Robust Self-tuning Controller under Outliers

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  • KANEDA Yasuaki
    Graduate School of Science and Engineering, Tokyo Institute of Technology Tokyo Metropolitan Industrial Technology Research Institute
  • IRIZUKI Yasuharu
    Tokyo Metropolitan Industrial Technology Research Institute
  • YAMAKITA Masaki
    Graduate School of Science and Engineering, Tokyo Institute of Technology

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  • 外れ値環境下におけるロバストSelf-tuning Controller
  • ハズレ チ カンキョウ カ ニ オケル ロバスト Self-tuning Controller

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Abstract

In this paper, we propose a robust self-tuning controller (STC) under outliers. A parameter update law of a conventional STC consists of a recursive least squares estimation, and the estimation is given by a solution of a minimization problem of estimated errors. In the proposed method, we estimate parameters and outliers explicitly by addition of a l1 regression to the minimization problem like a robust Kalman filter via l1 regression, and the estimated outliers are removed from measurement outputs in the controller. We also analyze control performances of the proposed method under outliers, and it is shown theoretically that performances in the proposed method with outliers are nearly equal to ones in the normal STC without outliers. Numerical simulation, in which a controlled object is a non-minimum phase system, demonstrates effectiveness of the proposed method.

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