Model-free multi-variable learning control of a five axis nanopositioning stage

Title: Model-free multi-variable learning control of a five axis nanopositioning stage
Creator: Sieswerda, Thijs; Fleming, Andrew J.; Oomen, Tom
Relation: 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (Delft, Netherlands 12-16 July, 2021) p. 1190-1194
Publisher Link: http://dx.doi.org/10.1109/AIM46487.2021.9517342
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2021
Description: This article compares the performance of recently introduced learning control methods on a 5-axis nanopositioning stage. Of these methods, the Smoothed Model-Free Inversion-based Iterative Control (SMF-IIC) method requires no modeling effort for effective tracking of repetitive trajectories and is readily applicable to multi-variable systems. Experimental results show that the tracking performance of the SMF-IIC method is similar to traditional learning control methods when applied to a single axis of the nanopositioning stage. The SMF-IIC method is also found to be effective for reference tracking of two axes simultaneously.
Subject: atomic force microscopy; mechatronics; nanopositioning; transient analysis; trajectory; iterative methods
Identifier: http://hdl.handle.net/1959.13/1467759
Identifier: uon:47897
Identifier: ISBN:9781665441391
Language: eng
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