Paper Info
Title
Discrete-Time Fractional-Order Control Based On Data-Driven Equivalent Model
Abstract
Considering the case of highly uncertain nonlinear systems, a controller based on an equivalent data-driven model is proposed, such that, the implementation relies only on the input-output information of the controlled plant. With the aim of enhancing the closed-loop performance, a discrete-time fractional-order reaching law is studied. Firstly, the nonlinear system is expressed in terms of discrete-time input-output variations, by considering the pseudo partial derivative (PPD) concept. Then, a multi-input fuzzy rules emulating networks (MiFREN) system is considered to dynamically estimate the PPD. Finally, a discrete-time fractional-order controller is synthesized to enforce the asymptotic convergence of the tracking error. A comparison based on simulations and experimental results are provided to highlight the reliability of the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.asoc.2020.106633
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Data-driven control, Fuzzy rules emulating networks, Fractional-order control, Discrete-time control
Journal
96
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Chidentree Treesatayapun100.34
A.-J. Munoz-Vazquez2429.97