A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

Abstract

The scientific community has recently seen a fast-growing number of publications tackling the topic of fractional-order controllers in general, with a focus on the fractional order PID. Several versions of this controller have been proposed, including different tuning methods and implementation possibilities. Quite a few recent papers discuss the practical use of such controllers. However, the industrial acceptance of these controllers is still far from being reached. Autotuning methods for such fractional order PIDs could possibly make them more appealing to industrial applications, as well. In this paper, the current autotuning methods for fractional order PIDs are reviewed. The focus is on the most recent findings. A comparison between several autotuning approaches is considered for various types of processes. Numerical examples are given to highlight the practicality of the methods that could be extended to simple industrial processes.

Advanced modeling and control of wind conversion systems based on hybrid generators using fractional order controllers

“…Because the parameters were designed using a data‐driven nonlinear system, the controller also provided greater tuning flexibility. These findings imply that the controller could be a viable and cost‐effective solution for industrial processes [29].…”

Reduced inverse integer-order controller design for a class of fractional-order model: Case study on two-tank liquid-level process

“…Instead of solving a higher-order equation (i.e. 11th order), we prefer to separate 11th À order characteristic equation into the third-order equation series to be able to obtain a parametric solution of the roots s p i of equation (5). The roots of the third-order equations are parametrically found using the formulas in Holmes 27 and Mitchell.…”

“…These methods, which can be considered as the generalized version of integer-order counterpart tuning methods, might be categorized into three types: analytic, numeric, and rule-based methods. 4–7…”

“…These methods, which can be considered as the generalized version of integer-order counterpart tuning methods, might be categorized into three types: analytic, numeric, and rule-based methods. [4][5][6][7] Non-integer-order calculation adds three more control system structures to the field of control engineering: fractional-order system (FOS) controlled by FOC, integer-order system (IOS) controlled by FOC, and FOS controlled by IOC. [8][9][10] There exist various application areas in industry where fractional system identification and control methods are employed.…”

Dynamic Analysis for the Physically Correct Model of a Fractional-Order Buck-Boost Converter

“…Therefore, fractional order models provide a more accurate description and deeper insight into physical processes [7,8]. In recent years, there has been significant study and development of fractional order systems [9,10]. Unfortunately, the inconsistency of units at the time of modeling is often overlooked when trying to model these electronic elements.…”