Adaptive dipole model based disturbance compensation in nonlinear magnetic position systems

Ryan Madson, Rajesh Rajamani

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The nonlinear magnetic model of an oscillating ferromagnetic object can be used for accurate real-time estimation of its position. This is useful for piston position estimation in a number of automation and performance improvement applications involving hydraulic actuators, pneumatic cylinders, and internal combustion engines. A significant challenge to magnetic field based position estimation comes from disturbances due to unexpected ferromagnetic objects coming close to the sensors. This paper develops a new disturbance estimation method based on modeling the magnetic disturbance as a dipole with unknown location, magnitude, and orientation. A truncated interval unscented Kalman filter is used to estimate all the parameters of this unknown dipole, in addition to estimating piston position from nonlinear magnetic field models. Experimental data from a pneumatic actuator are used to verify the performance of the developed estimator. Experimental results show that the developed estimator is significantly superior to a linear magnetic field model based disturbance estimator. It can reliably estimate piston position and the unknown dipole parameters in the presence of a variety of unknown disturbances.

Original languageEnglish (US)
Article number7820214
Pages (from-to)794-803
Number of pages10
JournalIEEE/ASME Transactions on Mechatronics
Volume22
Issue number2
DOIs
StatePublished - Apr 2017

Bibliographical note

Funding Information:
This work was supported in part by the MnDRIVE Robotics, Sensors, and Manufacturing Program at the University of Minnesota, and in part by the National Science Foundation under AIR-TT Grant 1601644.

Keywords

  • Magnetic fields
  • position measurement
  • state estimation

Fingerprint

Dive into the research topics of 'Adaptive dipole model based disturbance compensation in nonlinear magnetic position systems'. Together they form a unique fingerprint.

Cite this