Abstract

Fractional order calculus has been used to generalize various types of controllers, including internal model controllers (IMC). The focus of this manuscript is towards fractional order IMCs for first order plus dead-time (FOPDT) processes, including delay and lag dominant ones. The design is novel at it is based on a new approximation approach, the non-rational transfer function method. This allows for a more accurate approximation of the process dead-time and ensures an improved closed loop response. The main problem with fractional order controllers is concerned with their implementation as higher order transfer functions. In cases where central processing unit CPU, bandwidth allocation, and energy usage are limited, resources need to be efficiently managed. This can be achieved using an event-based implementation. The novelty of this paper resides in such an event-based algorithm for fractional order IMC (FO-IMC) controllers. Numerical results are provided for lag and delay dominant FOPDT processes. For comparison purposes, an integer order PI controller, tuned according to the same performance specifications as the FO-IMC, is also implemented as an event-based control strategy. The numerical results show that the proposed event-based implementation for the FO-IMC controller is suitable and provides for a smaller computational effort, thus being more suitable in various industrial applications.

Highlights

  • Fractional calculus has been reaching a larger part of the research community due to the numerous advantages it has

  • The event-based FO-internal model controllers (IMC)

  • Controller ensures better closed loop results compared to the event-based integer order PI controller, despite both controller being tuned and implemented in a similar fashion

Read more

Summary

Introduction

Fractional calculus has been reaching a larger part of the research community due to the numerous advantages it has. The increasing interest is mainly due to the ability to capture essential dynamics in physical phenomena. This is seconded by the demonstrated ability of fractional order controllers to meet more design specifications and provide for overall increased robustness and performance. Several researchers have used fractional order tools to model more accurately viscoelastic phenomena [1], aerodynamics [2], structural engineering [3], non-Newtonian characteristics in blood [4,5], type 1 diabetes [6], diffusion phenomena in magnetic resonance imaging [7], post-exposure prophylaxis model in HIV [8], epidemic models for infectious diseases [9], biochemical phenomena [10], etc. A manifold of papers have been published, presenting various modifications of the original fractional order

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call