Robust Backstepping Controllers for Linear Motor Drives

This work presents robust backstepping controllers to achieve position tracking control of a linear motor drive system with parameter uncertainties, discontinuous frictional force, and unknown external disturbance. First, a robust control scheme is developed to provide asymptotic stability under tracking control of the linear drive system. The assumption of a constant upper bound on the disturbance during the control design can lead to an overly conservative controller. To avoid this, an adaptation mechanism has been proposed to adapt the upper bound based on the current state measurement and the delayed state and input variables. The developed control scheme was shown to provide a global asymptotic stability of the closed-loop linear motor drive system. The control strategies are evaluated by numerical simulation of the linear motor drive. The simulation results show that the use of the adaptive disturbance upper bound adjustment technique results in significant performance improvement.

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Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/ccta60707.2024.10666649
Citations
1
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.33
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article

Robust Backstepping Controllers for Linear Motor Drives

İbrahim Beklan Küçükdemiral, Isah A. Jimoh, Geraint Bevan, Paul Ager
1 citations
Iterative Learning Control Systems
0.33
article

Robust Backstepping Controllers for Linear Motor Drives

İbrahim Beklan Küçükdemiral, Isah A. Jimoh, Geraint Bevan, Paul Ager
article en
1 citations

Abstract

This work presents robust backstepping controllers to achieve position tracking control of a linear motor drive system with parameter uncertainties, discontinuous frictional force, and unknown external disturbance. First, a robust control scheme is developed to provide asymptotic stability under tracking control of the linear drive system. The assumption of a constant upper bound on the disturbance during the control design can lead to an overly conservative controller. To avoid this, an adaptation mechanism has been proposed to adapt the upper bound based on the current state measurement and the delayed state and input variables. The developed control scheme was shown to provide a global asymptotic stability of the closed-loop linear motor drive system. The control strategies are evaluated by numerical simulation of the linear motor drive. The simulation results show that the use of the adaptive disturbance upper bound adjustment technique results in significant performance improvement.

Glasgow Caledonian University (GB)
Affordable and clean energy
Openalex Percentile: Top 42%
Iterative Learning Control Systems
0.33
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