One-shot data-driven design of fractional-order PID controller considering closed-loop stability: fictitious reference signal approach

A one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller is proposed. 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, 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

Auto-tuning of reference models in direct data-driven control

Designing controllers directly from data often requires choosing a reference closed-loop model, whose behavior should be reproduced as tightly as possible by the actual closed-loop system via the selected controller structure (e.g., PID). Within a linear setting, we present a derivative-based approach to jointly select the reference model and controller parameters directly from data. The proposed strategy allows one to maximize closed-loop performance while enforcing user-defined constraints, and it is designed to handle non-minimum phase dynamics. The effectiveness of the proposed approach is shown through three numerical case studies.</p

Investigations into implementation of an iterative feedback tuning algorithm into microcontroller

Includes abstract.Includes bibliographical references (leaves 73-75).Implementation of an Iterative Feedback Tuning (IFT) and Myopic Unfalsified Control (MUC) Algorithm into microcontroller is investigated in this dissertation. Motivation in carrying out this research emanates from successful results obtained in application of IFT algorithm to various physical systems since the method was originated in 1995 by Hjalmarsson [4]. The Motorola DSP56F807C microcontroller is selected for use in the investigations due to its matching characteristics with the requirements of IFT algorithm. Speed of program execution, large memory, in-built ADC & DAC and C compiler type are the key parameters qualifying for its usage. The Analog Devices ARM7024 microcontroller was chosen as an alternative to the DSP56F807C where it is not available. Myopic Unfalsified Control (MUC) is noted to be similar to IFT since it also employs ‘myopic’ gradient based steepest descent approach to parameter optimization. It is easier to implement in that its algorithm is not as complex as the IFT one, meaning that successful implementation of IFT algorithm in a microcontroller would obviously permit the implementation of MUC into microcontroller as well

Direct data‐driven design of switching controllers

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Studies on Data-Driven Controller Tuning for Cascade Control Systems

13301甲第4623号博士（工学）金沢大学博士論文本文Full 以下に掲載：Journal of Robotics and Mechatronics 28(5) pp.739-744 2016. FUJI TECHNOLOGY PRESS LTD. 共著者：Huy Quang Nguyen, Osamu Kaneko, Yoshihiko Kitazak

一般化出力誤差の最小化に基づくデータ指向型PID制御器の設計

広島大学(Hiroshima University)博士(工学)Engineeringdoctora