Attitude estimation with a 9-axis MEMS based motion tracking sensor

Yan Wang, Rajesh Rajamani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper discusses the development of the attitude estimation algorithm for a MEMS based 9-axis motion tracking sensor, which includes a tri-axis accelerometer, a tri-axis gyroscope and a tri-axis magnetometer. The comparison between the Euler angles and the direction cosine matrix (DCM) based approach is presented to illustrate the advantage of DCM. It will be shown that the kinematic model for DCM can be transformed into a linear time-varying state space form, which greatly simplifies the development of the estimation algorithm. Different from the existing estimation algorithms, which incorporate a nonlinear kinematic model and the nonlinear Kalman filter, such as extended Kalman filter (EKF) or unscented Kalman filter (UKF), the nonlinearity in the kinematic model is not the trouble maker anymore. Hence, global convergence can always be guaranteed. Finally, the estimation algorithm is demonstrated by using the real measurement data collected from InvenSenses MPU9250, which is one of the most popular 9-axis motion tracking sensors in the market.

Original languageEnglish (US)
Title of host publicationMechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791850701
DOIs
StatePublished - Jan 1 2016
EventASME 2016 Dynamic Systems and Control Conference, DSCC 2016 - Minneapolis, United States
Duration: Oct 12 2016Oct 14 2016

Publication series

NameASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Volume2

Other

OtherASME 2016 Dynamic Systems and Control Conference, DSCC 2016
CountryUnited States
CityMinneapolis
Period10/12/1610/14/16

Fingerprint

MEMS
Kinematics
Sensors
Kalman filters
Control nonlinearities
Gyroscopes
Extended Kalman filters
Magnetometers
Accelerometers

Cite this

Wang, Y., & Rajamani, R. (2016). Attitude estimation with a 9-axis MEMS based motion tracking sensor. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2016-9700

Attitude estimation with a 9-axis MEMS based motion tracking sensor. / Wang, Yan; Rajamani, Rajesh.

Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers, 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, Y & Rajamani, R 2016, Attitude estimation with a 9-axis MEMS based motion tracking sensor. in Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. ASME 2016 Dynamic Systems and Control Conference, DSCC 2016, vol. 2, American Society of Mechanical Engineers, ASME 2016 Dynamic Systems and Control Conference, DSCC 2016, Minneapolis, United States, 10/12/16. https://doi.org/10.1115/DSCC2016-9700
Wang Y, Rajamani R. Attitude estimation with a 9-axis MEMS based motion tracking sensor. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers. 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016). https://doi.org/10.1115/DSCC2016-9700
Wang, Yan ; Rajamani, Rajesh. / Attitude estimation with a 9-axis MEMS based motion tracking sensor. Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers, 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016).
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