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Gradient Based Pre-filter Design for Data-driven Parameter Updating for Regulatory Controller Based on Variance Evaluation

This paper presents data-driven parameter updating for discrete-time, linear controllers from closed-loop regulatory control data. The controller has a preset linearly parameterized structure and the controller parameters are updated directly from process input and output measurements so that updated controllers suppress the variance of the process output. The proposed approach firstly estimates a disturbance model from time-series analysis of the closed-loop process output, and then constructs a cost criterion from both the estimated disturbance model and collected data. The updated controller parameters are finally obtained by optimizing the data-driven cost criterion. The proposed approach introduces a pre-filter that makes a gradient vector of the data-driven cost criterion have the same direction as the original cost criterion representing control objective. Thus, the parameter updating from initial parameters toward minimizing parameters of the pre-filtered data-driven cost criterion definitely descends the original cost criterion. The effectiveness of the pre-filter design is shown through a numerical example.