Domain of Attraction of Optimization for Data-driven <i>H</i><sub>2</sub> Control Performance Criterion

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  • TANAKA Masahiro
    Graduate School of System Design, Tokyo Metropolitan University
  • MASUDA Shiro
    Graduate School of System Design, Tokyo Metropolitan University

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  • データ駆動型<i>H</i><sub>2</sub>制御性能評価の最適化計算における収束領域
  • データ駆動型H₂制御性能評価の最適化計算における収束領域
  • データ クドウガタ H ₂ セイギョ セイノウ ヒョウカ ノ サイテキ カ ケイサン ニ オケル シュウソク リョウイキ

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Abstract

This paper proposes a data-driven controller design method for minimum variance control and analyzes convergence properties of the method. Data-driven controller design methods tune control parameters by minimizing a criterion which is derived from a single set of input and output data without a process model. However, optimization problems in these methods are generally non-convex. The analytical results show that a gradient descent algorithm can converge from the set of initial parameter values to the global minimum if the maximum-phase difference between the initial controllers and the minimum variance controller is smaller than π/2 radians.

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