Motion control for magnetic micro-scale manipulation

This work demonstrates performance improvement in motion control under a particular set of machine system constraints. A high performance industrial magnetic micro-manipulation system, the Minimag, is introduced and modeled with both first principles and system identification. The form of the closed...

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Main Authors: Alleyne, Andrew G., Schurle, Simone, Meo, Alessandro, Nelson, Bradley J.
Format: Conference Proceeding
Language:English
Subjects:
Feedforward neural networks
Force
Magnetic fields
Magnetic flux
Mathematical model
Trajectory
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creator Alleyne, Andrew G.
Schurle, Simone
Meo, Alessandro
Nelson, Bradley J.
description This work demonstrates performance improvement in motion control under a particular set of machine system constraints. A high performance industrial magnetic micro-manipulation system, the Minimag, is introduced and modeled with both first principles and system identification. The form of the closed loop controller is constrained by operational bounds and the system software, resulting in limits to achievable performance. A model-based motion control enhancement is developed and implemented using tools from Iterative Learning Control. The resulting performance improvements indicate the benefits of motion control even when the closed loop controller is fixed. Experimental results at two different size scales (500 μm and 4.5μm) are given.
doi_str_mv 10.23919/ECC.2013.6669173
format conference_proceeding
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subjects Feedforward neural networks
Force
Magnetic fields
Magnetic flux
Mathematical model
Trajectory
title Motion control for magnetic micro-scale manipulation
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