Improvement Method for Control System in HDD with Dual-Stage Actuator Using Robust Controller Bode Plot

Hard disk drives (HDDs), which store vast amounts of data, are crucial for AI data centers. In this paper, we propose an improved method for the magnetic-head positioning control system in HDDs based on the Robust Controller Bode (RCBode) plot. This method enables the straightforward design of loop-shaping filters that satisfy robust performance using only classical control knowledge. This study extends the design method with the RCBode plot for Dual-Input Single-Output (DISO) systems and applies it to the control design of dual-stage actuator HDDs. The effectiveness of the proposed approach was verified using HDD benchmark problems, yielding validation results equivalent to actual experimental outcomes.

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Publication Details

Journal
IFAC-PapersOnLine
Published
2025-01-01
DOI
https://doi.org/10.1016/j.ifacol.2025.10.171
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Improvement Method for Control System in HDD with Dual-Stage Actuator Using Robust Controller Bode Plot

Takenori Atsumi, Ryutaro Tokuyama
IFAC-PapersOnLine
Iterative Learning Control Systems
article

Improvement Method for Control System in HDD with Dual-Stage Actuator Using Robust Controller Bode Plot

Takenori Atsumi, Ryutaro Tokuyama
article en

Abstract

Hard disk drives (HDDs), which store vast amounts of data, are crucial for AI data centers. In this paper, we propose an improved method for the magnetic-head positioning control system in HDDs based on the Robust Controller Bode (RCBode) plot. This method enables the straightforward design of loop-shaping filters that satisfy robust performance using only classical control knowledge. This study extends the design method with the RCBode plot for Dual-Input Single-Output (DISO) systems and applies it to the control design of dual-stage actuator HDDs. The effectiveness of the proposed approach was verified using HDD benchmark problems, yielding validation results equivalent to actual experimental outcomes.

IFAC-PapersOnLineVol. 59(17)
Chiba Institute of Technology (JP)
Openalex Percentile: Top 59%
Iterative Learning Control Systems
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