A neural fuzzy inference based adaptive controller using learning process for nonholonomic robots

  • Authors:

  • Ting Wang;Fabien Gautero;Christophe Sabourin;Kurosh Madani

  • Affiliations:

  • Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956), University PARIS-EST Creteil, Senart-FB Institute of Technology, Lieusaint, France;Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956), University PARIS-EST Creteil, Senart-FB Institute of Technology, Lieusaint, France;Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956), University PARIS-EST Creteil, Senart-FB Institute of Technology, Lieusaint, France;Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956), University PARIS-EST Creteil, Senart-FB Institute of Technology, Lieusaint, France

  • Venue:
  • IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
  • Year:
  • 2011

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Abstract

In this paper, we propose a control strategy for a nonholonomic robot which is based on using the learning process of an Adaptive Neural Fuzzy Inference System (ANFIS). The proposed neuro-controller allows the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations' results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.