Data-Driven Output Complementary Sensitivity Shaping by One-Shot Experimental Data

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  • 一度のデータ取得によるデータ駆動出力相補感度整形
  • イチド ノ データ シュトク ニ ヨル データ クドウ シュツリョク ソウホ カンド セイケイ

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Abstract

<p>Recently, data-driven controller tuning methods have been actively researched in the control engineering field, and some practical applications also have been reported. For Multi-Input Multi-Output (MIMO) systems, some data-driven controller tuning methods have been proposed. However, conventional methods for MIMO systems have some problems: restriction for reference models, increase of data acquisition, and restriction for use of open-loop data. This paper proposes a new data-driven controller tuning method to overcome the above problems. By shaping an input complementary sensitivity function, the number of data acquisition can be reduced and the closed-loop data can be applicable. To avoid restriction for reference models, we formulate a design problem based on the cost function of the Noniterative Correlation based Tuning (NCbT). However, since the proposed design method shapes an input complementary sensitivity function, an output complementary sensitivity function specifying the tracking performance might not be tuned well. This paper resolves this problem to impose a constraint for a condition number of a designed controller to the optimization problem. The constraint for the condition number of the designed controller is divided into two constraints to make the optimization problem convex. The effectiveness of the proposed method is verified by some numerical simulations.</p>

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