Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution

Cheng-Hung Chen, Cheng-Jian Lin, Chin-Teng Lin

Research output: Contribution to journalArticle

83 Scopus citations

Abstract

This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables. The proposed MODE learning algorithm adopts an evolutionary learning method to optimize the controller parameters. For design optimization, a new criterion is introduced. A hardware-in-the loop control technique is developed and applied to the designed ANFN controller using the MODE learning algorithm. The proposed ANFN controller with the MODE learning algorithm (ANFN-MODE) is used in two practical applications-the planetary-train-type inverted pendulum system and the magnetic levitation system. The experiment is developed in a real-time visual simulation environment. Experimental results of this study have demonstrated the robustness and effectiveness of the proposed ANFN-MODE controller.
Original languageEnglish
Pages (from-to)459-473
Number of pages17
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume39
Issue number4
DOIs
StatePublished - Jul 2009

Keywords

  • Differential evolution (DE); magnetic levitation system; neural fuzzy networks; planetary-train-type inverted pendulum

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