Learning position controls for hybrid step motors: from current-fed to full-order models

Salis, Valerio, Chiappinelli, Nicolas, Costabeber, Alessando, Zanchetta, Pericle, Bifaretti, Stefano, Tomei, Patrizio and Verrelli, Cristiano Maria (2018) Learning position controls for hybrid step motors: from current-fed to full-order models. IEEE Transactions on Industrial Electronics, 65 (8). pp. 6120-6130. ISSN 0278-0046

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The experimental comparison of two different global learning position controls (namely, ‘adaptive learning’ and ‘repetitive learning’ controls) for hybrid step motors performing repetitive tasks has been recently presented in the literature. Related benefits and drawbacks have been successfully analyzed on the same robotic application. However, the design of the two aforementioned learning controls - though relying on a rigorous stability analysis - are based on a simplified current-fed model of the motor. They cannot achieve precise current tracking due to the mere presence of PI control actions in the outer current control loops. The aim of this paper is to enrich and update the results of the above comparison in the light of the latest contributions that generalize the theoretical design to the fullorder voltage-fed motor models of hybrid step motors. Learning actions are now included in the outer current control loops: they generalize the corresponding PI actions to the periodic scenario and allow to solve a control problem whose solution was seeming very difficult to be obtained.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/948796
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Permanent magnet step motors, learning control, position tracking.
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Electrical and Electronic Engineering
Identification Number: https://doi.org/10.1109/TIE.2018.2793183
Depositing User: Burns, Rebecca
Date Deposited: 11 Apr 2018 12:12
Last Modified: 04 May 2020 19:48
URI: https://eprints.nottingham.ac.uk/id/eprint/51081

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