NDLI: A genetically trained simplified ANFIS Controller to control nonlinear MIMO systems

Content Provider	IEEE Xplore Digital Library
Author	Lutfy, O.F. Noor, S.B.M. Marhaban, M.H.
Copyright Year	2011
Description	Author affiliation: Electrical and Electronic Engineering Dept., Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia (Lutfy, O.F.; Noor, S.B.M.; Marhaban, M.H.)
Abstract	This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller.
Starting Page	349
Ending Page	354
File Size	1178151
Page Count	6
File Format	PDF
ISBN	9781612842295
e-ISBN	9781612842301
DOI	10.1109/INECCE.2011.5953905
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-21
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Learning systems Input variables Nonlinear MIMO systems MIMO ANFIS Neuro-fuzzy systems Testing Genetic algorithms
Content Type	Text
Resource Type	Article

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