Practical one-shot data-driven design of fractional-order PID controller: Fictitious reference signal approach

TL;DR

This paper introduces a practical one-shot data-driven method for tuning fractional-order PID controllers using a fictitious reference signal, ensuring stability and good control performance without needing a plant model.

Contribution

It presents a novel one-shot tuning approach for FOPID controllers based on input/output data and a model-reference framework, considering stability explicitly.

Findings

Effective control performance demonstrated in simulations

Method avoids destabilization even with imperfect model matching

Requires only one-shot input/output data, no plant model needed

Abstract

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.