Abstract
This paper proposes a data-driven control parameter tuning method based on generalized minimum variance (GMV) evaluation in regulatory control. The proposed method can perform with no need for plant characteristics nor disturbance ones. For GMV control, this paper introduces a data-driven variance criterion which consists of the input and output data generated by stochastic disturbance. The control parameters are derived by using system parameters as optimization variables. Advantages of the method include applicability to routine operating data, which brings that additive experiments are not required. The efficiency of the method is demonstrated through numerical examples.
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