Precision Control of Wafer Transfer Robots with Unknown Dynamics: an Output Feedback Approach

An output feedback trajectory tracking controller is presented for a belt-driven wafer transfer robot (WTR) with unknown dynamics, achieving precise tracking. The controller employs an extended high-gain observer (EHGO) to effectively estimate the lumped effect of unknown dynamics and disturbances. The robot dynamic model is formulated using the Euler-Lagrange equation, incorporating the belts elongation and contraction, as these factors are the dominant contributors to the tracking error. In contrast, coupling effects, frictional forces, and parametric uncertainties are assumed to be unknown, as they are not the primary determinants of tracking precision and are difficult to measure. This strategic distinction between known and unknown model components contributes to the proposed controller performance. The simulation results illustrate the minimization of the tracking error for the base link.

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Published
2025-11-04
DOI
https://doi.org/10.23919/iccas66577.2025.11301113
Primary Topic
Iterative Learning Control Systems
Type
article
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article

Precision Control of Wafer Transfer Robots with Unknown Dynamics: an Output Feedback Approach

Mohammad Al Saaideh, Yoshiro Fukui, Mohammad Al Janaideh, Eiru Doi
Iterative Learning Control Systems
article

Precision Control of Wafer Transfer Robots with Unknown Dynamics: an Output Feedback Approach

Mohammad Al Saaideh, Yoshiro Fukui, Mohammad Al Janaideh, Eiru Doi
article en

Abstract

An output feedback trajectory tracking controller is presented for a belt-driven wafer transfer robot (WTR) with unknown dynamics, achieving precise tracking. The controller employs an extended high-gain observer (EHGO) to effectively estimate the lumped effect of unknown dynamics and disturbances. The robot dynamic model is formulated using the Euler-Lagrange equation, incorporating the belts elongation and contraction, as these factors are the dominant contributors to the tracking error. In contrast, coupling effects, frictional forces, and parametric uncertainties are assumed to be unknown, as they are not the primary determinants of tracking precision and are difficult to measure. This strategic distinction between known and unknown model components contributes to the proposed controller performance. The simulation results illustrate the minimization of the tracking error for the base link.

Memorial University of Newfoundland (CA), Kyushu Institute of Technology (JP), University of Guelph (CA)
Openalex Percentile: Top 36%
Iterative Learning Control Systems
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