Model-based real-time control of a magnetic manipulator system

Conference paper (2017)

Authors

S.P. Nageshrao Ford Motor Company, Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
R. Babuska Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Steel Mathematical model Force Manipulators Coils Position measurement
More Info
expand_more

Published Date

2017

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Precise magnetic manipulation has numerous applications, ranging from manufacturing to the medical field. Owing to the nonlinear nature of the electromagnetic force, magnetic manipulation requires advanced nonlinear control. In this paper, we design and experimentally evaluate two nonlinear controllers for a magnetic manipulation (Magman) system, which consists of four electromagnetic coils arranged linearly. The current through the coils is controlled in order to accurately position a steel ball, rolling freely in a track above the coils. We benchmark two nonlinear control methods, namely feedback linearization and a constrained state-dependent Riccati equation (SDRE) control. These methods are chosen due to their widespread use in academia as well as industrial applications. On the actual setup, constrained SDRE has performed considerably better in terms of the settling time, overshoot, and the amount of control effort when compared to feedback linearization.

Files

08264140.pdf
(.pdf | 0.79 Mb)