Adaptive Neuro-Fuzzy Controler With Genetic Training For Mobile Robot Control

  • Olumide Obe "Politehnica" University of Bucharest, Romania
  • Ioan Dumitrache "Politehnica" University of Bucharest, Romania

Abstract

In this paper, we investigate the use of adaptive techniques in the optimization of navigation of Khepera mobile robot in an unstructured and dynamic environment. We optimize the performance of our simplified fuzzy controller using neural network that utilizes genetic algorithm learning. The adaptation of the system involves the tuning of the control rules thereby trimming the control actions, and adjusting the fuzzy controller output gain. We realised an improved performance in our adaptive neuro-fuzzy controller with genetic training for various implemented behaviours on the robot.

References

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Published
2012-03-01
How to Cite
OBE, Olumide; DUMITRACHE, Ioan. Adaptive Neuro-Fuzzy Controler With Genetic Training For Mobile Robot Control. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 7, n. 1, p. 135-146, mar. 2012. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1429>. Date accessed: 29 sep. 2020. doi: https://doi.org/10.15837/ijccc.2012.1.1429.

Keywords

Khepera, fuzzy controller, neuro-fuzzy controller, navigation, genetic algorithm