Self-organizing Fuzzy Controller Based on Fuzzy Neural Network

Cho, Seongwon; Kim, Jaemin; Chung, Sun-Tae

doi:10.1007/978-3-540-72432-2_19

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…SOCs have proved to be successful in "simple" applications since only the rule consequents have to be tuned. In highly nonlinear plants, SOCs are only capable of a coarse tuning of the controller parameters; therefore, more sophisticated fuzzy controllers have to be constructed [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For example, in [11], a hybrid adaptive fuzzy control is suggested where the nonlinear system is controlled by a state feedback controller and an adaptive fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

Innovative Strategy to Improve Precision and to Save Power of a Real-Time Control Process Using an Online Adaptive Fuzzy Logic Controller

Lasri

Rojas

Pomares

et al. 2013

Advances in Fuzzy Systems

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The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set of various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in order to compare the obtained results with each other. Through a training board that allows us to control the temperature, all the used algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive fuzzy logic controller (FLC) will be required to present great improvements in the control task and a real high control performance. Simulation results can show clearly that the new approach presented and tested in this work is very efficient. Thus, our adaptive and self-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee an important reduction of the power consumption during the control process.

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