A Study on a Performance-Driven Hierarchical Control System Design Using a GMV Compensator

This paper proposes a design method for a performance-driven hierarchical control system by integrating a hierarchical control structure and a control system update approach based on performance evaluation. In the proposed method, the compensation input generated by the GMV (Generalized Minimum Variance) compensator is used as the performance index. A mechanism is established to update the nominal model, as well as the control parameters of both the MBC (Model-Based Controller) and the GMV compensator, based on system identification. The effectiveness of the method is verified through numerical simulations under assumed system variations, confirming that the desired control performance is maintained even when system identification results fluctuate.

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Publication Details

Journal
Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
Published
2025-11-05
DOI
https://doi.org/10.5687/sss.2026.75
Primary Topic
Control Systems and Identification
Type
article
Field-Weighted Citation Impact
0.00
Controls
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article

A Study on a Performance-Driven Hierarchical Control System Design Using a GMV Compensator

Hideki Tomiyama, Takashi Ochiiwa, Toru Yamamoto, Takahiro Sugahara et al.
Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
Control Systems and Identification
article

A Study on a Performance-Driven Hierarchical Control System Design Using a GMV Compensator

Hideki Tomiyama, Takashi Ochiiwa, Toru Yamamoto, Takahiro Sugahara, Shin Wakitani
article en

Abstract

This paper proposes a design method for a performance-driven hierarchical control system by integrating a hierarchical control structure and a control system update approach based on performance evaluation. In the proposed method, the compensation input generated by the GMV (Generalized Minimum Variance) compensator is used as the performance index. A mechanism is established to update the nominal model, as well as the control parameters of both the MBC (Model-Based Controller) and the GMV compensator, based on system identification. The effectiveness of the method is verified through numerical simulations under assumed system variations, confirming that the desired control performance is maintained even when system identification results fluctuate.

Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its ApplicationsVol. 2026(0)
Hiroshima University (JP), Japan Steel Works (Japan) (JP)
Openalex Percentile: Top 32%
Control Systems and Identification
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