Data-driven PID Gain Tuning from Regulatory Control Data Based on Generalized Minimum Variance Evaluation

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  • Yokoyama Ryoko
    Graduate School of System Design, Tokyo Metropolitan University
  • Masuda Shiro
    Graduate School of System Design, Tokyo Metropolitan University

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  • 一般化最小分散評価に基づく定値制御データからのデータ駆動型PIDゲイン調整
  • イッパンカ サイショウ ブンサン ヒョウカ ニ モトズク テイチ セイギョ データ カラ ノ データ クドウガタ PID ゲイン チョウセイ

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<p>This paper considers a PID gain tuning method based on generalized minimum variance evaluation. The method derives the PID gains for reducing the variance of the generalized output from regulatory control data disturbed by colored noise. Advantages of the method include the use of normal operating data with no additional experiment for PID tuning. Numerical examples for a CARIMA (Controlled Auto-Regressive Integrated Moving Average) model and a liquid level control model show the proposed method is effective for not only colored noise but also stochastic disturbances combined with periodical rectangular deterministic signals. The simulation results demonstrate that the proposed approach has robustness to model mismatch of disturbance characteristics.</p>

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