptive strong tracking Kalman filters

Zirui Liu; Ming Yang; Jiang Long; Dianguo Xu

doi:10.1109/ITEC-AP.2017.8080941

Speed measurement error reduction via adaptive strong tracking Kalman filters

Zirui Liu
, Ming Yang
, Jiang Long
, Dianguo Xu

School of Electrical Engineering and Automation

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper proposes an adaptive strong tracking Kalman filters (ASTKF) for reducing the measurement deviation of speed and improving the tracking ability of the observer. The proposed ASTKF can limit the tracking mismatch caused by low- resolution encoders and track the load torque better in real time. The proposed ASTKF introduces a suboptimal scaling factor to the gain matrix and calculating the system noise matrix at the current time. The simulations illustrate the ASTKF can precisely obtain the information of both speed and load torque. And it can achieve smaller measurement deviation and faster response.

Original language	English
Title of host publication	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538628942
DOIs	https://doi.org/10.1109/ITEC-AP.2017.8080941
State	Published - 23 Oct 2017
Event	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017 - Harbin, China Duration: 7 Aug 2017 → 10 Aug 2017

Publication series

Name	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

Conference

Conference	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017
Country/Territory	China
City	Harbin
Period	7/08/17 → 10/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Kalman filter
Measurement noise
Speed estimation

Access to Document

10.1109/ITEC-AP.2017.8080941

Cite this

Liu, Z., Yang, M., Long, J., & Xu, D. (2017). Speed measurement error reduction via adaptive strong tracking Kalman filters. In 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017 Article 8080941 (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITEC-AP.2017.8080941

Liu, Zirui ; Yang, Ming ; Long, Jiang et al. / Speed measurement error reduction via adaptive strong tracking Kalman filters. 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017).

@inproceedings{5562abbd2ba5451c9c503d1462a53e4a,

title = "Speed measurement error reduction via adaptive strong tracking Kalman filters",

abstract = "This paper proposes an adaptive strong tracking Kalman filters (ASTKF) for reducing the measurement deviation of speed and improving the tracking ability of the observer. The proposed ASTKF can limit the tracking mismatch caused by low- resolution encoders and track the load torque better in real time. The proposed ASTKF introduces a suboptimal scaling factor to the gain matrix and calculating the system noise matrix at the current time. The simulations illustrate the ASTKF can precisely obtain the information of both speed and load torque. And it can achieve smaller measurement deviation and faster response.",

keywords = "Kalman filter, Measurement noise, Speed estimation",

author = "Zirui Liu and Ming Yang and Jiang Long and Dianguo Xu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017 ; Conference date: 07-08-2017 Through 10-08-2017",

year = "2017",

month = oct,

day = "23",

doi = "10.1109/ITEC-AP.2017.8080941",

language = "英语",

series = "2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017",

address = "美国",

}

Liu, Z, Yang, M, Long, J & Xu, D 2017, Speed measurement error reduction via adaptive strong tracking Kalman filters. in 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017., 8080941, 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017, Harbin, China, 7/08/17. https://doi.org/10.1109/ITEC-AP.2017.8080941

Speed measurement error reduction via adaptive strong tracking Kalman filters. / Liu, Zirui; Yang, Ming; Long, Jiang et al.
2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8080941 (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Speed measurement error reduction via adaptive strong tracking Kalman filters

AU - Liu, Zirui

AU - Yang, Ming

AU - Long, Jiang

AU - Xu, Dianguo

PY - 2017/10/23

Y1 - 2017/10/23

N2 - This paper proposes an adaptive strong tracking Kalman filters (ASTKF) for reducing the measurement deviation of speed and improving the tracking ability of the observer. The proposed ASTKF can limit the tracking mismatch caused by low- resolution encoders and track the load torque better in real time. The proposed ASTKF introduces a suboptimal scaling factor to the gain matrix and calculating the system noise matrix at the current time. The simulations illustrate the ASTKF can precisely obtain the information of both speed and load torque. And it can achieve smaller measurement deviation and faster response.

AB - This paper proposes an adaptive strong tracking Kalman filters (ASTKF) for reducing the measurement deviation of speed and improving the tracking ability of the observer. The proposed ASTKF can limit the tracking mismatch caused by low- resolution encoders and track the load torque better in real time. The proposed ASTKF introduces a suboptimal scaling factor to the gain matrix and calculating the system noise matrix at the current time. The simulations illustrate the ASTKF can precisely obtain the information of both speed and load torque. And it can achieve smaller measurement deviation and faster response.

KW - Kalman filter

KW - Measurement noise

KW - Speed estimation

UR - https://www.scopus.com/pages/publications/85040105909

U2 - 10.1109/ITEC-AP.2017.8080941

DO - 10.1109/ITEC-AP.2017.8080941

M3 - 会议稿件

AN - SCOPUS:85040105909

T3 - 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

BT - 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

Y2 - 7 August 2017 through 10 August 2017

ER -

Liu Z, Yang M, Long J, Xu D. Speed measurement error reduction via adaptive strong tracking Kalman filters. In 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8080941. (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017). doi: 10.1109/ITEC-AP.2017.8080941

Speed measurement error reduction via adaptive strong tracking Kalman filters

Abstract

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UN SDGs

Keywords

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