A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

Egypt.: Cairo, Hindawi. — (Discrete Dynamics in Nature and Society). — 2013 (Apr 12). — 63 p. English.[Yiming Jiang. Key Lab of Autonomous Systems and Networked Control (MOE), School of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
Chenguang Yang. Zienkiewicz Centre for Computational Engineering, Swansea University, Swansea, UK.
Hongbin Ma. State Key Lab of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, China].Received 5 November 2015; Accepted 29 December 2015
Academic Editor: Juan R. TorregrosaCopyright 2016 Yiming Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Abstract.
Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC) and neural network (NN) control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.Introduction.
Fuzzy Logic Control for Discrete-Time Systems.
Adaptive FLC of Discrete-Time Systems.
Robustness Issue in Discrete-Time Fuzzy Control.
Stability Issue in Discrete-Time Fuzzy Control.
Fuzzy Control for Discrete-Time Systems with Time Delays.
NN Control for Discrete-Time Systems.
Adaptive NN Control for Discrete-Time Systems.
NN-Based Dynamic Programming Algorithm for Discrete-Time Systems.
Conclusion.
Conflict of Interests.
Acknowledgment.
References (106 publ).